International Conference on Language Resources and Evaluation (2022)

Volumes

Proceedings of the Thirteenth Language Resources and Evaluation Conference 805 papers
Proceedings of the BUCC Workshop within LREC 2022 10 papers
Proceedings of the 4th Celtic Language Technology Workshop within LREC2022 19 papers
Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-10) 7 papers
Proceedings of the First Computing Social Responsibility Workshop within the 13th Language Resources and Evaluation Conference 9 papers
Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference 11 papers
Proceedings of the Workshop on Processing Language Variation: Digital Armenian (DigitAm) within the 13th Language Resources and Evaluation Conference 7 papers
Proceedings of the Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia within the 13th Language Resources and Evaluation Conference 19 papers
Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022 25 papers
Proceedings of the 9th Workshop on Games and Natural Language Processing within the 13th Language Resources and Evaluation Conference 8 papers
Proceedings of Globalex Workshop on Linked Lexicography within the 13th Language Resources and Evaluation Conference 14 papers
Proceedings of the 18th Joint ACL - ISO Workshop on Interoperable Semantic Annotation within LREC2022 20 papers
Proceedings of the First Workshop on Language Technology and Resources for a Fair, Inclusive, and Safe Society within the 13th Language Resources and Evaluation Conference 7 papers
Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022 21 papers
Proceedings of the Workshop on Ethical and Legal Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Data In Language Resources within the 13th Language Resources and Evaluation Conference 16 papers
Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages 32 papers
Proceedings of the 18th Workshop on Multiword Expressions @LREC2022 18 papers
Proceedings of the 2nd Workshop on Novel Incentives in Data Collection from People: models, implementations, challenges and results within LREC 2022 10 papers
Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022 16 papers
Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection 29 papers
Proceedings of the Workshop ParlaCLARIN III within the 13th Language Resources and Evaluation Conference 20 papers
Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences 15 papers
Proceedings of the 2nd Workshop on People in Vision, Language, and the Mind 7 papers
Proceedings of the RaPID Workshop - Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments - within the 13th Language Resources and Evaluation Conference 13 papers
Proceedings of the 2nd Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI) within the 13th Language Resources and Evaluation Conference 10 papers
Proceedings of the Second International Workshop on Resources and Techniques for User Information in Abusive Language Analysis 5 papers
Proceedings of the 2nd Workshop on Sentiment Analysis and Linguistic Linked Data 7 papers
Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources 33 papers
Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages 28 papers
Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives 20 papers
Proceedings of the Workshop on Smiling and Laughter across Contexts and the Life-span within the 13th Language Resources and Evaluation Conference 11 papers
Proceedings of the Workshop Towards Digital Language Equality within the 13th Language Resources and Evaluation Conference 7 papers
Proceedings of the Workshop on Terminology in the 21st century: many faces, many places 8 papers
Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference 18 papers

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Domain Adaptation in Neural Machine Translation using a Qualia-Enriched FrameNet
Alexandre Diniz da Costa | Mateus Coutinho Marim | Ely Matos | Tiago Timponi Torrent

In this paper we present Scylla, a methodology for domain adaptation of Neural Machine Translation (NMT) systems that make use of a multilingual FrameNet enriched with qualia relations as an external knowledge base. Domain adaptation techniques used in NMT usually require fine-tuning and in-domain training data, which may pose difficulties for those working with lesser-resourced languages and may also lead to performance decay of the NMT system for out-of-domain sentences. Scylla does not require fine-tuning of the NMT model, avoiding the risk of model over-fitting and consequent decrease in performance for out-of-domain translations. Two versions of Scylla are presented: one using the source sentence as input, and another one using the target sentence. We evaluate Scylla in comparison to a state-of-the-art commercial NMT system in an experiment in which 50 sentences from the Sports domain are translated from Brazilian Portuguese to English. The two versions of Scylla significantly outperform the baseline commercial system in HTER.

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HOPE: A Task-Oriented and Human-Centric Evaluation Framework Using Professional Post-Editing Towards More Effective MT Evaluation
Serge Gladkoff | Lifeng Han

Traditional automatic evaluation metrics for machine translation have been widely criticized by linguists due to their low accuracy, lack of transparency, focus on language mechanics rather than semantics, and low agreement with human quality evaluation. Human evaluations in the form of MQM-like scorecards have always been carried out in real industry setting by both clients and translation service providers (TSPs). However, traditional human translation quality evaluations are costly to perform and go into great linguistic detail, raise issues as to inter-rater reliability (IRR) and are not designed to measure quality of worse than premium quality translations. In this work, we introduce HOPE, a task-oriented and human-centric evaluation framework for machine translation output based on professional post-editing annotations. It contains only a limited number of commonly occurring error types, and uses a scoring model with geometric progression of error penalty points (EPPs) reflecting error severity level to each translation unit. The initial experimental work carried out on English-Russian language pair MT outputs on marketing content type of text from highly technical domain reveals that our evaluation framework is quite effective in reflecting the MT output quality regarding both overall system-level performance and segment-level transparency, and it increases the IRR for error type interpretation. The approach has several key advantages, such as ability to measure and compare less than perfect MT output from different systems, ability to indicate human perception of quality, immediate estimation of the labor effort required to bring MT output to premium quality, low-cost and faster application, as well as higher IRR. Our experimental data is available at https://github.com/lHan87/HOPE.

In recent years, there has been an increasing need for the restoration and translation of historical languages. In this study, we attempt to translate historical records in ancient Korean language based on neural machine translation (NMT). Inspired by priming, a cognitive science theory that two different stimuli influence each other, we propose novel priming ancient-Korean NMT (AKNMT) using bilingual subword embedding initialization with structural property awareness in the ancient documents. Finally, we obtain state-of-the-art results in the AKNMT task. To the best of our knowledge, we confirm the possibility of developing a human-centric model that incorporates the concepts of cognitive science and analyzes the result from the perspective of interference and cognitive dissonance theory for the first time.

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GECO-MT: The Ghent Eye-tracking Corpus of Machine Translation
Toon Colman | Margot Fonteyne | Joke Daems | Nicolas Dirix | Lieve Macken

In the present paper, we describe a large corpus of eye movement data, collected during natural reading of a human translation and a machine translation of a full novel. This data set, called GECO-MT (Ghent Eye tracking Corpus of Machine Translation) expands upon an earlier corpus called GECO (Ghent Eye-tracking Corpus) by Cop et al. (2017). The eye movement data in GECO-MT will be used in future research to investigate the effect of machine translation on the reading process and the effects of various error types on reading. In this article, we describe in detail the materials and data collection procedure of GECO-MT. Extensive information on the language proficiency of our participants is given, as well as a comparison with the participants of the original GECO. We investigate the distribution of a selection of important eye movement variables and explore the possibilities for future analyses of the data. GECO-MT is freely available at https://www.lt3.ugent.be/resources/geco-mt.

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Introducing Frege to Fillmore: A FrameNet Dataset that Captures both Sense and Reference
Levi Remijnse | Piek Vossen | Antske Fokkens | Sam Titarsolej

This article presents the first output of the Dutch FrameNet annotation tool, which facilitates both referential- and frame annotations of language-independent corpora. On the referential level, the tool links in-text mentions to structured data, grounding the text in the real world. On the frame level, those same mentions are annotated with respect to their semantic sense. This way of annotating not only generates a rich linguistic dataset that is grounded in real-world event instances, but also guides the annotators in frame identification, resulting in high inter-annotator-agreement and consistent annotations across documents and at discourse level, exceeding traditional sentence level annotations of frame elements. Moreover, the annotation tool features a dynamic lexical lookup that increases the development of a cross-domain FrameNet lexicon.

We present The Central Word Register for Danish (COR), which is an open source lexicon project for general AI purposes funded and initiated by the Danish Agency for Digitisation as part of an AI initiative embarked by the Danish Government in 2020. We focus here on the lexical semantic part of the project (COR-S) and describe how we – based on the existing fine-grained sense inventory from Den Danske Ordbog (DDO) – compile a more AI suitable sense granularity level of the vocabulary. A three-step methodology is applied: We establish a set of linguistic principles for defining core senses in COR-S and from there, we generate a hand-crafted gold standard of 6,000 lemmas depicting how to come from the fine-grained DDO sense to the COR inventory. Finally, we experiment with a number of language models in order to automatize the sense reduction of the rest of the lexicon. The models comprise a ruled-based model that applies our linguistic principles in terms of features, a word2vec model using cosine similarity to measure the sense proximity, and finally a deep neural BERT model fine-tuned on our annotations. The rule-based approach shows best results, in particular on adjectives, however, when focusing on the average polysemous vocabulary, the BERT model shows promising results too.

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Sense and Sentiment
Francis Bond | Merrick Choo

In this paper we examine existing sentiment lexicons and sense-based sentiment-tagged corpora to find out how sense and concept-based semantic relations effect sentiment scores (for polarity and valence). We show that some relations are good predictors of sentiment of related words: antonyms have similar valence and opposite polarity, synonyms similar valence and polarity, as do many derivational relations. We use this knowledge and existing resources to build a sentiment annotated wordnet of English, and show how it can be used to produce sentiment lexicons for other languages using the Open Multilingual Wordnet.

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Enriching Linguistic Representation in the Cantonese Wordnet and Building the New Cantonese Wordnet Corpus
Ut Seong Sio | Luís Morgado da Costa

This paper reports on the most recent improvements on the Cantonese Wordnet, a wordnet project started in 2019 (Sio and Morgado da Costa, 2019) with the aim of capturing and organizing lexico-semantic information of Hong Kong Cantonese. The improvements we present here extend both the breadth and depth of the Cantonese Wordnet: increasing the general coverage, adding functional categories, enriching verbal representations, as well as creating the Cantonese Wordnet Corpus – a corpus of handcrafted examples where individual senses are shown in context.

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ZAEBUC: An Annotated Arabic-English Bilingual Writer Corpus
Nizar Habash | David Palfreyman

We present ZAEBUC, an annotated Arabic-English bilingual writer corpus comprising short essays by first-year university students at Zayed University in the United Arab Emirates. We describe and discuss the various guidelines and pipeline processes we followed to create the annotations and quality check them. The annotations include spelling and grammar correction, morphological tokenization, Part-of-Speech tagging, lemmatization, and Common European Framework of Reference (CEFR) ratings. All of the annotations are done on Arabic and English texts using consistent guidelines as much as possible, with tracked alignments among the different annotations, and to the original raw texts. For morphological tokenization, POS tagging, and lemmatization, we use existing automatic annotation tools followed by manual correction. We also present various measurements and correlations with preliminary insights drawn from the data and annotations. The publicly available ZAEBUC corpus and its annotations are intended to be the stepping stones for additional annotations.

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Turkish Universal Conceptual Cognitive Annotation
Necva Bölücü | Burcu Can

Universal Conceptual Cognitive Annotation (UCCA) (Abend and Rappoport, 2013a) is a cross-lingual semantic annotation framework that provides an easy annotation without any requirement for linguistic background. UCCA-annotated datasets have been already released in English, French, and German. In this paper, we introduce the first UCCA-annotated Turkish dataset that currently involves 50 sentences obtained from the METU-Sabanci Turkish Treebank (Atalay et al., 2003; Oflazeret al., 2003). We followed a semi-automatic annotation approach, where an external semantic parser is utilised for an initial annotation of the dataset, which is partially accurate and requires refinement. We manually revised the annotations obtained from the semantic parser that are not in line with the UCCA rules that we defined for Turkish. We used the same external semantic parser for evaluation purposes and conducted experiments with both zero-shot and few-shot learning. While the parser cannot predict remote edges in zero-shot setting, using even a small subset of training data in few-shot setting increased the overall F-1 score including the remote edges. This is the initial version of the annotated dataset and we are currently extending the dataset. We will release the current Turkish UCCA annotation guideline along with the annotated dataset.

This article presents the current outcomes of the CURLICAT CEF Telecom project, which aims to collect and deeply annotate a set of large corpora from selected domains. The CURLICAT corpus includes 7 monolingual corpora (Bulgarian, Croatian, Hungarian, Polish, Romanian, Slovak and Slovenian) containing selected samples from respective national corpora. These corpora are automatically tokenized, lemmatized and morphologically analysed and the named entities annotated. The annotations are uniformly provided for each language specific corpus while the common metadata schema is harmonised across the languages. Additionally, the corpora are annotated for IATE terms in all languages. The file format is CoNLL-U Plus format, containing the ten columns specific to the CoNLL-U format and three extra columns specific to our corpora as defined by Varádi et al. (2020). The CURLICAT corpora represent a rich and valuable source not just for training NMT models, but also for further studies and developments in machine learning, cross-lingual terminological data extraction and classification.

Social media has provided a platform for many individuals to easily express themselves naturally and publicly, and researchers have had the opportunity to utilize large quantities of this data to improve author trait analysis techniques and to improve author trait profiling systems. The majority of the work in this area, however, has been narrowly spent on English and other Western European languages, and generally focuses on a single social network at a time, despite the large quantity of data now available across languages and differences that have been found across platforms. This paper introduces RU-ADEPT, a dataset of Russian authors’ personality trait scores–Big Five and Dark Triad, demographic information (e.g. age, gender), with associated corpus of the authors’ cross-contributions to (up to) four different social media platforms–VKontakte (VK), LiveJournal, Blogger, and Moi Mir. We believe this to be the first publicly-available dataset associating demographic and personality trait data with Russian-language social media content, the first paper to describe the collection of Dark Triad scores with texts across multiple Russian-language social media platforms, and to a limited extent, the first publicly-available dataset of personality traits to author content across several different social media sites.

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CoQAR: Question Rewriting on CoQA
Quentin Brabant | Gwénolé Lecorvé | Lina M. Rojas Barahona

Questions asked by humans during a conversation often contain contextual dependencies, i.e., explicit or implicit references to previous dialogue turns. These dependencies take the form of coreferences (e.g., via pronoun use) or ellipses, and can make the understanding difficult for automated systems. One way to facilitate the understanding and subsequent treatments of a question is to rewrite it into an out-of-context form, i.e., a form that can be understood without the conversational context. We propose CoQAR, a corpus containing 4.5K conversations from the Conversational Question-Answering dataset CoQA, for a total of 53K follow-up question-answer pairs. Each original question was manually annotated with at least 2 at most 3 out-of-context rewritings. CoQA originally contains 8k conversations, which sum up to 127k question-answer pairs. CoQAR can be used in the supervised learning of three tasks: question paraphrasing, question rewriting and conversational question answering. In order to assess the quality of CoQAR’s rewritings, we conduct several experiments consisting in training and evaluating models for these three tasks. Our results support the idea that question rewriting can be used as a preprocessing step for (conversational and non-conversational) question answering models, thereby increasing their performances.

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User Interest Modelling in Argumentative Dialogue Systems
Annalena Aicher | Nadine Gerstenlauer | Wolfgang Minker | Stefan Ultes

Most systems helping to provide structured information and support opinion building, discuss with users without considering their individual interest. The scarce existing research on user interest in dialogue systems depends on explicit user feedback. Such systems require user responses that are not content-related and thus, tend to disturb the dialogue flow. In this paper, we present a novel model for implicitly estimating user interest during argumentative dialogues based on semantically clustered data. Therefore, an online user study was conducted to acquire training data which was used to train a binary neural network classifier in order to predict whether or not users are still interested in the content of the ongoing dialogue. We achieved a classification accuracy of 74.9% and furthermore investigated with different Artificial Neural Networks (ANN) which new argument would fit the user interest best.

Incorporating handwritten domain scripts into neural-based task-oriented dialogue systems may be an effective way to reduce the need for large sets of annotated dialogues. In this paper, we investigate how the use of domain scripts written by conversational designers affects the performance of neural-based dialogue systems. To support this investigation, we propose the Conversational-Logic-Injection-in-Neural-Network system (CLINN) where domain scripts are coded in semi-logical rules. By using CLINN, we evaluated semi-logical rules produced by a team of differently-skilled conversational designers. We experimented with the Restaurant domain of the MultiWOZ dataset. Results show that external knowledge is extremely important for reducing the need for annotated examples for conversational systems. In fact, rules from conversational designers used in CLINN significantly outperform a state-of-the-art neural-based dialogue system when trained with smaller sets of annotated dialogues.

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An Empirical Study on the Overlapping Problem of Open-Domain Dialogue Datasets
Yuqiao Wen | Guoqing Luo | Lili Mou

Open-domain dialogue systems aim to converse with humans through text, and dialogue research has heavily relied on benchmark datasets. In this work, we observe the overlapping problem in DailyDialog and OpenSubtitles, two popular open-domain dialogue benchmark datasets. Our systematic analysis then shows that such overlapping can be exploited to obtain fake state-of-the-art performance. Finally, we address this issue by cleaning these datasets and setting up a proper data processing procedure for future research.

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Language Technologies for the Creation of Multilingual Terminologies. Lessons Learned from the SSHOC Project
Federica Gamba | Francesca Frontini | Daan Broeder | Monica Monachini

This paper is framed in the context of the SSHOC project and aims at exploring how Language Technologies can help in promoting and facilitating multilingualism in the Social Sciences and Humanities (SSH). Although most SSH researchers produce culturally and societally relevant work in their local languages, metadata and vocabularies used in the SSH domain to describe and index research data are currently mostly in English. We thus investigate Natural Language Processing and Machine Translation approaches in view of providing resources and tools to foster multilingual access and discovery to SSH content across different languages. As case studies, we create and deliver as freely, openly available data a set of multilingual metadata concepts and an automatically extracted multilingual Data Stewardship terminology. The two case studies allow as well to evaluate performances of state-of-the-art tools and to derive a set of recommendations as to how best apply them. Although not adapted to the specific domain, the employed tools prove to be a valid asset to translation tasks. Nonetheless, validation of results by domain experts proficient in the language is an unavoidable phase of the whole workflow.

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How to be FAIR when you CARE: The DGS Corpus as a Case Study of Open Science Resources for Minority Languages
Marc Schulder | Thomas Hanke

The publication of resources for minority languages requires a balance between making data open and accessible and respecting the rights and needs of its language community. The FAIR principles were introduced as a guide to good open data practices and they have since been complemented by the CARE principles for indigenous data governance. This article describes how the DGS Corpus implemented these principles and how the two sets of principles affected each other. The DGS Corpus is a large collection of recordings of members of the deaf community in Germany communicating in their primary language, German Sign Language (DGS); it was created to be both as a resource for linguistic research and as a record of the life experiences of deaf people in Germany. The corpus was designed with CARE in mind to respect and empower the language community and FAIR data publishing was used to enhance its usefulness as a scientific resource.

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Italian NLP for Everyone: Resources and Models from EVALITA to the European Language Grid
Valerio Basile | Cristina Bosco | Michael Fell | Viviana Patti | Rossella Varvara

The European Language Grid enables researchers and practitioners to easily distribute and use NLP resources and models, such as corpora and classifiers. We describe in this paper how, during the course of our EVALITA4ELG project, we have integrated datasets and systems for the Italian language. We show how easy it is to use the integrated systems, and demonstrate in case studies how seamless the application of the platform is, providing Italian NLP for everyone.

In this paper, we provide an overview of current technologies for cross-lingual link discovery, and we discuss challenges, experiences and prospects of their application to under-resourced languages. We rst introduce the goals of cross-lingual linking and associated technologies, and in particular, the role that the Linked Data paradigm (Bizer et al., 2011) applied to language data can play in this context. We de ne under-resourced languages with a speci c focus on languages actively used on the internet, i.e., languages with a digitally versatile speaker community, but limited support in terms of language technology. We argue that languages for which considerable amounts of textual data and (at least) a bilingual word list are available, techniques for cross-lingual linking can be readily applied, and that these enable the implementation of downstream applications for under-resourced languages via the localisation and adaptation of existing technologies and resources.

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Angry or Sad ? Emotion Annotation for Extremist Content Characterisation
Valentina Dragos | Delphine Battistelli | Aline Etienne | Yolène Constable

This paper examines the role of emotion annotations to characterize extremist content released on social platforms. The analysis of extremist content is important to identify user emotions towards some extremist ideas and to highlight the root cause of where emotions and extremist attitudes merge together. To address these issues our methodology combines knowledge from sociological and linguistic annotations to explore French extremist content collected online. For emotion linguistic analysis, the solution presented in this paper relies on a complex linguistic annotation scheme. The scheme was used to annotate extremist text corpora in French. Data sets were collected online by following semi-automatic procedures for content selection and validation. The paper describes the integrated annotation scheme, the annotation protocol that was set-up for French corpora annotation and the results, e.g. agreement measures and remarks on annotation disagreements. The aim of this work is twofold: first, to provide a characterization of extremist contents; second, to validate the annotation scheme and to test its capacity to capture and describe various aspects of emotions.

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Identification of Multiword Expressions in Tweets for Hate Speech Detection
Nicolas Zampieri | Carlos Ramisch | Irina Illina | Dominique Fohr

Multiword expression (MWE) identification in tweets is a complex task due to the complex linguistic nature of MWEs combined with the non-standard language use in social networks. MWE features were shown to be helpful for hate speech detection (HSD). In this article, we present joint experiments on these two related tasks on English Twitter data: first we focus on the MWE identification task, and then we observe the influence of MWE-based features on the HSD task. For MWE identification, we compare the performance of two systems: lexicon-based and deep neural networks-based (DNN). We experimentally evaluate seven configurations of a state-of-the-art DNN system based on recurrent networks using pre-trained contextual embeddings from BERT. The DNN-based system outperforms the lexicon-based one thanks to its superior generalisation power, yielding much better recall. For the HSD task, we propose a new DNN architecture for incorporating MWE features. We confirm that MWE features are helpful for the HSD task. Moreover, the proposed DNN architecture beats previous MWE-based HSD systems by 0.4 to 1.1 F-measure points on average on four Twitter HSD corpora.

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Causal Investigation of Public Opinion during the COVID-19 Pandemic via Social Media Text
Michael Jantscher | Roman Kern

Understanding the needs and fears of citizens, especially during a pandemic such as COVID-19, is essential for any government or legislative entity. An effective COVID-19 strategy further requires that the public understand and accept the restriction plans imposed by these entities. In this paper, we explore a causal mediation scenario in which we want to emphasize the use of NLP methods in combination with methods from economics and social sciences. Based on sentiment analysis of Tweets towards the current COVID-19 situation in the UK and Sweden, we conduct several causal inference experiments and attempt to decouple the effect of government restrictions on mobility behavior from the effect that occurs due to public perception of the COVID-19 strategy in a country. To avoid biased results we control for valid country specific epidemiological and time-varying confounders. Comprehensive experiments show that not all changes in mobility are caused by countries implemented policies but also by the support of individuals in the fight against this pandemic. We find that social media texts are an important source to capture citizens’ concerns and trust in policy makers and are suitable to evaluate the success of government policies.

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Misspelling Semantics in Thai
Pakawat Nakwijit | Matthew Purver

User-generated content is full of misspellings. Rather than being just random noise, we hypothesise that many misspellings contain hidden semantics that can be leveraged for language understanding tasks. This paper presents a fine-grained annotated corpus of misspelling in Thai, together with an analysis of misspelling intention and its possible semantics to get a better understanding of the misspelling patterns observed in the corpus. In addition, we introduce two approaches to incorporate the semantics of misspelling: Misspelling Average Embedding (MAE) and Misspelling Semantic Tokens (MST). Experiments on a sentiment analysis task confirm our overall hypothesis: additional semantics from misspelling can boost the micro F1 score up to 0.4-2%, while blindly normalising misspelling is harmful and suboptimal.

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Automatic Detection of Stigmatizing Uses of Psychiatric Terms on Twitter
Véronique Moriceau | Farah Benamara | Abdelmoumene Boumadane

Psychiatry and people suffering from mental disorders have often been given a pejorative label that induces social rejection. Many studies have addressed discourse content about psychiatry on social media, suggesting that they convey stigmatizingrepresentations of mental health disorders. In this paper, we focus for the first time on the use of psychiatric terms in tweetsin French. We first describe the annotated dataset that we use. Then we propose several deep learning models to detectautomatically (1) the different types of use of psychiatric terms (medical use, misuse or irrelevant use), and (2) the polarityof the tweet. We show that polarity detection can be improved when done in a multitask framework in combination with typeof use detection. This confirms the observations made manually on several datasets, namely that the polarity of a tweet iscorrelated to the type of term use (misuses are mostly negative whereas medical uses are neutral). The results are interesting forboth tasks and it allows to consider the possibility for performant automatic approaches in order to conduct real-time surveyson social media, larger and less expensive than existing manual ones

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CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets
Isabelle Mohr | Amelie Wührl | Roman Klinger

During the first two years of the COVID-19 pandemic, large volumes of biomedical information concerning this new disease have been published on social media. Some of this information can pose a real danger, particularly when false information is shared, for instance recommendations how to treat diseases without professional medical advice. Therefore, automatic fact-checking resources and systems developed specifically for medical domain are crucial. While existing fact-checking resources cover COVID-19 related information in news or quantify the amount of misinformation in tweets, there is no dataset providing fact-checked COVID-19 related Twitter posts with detailed annotations for biomedical entities, relations and relevant evidence. We contribute CoVERT, a fact-checked corpus of tweets with a focus on the domain of biomedicine and COVID-19 related (mis)information. The corpus consists of 300 tweets, each annotated with named entities and relations. We employ a novel crowdsourcing methodology to annotate all tweets with fact-checking labels and supporting evidence, which crowdworkers search for online. This methodology results in substantial inter-annotator agreement. Furthermore, we use the retrieved evidence extracts as part of a fact-checking pipeline, finding that the real-world evidence is more useful than the knowledge directly available in pretrained language models.

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XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond
Francesco Barbieri | Luis Espinosa Anke | Jose Camacho-Collados

Language models are ubiquitous in current NLP, and their multilingual capacity has recently attracted considerable attention. However, current analyses have almost exclusively focused on (multilingual variants of) standard benchmarks, and have relied on clean pre-training and task-specific corpora as multilingual signals. In this paper, we introduce XLM-T, a model to train and evaluate multilingual language models in Twitter. In this paper we provide: (1) a new strong multilingual baseline consisting of an XLM-R (Conneau et al. 2020) model pre-trained on millions of tweets in over thirty languages, alongside starter code to subsequently fine-tune on a target task; and (2) a set of unified sentiment analysis Twitter datasets in eight different languages and a XLM-T model trained on this dataset.

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‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models
Areej Alhassan | Jinkai Zhang | Viktor Schlegel

Natural language processing (NLP) has been shown to perform well in various tasks, such as answering questions, ascertaining natural language inference and anomaly detection. However, there are few NLP-related studies that touch upon the moral context conveyed in text. This paper studies whether state-of-the-art, pre-trained language models are capable of passing moral judgments on posts retrieved from a popular Reddit user board. Reddit is a social discussion website and forum where posts are promoted by users through a voting system. In this work, we construct a dataset that can be used for moral judgement tasks by collecting data from the AITA? (Am I the A*******?) subreddit. To model our task, we harnessed the power of pre-trained language models, including BERT, RoBERTa, RoBERTa-large, ALBERT and Longformer. We then fine-tuned these models and evaluated their ability to predict the correct verdict as judged by users for each post in the datasets. RoBERTa showed relative improvements across the three datasets, exhibiting a rate of 87% accuracy and a Matthews correlation coefficient (MCC) of 0.76, while the use of the Longformer model slightly improved the performance when used with longer sequences, achieving 87% accuracy and 0.77 MCC.

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Generating Questions from Wikidata Triples
Kelvin Han | Thiago Castro Ferreira | Claire Gardent

Question generation from knowledge bases (or knowledge base question generation, KBQG) is the task of generating questions from structured database information, typically in the form of triples representing facts. To handle rare entities and generalize to unseen properties, previous work on KBQG resorted to extensive, often ad-hoc pre- and post-processing of the input triple. We revisit KBQG – using pre training, a new (triple, question) dataset and taking question type into account – and show that our approach outperforms previous work both in a standard and in a zero-shot setting. We also show that the extended KBQG dataset (also helpful for knowledge base question answering) we provide allows not only for better coverage in terms of knowledge base (KB) properties but also for increased output variability in that it permits the generation of multiple questions from the same KB triple.

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Evaluating Transformer Language Models on Arithmetic Operations Using Number Decomposition
Matteo Muffo | Aldo Cocco | Enrico Bertino

In recent years, Large Language Models such as GPT-3 showed remarkable capabilities in performing NLP tasks in the zero and few shot settings. On the other hand, the experiments highlighted the difficulty of GPT-3 in carrying out tasks that require a certain degree of reasoning, such as arithmetic operations. In this paper we evaluate the ability of Transformer Language Models to perform arithmetic operations following a pipeline that, before performing computations, decomposes numbers in units, tens, and so on. We denote the models fine-tuned with this pipeline with the name Calculon and we test them in the task of performing additions, subtractions and multiplications on the same test sets of GPT-3. Results show an increase of accuracy of 63% in the five-digit addition task. Moreover, we demonstrate the importance of the decomposition pipeline introduced, since fine-tuning the same Language Model without decomposing numbers results in 0% accuracy in the five-digit addition task.

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Evaluating the Effects of Embedding with Speaker Identity Information in Dialogue Summarization
Yuji Naraki | Tetsuya Sakai | Yoshihiko Hayashi

Automatic dialogue summarization is a task used to succinctly summarize a dialogue transcript while correctly linking the speakers and their speech, which distinguishes this task from a conventional document summarization. To address this issue and reduce the “who said what”-related errors in a summary, we propose embedding the speaker identity information in the input embedding into the dialogue transcript encoder. Unlike the speaker embedding proposed by Gu et al. (2020), our proposal takes into account the informativeness of position embedding. By experimentally comparing several embedding methods, we confirmed that the scores of ROUGE and a human evaluation of the generated summaries were substantially increased by embedding speaker information at the less informative part of the fixed position embedding with sinusoidal functions.

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Perceived Text Quality and Readability in Extractive and Abstractive Summaries
Julius Monsen | Evelina Rennes

We present results from a study investigating how users perceive text quality and readability in extractive and abstractive summaries. We trained two summarisation models on Swedish news data and used these to produce summaries of articles. With the produced summaries, we conducted an online survey in which the extractive summaries were compared to the abstractive summaries in terms of fluency, adequacy and simplicity. We found statistically significant differences in perceived fluency and adequacy between abstractive and extractive summaries but no statistically significant difference in simplicity. Extractive summaries were preferred in most cases, possibly due to the types of errors the summaries tend to have.

Neural text summarization has shown great potential in recent years. However, current state-of-the-art summarization models are limited by their maximum input length, posing a challenge to summarizing longer texts comprehensively. As part of a layered summarization architecture, we introduce PureText, a simple yet effective pre-processing layer that removes low- quality sentences in articles to improve existing summarization models. When evaluated on popular datasets like WikiHow and Reddit TIFU, we show up to 3.84 and 8.57 point ROUGE-1 absolute improvement on the full test set and the long article subset, respectively, for state-of-the-art summarization models such as BertSum and BART. Our approach provides downstream models with higher-quality sentences for summarization, improving overall model performance, especially on long text articles.

We introduce document retrieval and comment generation tasks for automating horizon scanning. This is an important task in the field of futurology that collects sufficient information for predicting drastic societal changes in the mid- or long-term future. The steps used are: 1) retrieving news articles that imply drastic changes, and 2) writing subjective comments on each article for others’ ease of understanding. As a first step in automating these tasks, we create a dataset that contains 2,266 manually collected news articles with comments written by experts. We analyze the collected documents and comments regarding characteristic words, the distance to general articles, and contents in the comments. Furthermore, we compare several methods for automating horizon scanning. Our experiments show that 1) manually collected articles are different from general articles regarding the words used and semantic distances, 2) the contents in the comment can be classified into several categories, and 3) a supervised model trained on our dataset achieves a better performance. The contributions are: 1) we propose document retrieval and comment generation tasks for horizon scanning, 2) create and analyze a new dataset, and 3) report the performance of several models and show that comment generation tasks are challenging.

Pre-trained language models have become crucial to achieving competitive results across many Natural Language Processing (NLP) problems. For monolingual pre-trained models in low-resource languages, the quantity has been significantly increased. However, most of them relate to the general domain, and there are limited strong baseline language models for domain-specific. We introduce ViHealthBERT, the first domain-specific pre-trained language model for Vietnamese healthcare. The performance of our model shows strong results while outperforming the general domain language models in all health-related datasets. Moreover, we also present Vietnamese datasets for the healthcare domain for two tasks are Acronym Disambiguation (AD) and Frequently Asked Questions (FAQ) Summarization. We release our ViHealthBERT to facilitate future research and downstream application for Vietnamese NLP in domain-specific. Our dataset and code are available in https://github.com/demdecuong/vihealthbert.

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Privacy-Preserving Graph Convolutional Networks for Text Classification
Timour Igamberdiev | Ivan Habernal

Graph convolutional networks (GCNs) are a powerful architecture for representation learning on documents that naturally occur as graphs, e.g., citation or social networks. However, sensitive personal information, such as documents with people’s profiles or relationships as edges, are prone to privacy leaks, as the trained model might reveal the original input. Although differential privacy (DP) offers a well-founded privacy-preserving framework, GCNs pose theoretical and practical challenges due to their training specifics. We address these challenges by adapting differentially-private gradient-based training to GCNs and conduct experiments using two optimizers on five NLP datasets in two languages. We propose a simple yet efficient method based on random graph splits that not only improves the baseline privacy bounds by a factor of 2.7 while retaining competitive F1 scores, but also provides strong privacy guarantees of epsilon = 1.0. We show that, under certain modeling choices, privacy-preserving GCNs perform up to 90% of their non-private variants, while formally guaranteeing strong privacy measures.

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ArMATH: a Dataset for Solving Arabic Math Word Problems
Reem Alghamdi | Zhenwen Liang | Xiangliang Zhang

This paper studies solving Arabic Math Word Problems by deep learning. A Math Word Problem (MWP) is a text description of a mathematical problem that can be solved by deriving a math equation to reach the answer. Effective models have been developed for solving MWPs in English and Chinese. However, Arabic MWPs are rarely studied. This paper contributes the first large-scale dataset for Arabic MWPs, which contains 6,000 samples of primary-school math problems, written in Modern Standard Arabic (MSA). Arabic MWP solvers are then built with deep learning models and evaluated on this dataset. In addition, a transfer learning model is built to let the high-resource Chinese MWP solver promote the performance of the low-resource Arabic MWP solver. This work is the first to use deep learning methods to solve Arabic MWP and the first to use transfer learning to solve MWP across different languages. The transfer learning enhanced solver has an accuracy of 74.15%, which is 3% higher than the solver without using transfer learning. We make the dataset and solvers available in public for encouraging more research of Arabic MWPs: https://github.com/reem-codes/ArMATH

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KIMERA: Injecting Domain Knowledge into Vacant Transformer Heads
Benjamin Winter | Alexei Figueroa Rosero | Alexander Löser | Felix Alexander Gers | Amy Siu

Training transformer language models requires vast amounts of text and computational resources. This drastically limits the usage of these models in niche domains for which they are not optimized, or where domain-specific training data is scarce. We focus here on the clinical domain because of its limited access to training data in common tasks, while structured ontological data is often readily available. Recent observations in model compression of transformer models show optimization potential in improving the representation capacity of attention heads. We propose KIMERA (Knowledge Injection via Mask Enforced Retraining of Attention) for detecting, retraining and instilling attention heads with complementary structured domain knowledge. Our novel multi-task training scheme effectively identifies and targets individual attention heads that are least useful for a given downstream task and optimizes their representation with information from structured data. KIMERA generalizes well, thereby building the basis for an efficient fine-tuning. KIMERA achieves significant performance boosts on seven datasets in the medical domain in Information Retrieval and Clinical Outcome Prediction settings. We apply KIMERA to BERT-base to evaluate the extent of the domain transfer and also improve on the already strong results of BioBERT in the clinical domain.

Running large-scale pre-trained language models in computationally constrained environments remains a challenging problem yet to be addressed, while transfer learning from these models has become prevalent in Natural Language Processing tasks. Several solutions, including knowledge distillation, network quantization, or network pruning have been previously proposed; however, these approaches focus mostly on the English language, thus widening the gap when considering low-resource languages. In this work, we introduce three light and fast versions of distilled BERT models for the Romanian language: Distil-BERT-base-ro, Distil-RoBERT-base, and DistilMulti-BERT-base-ro. The first two models resulted from the individual distillation of knowledge from two base versions of Romanian BERTs available in literature, while the last one was obtained by distilling their ensemble. To our knowledge, this is the first attempt to create publicly available Romanian distilled BERT models, which were thoroughly evaluated on five tasks: part-of-speech tagging, named entity recognition, sentiment analysis, semantic textual similarity, and dialect identification. Our experimental results argue that the three distilled models offer performance comparable to their teachers, while being twice as fast on a GPU and ~35% smaller. In addition, we further test the similarity between the predictions of our students versus their teachers by measuring their label and probability loyalty, together with regression loyalty - a new metric introduced in this work.

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Personalized Filled-pause Generation with Group-wise Prediction Models
Yuta Matsunaga | Takaaki Saeki | Shinnosuke Takamichi | Hiroshi Saruwatari

In this paper, we propose a method to generate personalized filled pauses (FPs) with group-wise prediction models. Compared with fluent text generation, disfluent text generation has not been widely explored. To generate more human-like texts, we addressed disfluent text generation. The usage of disfluency, such as FPs, rephrases, and word fragments, differs from speaker to speaker, and thus, the generation of personalized FPs is required. However, it is difficult to predict them because of the sparsity of position and the frequency difference between more and less frequently used FPs. Moreover, it is sometimes difficult to adapt FP prediction models to each speaker because of the large variation of the tendency within each speaker. To address these issues, we propose a method to build group-dependent prediction models by grouping speakers on the basis of their tendency to use FPs. This method does not require a large amount of data and time to train each speaker model. We further introduce a loss function and a word embedding model suitable for FP prediction. Our experimental results demonstrate that group-dependent models can predict FPs with higher scores than a non-personalized one and the introduced loss function and word embedding model improve the prediction performance.

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Transformer versus LSTM Language Models trained on Uncertain ASR Hypotheses in Limited Data Scenarios
Imran Sheikh | Emmanuel Vincent | Irina Illina

In several ASR use cases, training and adaptation of domain-specific LMs can only rely on a small amount of manually verified text transcriptions and sometimes a limited amount of in-domain speech. Training of LSTM LMs in such limited data scenarios can benefit from alternate uncertain ASR hypotheses, as observed in our recent work. In this paper, we propose a method to train Transformer LMs on ASR confusion networks. We evaluate whether these self-attention based LMs are better at exploiting alternate ASR hypotheses as compared to LSTM LMs. Evaluation results show that Transformer LMs achieve 3-6% relative reduction in perplexity on the AMI scenario meetings but perform similar to LSTM LMs on the smaller Verbmobil conversational corpus. Evaluation on ASR N-best rescoring shows that LSTM and Transformer LMs trained on ASR confusion networks do not bring significant WER reductions. However, a qualitative analysis reveals that they are better at predicting less frequent words.

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Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised?
Boshko Koloski | Senja Pollak | Blaž Škrlj | Matej Martinc

Keyword extraction is the task of retrieving words that are essential to the content of a given document. Researchers proposed various approaches to tackle this problem. At the top-most level, approaches are divided into ones that require training - supervised and ones that do not - unsupervised. In this study, we are interested in settings, where for a language under investigation, no training data is available. More specifically, we explore whether pretrained multilingual language models can be employed for zero-shot cross-lingual keyword extraction on low-resource languages with limited or no available labeled training data and whether they outperform state-of-the-art unsupervised keyword extractors. The comparison is conducted on six news article datasets covering two high-resource languages, English and Russian, and four low-resource languages, Croatian, Estonian, Latvian, and Slovenian. We find that the pretrained models fine-tuned on a multilingual corpus covering languages that do not appear in the test set (i.e. in a zero-shot setting), consistently outscore unsupervised models in all six languages.

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Evaluating Pretraining Strategies for Clinical BERT Models
Anastasios Lamproudis | Aron Henriksson | Hercules Dalianis

Research suggests that using generic language models in specialized domains may be sub-optimal due to significant domain differences. As a result, various strategies for developing domain-specific language models have been proposed, including techniques for adapting an existing generic language model to the target domain, e.g. through various forms of vocabulary modifications and continued domain-adaptive pretraining with in-domain data. Here, an empirical investigation is carried out in which various strategies for adapting a generic language model to the clinical domain are compared to pretraining a pure clinical language model. Three clinical language models for Swedish, pretrained for up to ten epochs, are fine-tuned and evaluated on several downstream tasks in the clinical domain. A comparison of the language models’ downstream performance over the training epochs is conducted. The results show that the domain-specific language models outperform a general-domain language model; however, there is little difference in performance of the various clinical language models. However, compared to pretraining a pure clinical language model with only in-domain data, leveraging and adapting an existing general-domain language model requires fewer epochs of pretraining with in-domain data.

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KazNERD: Kazakh Named Entity Recognition Dataset
Rustem Yeshpanov | Yerbolat Khassanov | Huseyin Atakan Varol

We present the development of a dataset for Kazakh named entity recognition. The dataset was built as there is a clear need for publicly available annotated corpora in Kazakh, as well as annotation guidelines containing straightforward—but rigorous—rules and examples. The dataset annotation, based on the IOB2 scheme, was carried out on television news text by two native Kazakh speakers under the supervision of the first author. The resulting dataset contains 112,702 sentences and 136,333 annotations for 25 entity classes. State-of-the-art machine learning models to automatise Kazakh named entity recognition were also built, with the best-performing model achieving an exact match F1-score of 97.22% on the test set. The annotated dataset, guidelines, and codes used to train the models are freely available for download under the CC BY 4.0 licence from https://github.com/IS2AI/KazNERD.

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Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization
Michail Mersinias | Panagiotis Valvis

In recent years, natural language inference has been an emerging research area. In this paper, we present a novel data augmentation technique and combine it with a unique learning procedure for that task. Our so-called automatic contextual data augmentation (acda) method manages to be fully automatic, non-trivially contextual, and computationally efficient at the same time. When compared to established data augmentation methods, it is substantially more computationally efficient and requires no manual annotation by a human expert as they usually do. In order to increase its efficiency, we combine acda with two learning optimization techniques: contrastive learning and a hybrid loss function. The former maximizes the benefit of the supervisory signal generated by acda, while the latter incentivises the model to learn the nuances of the decision boundary. Our combined approach is shown experimentally to provide an effective way for mitigating spurious data correlations within a dataset, called dataset artifacts, and as a result improves performance. Specifically, our experiments verify that acda-boosted pre-trained language models that employ our learning optimization techniques, consistently outperform the respective fine-tuned baseline pre-trained language models across both benchmark datasets and adversarial examples.

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Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning
Mike Zhang | Kristian Nørgaard Jensen | Barbara Plank

Skill Classification (SC) is the task of classifying job competences from job postings. This work is the first in SC applied to Danish job vacancy data. We release the first Danish job posting dataset: *Kompetencer* (_en_: competences), annotated for nested spans of competences. To improve upon coarse-grained annotations, we make use of The European Skills, Competences, Qualifications and Occupations (ESCO; le Vrang et al., (2014)) taxonomy API to obtain fine-grained labels via distant supervision. We study two setups: The zero-shot and few-shot classification setting. We fine-tune English-based models and RemBERT (Chung et al., 2020) and compare them to in-language Danish models. Our results show RemBERT significantly outperforms all other models in both the zero-shot and the few-shot setting.

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Semantic Role Labelling for Dutch Law Texts
Roos Bakker | Romy A.N. van Drie | Maaike de Boer | Robert van Doesburg | Tom van Engers

Legal texts are often difficult to interpret, and people who interpret them need to make choices about the interpretation. To improve transparency, the interpretation of a legal text can be made explicit by formalising it. However, creating formalised representations of legal texts manually is quite labour-intensive. In this paper, we describe a method to extract structured representations in the Flint language (van Doesburg and van Engers, 2019) from natural language. Automated extraction of knowledge representation not only makes the interpretation and modelling efforts more efficient, it also contributes to reducing inter-coder dependencies. The Flint language offers a formal model that enables the interpretation of legal text by describing the norms in these texts as acts, facts and duties. To extract the components of a Flint representation, we use a rule-based method and a transformer-based method. In the transformer-based method we fine-tune the last layer with annotated legal texts. The results show that the transformed-based method (80% accuracy) outperforms the rule-based method (42% accuracy) on the Dutch Aliens Act. This indicates that the transformer-based method is a promising approach of automatically extracting Flint frames.

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English Language Spelling Correction as an Information Retrieval Task Using Wikipedia Search Statistics
Kyle Goslin | Markus Hofmann

Spelling correction utilities have become commonplace during the writing process, however, many spelling correction utilities suffer due to the size and quality of dictionaries available to aid correction. Many terms, acronyms, and morphological variations of terms are often missing, leaving potential spelling errors unidentified and potentially uncorrected. This research describes the implementation of WikiSpell, a dynamic spelling correction tool that relies on the Wikipedia dataset search API functionality as the sole source of knowledge to aid misspelled term identification and automatic replacement. Instead of a traditional matching process to select candidate replacement terms, the replacement process is treated as a natural language information retrieval process harnessing wildcard string matching and search result statistics. The aims of this research include: 1) the implementation of a spelling correction algorithm that utilizes the wildcard operators in the Wikipedia dataset search API, 2) a review of the current spell correction tools and approaches being utilized, and 3) testing and validation of the developed algorithm against the benchmark spelling correction tool, Hunspell. The key contribution of this research is a robust, dynamic information retrieval-based spelling correction algorithm that does not require prior training. Results of this research show that the proposed spelling correction algorithm, WikiSpell, achieved comparable results to an industry-standard spelling correction algorithm, Hunspell.

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CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction
Meisin Lee | Lay-Ki Soon | Eu Gene Siew | Ly Fie Sugianto

In this paper, we present CrudeOilNews, a corpus of English Crude Oil news for event extraction. It is the first of its kind for Commodity News and serves to contribute towards resource building for economic and financial text mining. This paper describes the data collection process, the annotation methodology, and the event typology used in producing the corpus. Firstly, a seed set of 175 news articles were manually annotated, of which a subset of 25 news was used as the adjudicated reference test set for inter-annotator and system evaluation. The inter-annotator agreement was generally substantial, and annotator performance was adequate, indicating that the annotation scheme produces consistent event annotations of high quality. Subsequently, the dataset is expanded through (1) data augmentation and (2) Human-in-the-loop active learning. The resulting corpus has 425 news articles with approximately 11k events annotated. As part of the active learning process, the corpus was used to train basic event extraction models for machine labeling; the resulting models also serve as a validation or as a pilot study demonstrating the use of the corpus in machine learning purposes. The annotated corpus is made available for academic research purpose at https://github.com/meisin/CrudeOilNews-Corpus

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Claim Extraction and Law Matching for COVID-19-related Legislation
Niklas Dehio | Malte Ostendorff | Georg Rehm

To cope with the COVID-19 pandemic, many jurisdictions have introduced new or altered existing legislation. Even though these new rules are often communicated to the public in news articles, it remains challenging for laypersons to learn about what is currently allowed or forbidden since news articles typically do not reference underlying laws. We investigate an automated approach to extract legal claims from news articles and to match the claims with their corresponding applicable laws. We examine the feasibility of the two tasks concerning claims about COVID-19-related laws from Berlin, Germany. For both tasks, we create and make publicly available the data sets and report the results of initial experiments. We obtain promising results with Transformer-based models that achieve 46.7 F1 for claim extraction and 91.4 F1 for law matching, albeit with some conceptual limitations. Furthermore, we discuss challenges of current machine learning approaches for legal language processing and their ability for complex legal reasoning tasks.

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Constructing A Dataset of Support and Attack Relations in Legal Arguments in Court Judgements using Linguistic Rules
Basit Ali | Sachin Pawar | Girish Palshikar | Rituraj Singh

Argumentation mining is a growing area of research and has several interesting practical applications of mining legal arguments. Support and Attack relations are the backbone of any legal argument. However, there is no publicly available dataset of these relations in the context of legal arguments expressed in court judgements. In this paper, we focus on automatically constructing such a dataset of Support and Attack relations between sentences in a court judgment with reasonable accuracy. We propose three sets of rules based on linguistic knowledge and distant supervision to identify such relations from Indian Supreme Court judgments. The first rule set is based on multiple discourse connectors, the second rule set is based on common semantic structures between argumentative sentences in a close neighbourhood, and the third rule set uses the information about the source of the argument. We also explore a BERT-based sentence pair classification model which is trained on this dataset. We release the dataset of 20506 sentence pairs - 10746 Support (precision 77.3%) and 9760 Attack (precision 65.8%). We believe that this dataset and the ideas explored in designing the linguistic rules and will boost the argumentation mining research for legal arguments.

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KIND: an Italian Multi-Domain Dataset for Named Entity Recognition
Teresa Paccosi | Alessio Palmero Aprosio

In this paper we present KIND, an Italian dataset for Named-entity recognition. It contains more than one million tokens with annotation covering three classes: person, location, and organization. The dataset (around 600K tokens) mostly contains manual gold annotations in three different domains (news, literature, and political discourses) and a semi-automatically annotated part. The multi-domain feature is the main strength of the present work, offering a resource which covers different styles and language uses, as well as the largest Italian NER dataset with manual gold annotations. It represents an important resource for the training of NER systems in Italian. Texts and annotations are freely downloadable from the Github repository.

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Russian Jeopardy! Data Set for Question-Answering Systems
Elena Mikhalkova | Alexander A. Khlyupin

Question answering (QA) is one of the most common NLP tasks that relates to named entity recognition, fact extraction, semantic search and some other fields. In industry, it is much valued in chat-bots and corporate information systems. It is also a challenging task that attracted the attention of a very general audience at the quiz show Jeopardy! In this article we describe a Jeopardy!-like Russian QA data set collected from the official Russian quiz database Ch-g-k. The data set includes 379,284 quiz-like questions with 29,375 from the Russian analogue of Jeopardy! (Own Game). We observe its linguistic features and the related QA-task. We conclude about perspectives of a QA challenge based on the collected data set.

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Know Better – A Clickbait Resolving Challenge
Benjamin Hättasch | Carsten Binnig

In this paper, we present a new corpus of clickbait articles annotated by university students along with a corresponding shared task: clickbait articles use a headline or teaser that hides information from the reader to make them curious to open the article. We therefore propose to construct approaches that can automatically extract the relevant information from such an article, which we call clickbait resolving. We show why solving this task might be relevant for end users, and why clickbait can probably not be defeated with clickbait detection alone. Additionally, we argue that this task, although similar to question answering and some automatic summarization approaches, needs to be tackled with specialized models. We analyze the performance of some basic approaches on this task and show that models fine-tuned on our data can outperform general question answering models, while providing a systematic approach to evaluate the results. We hope that the data set and the task will help in giving users tools to counter clickbait in the future.

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Valet: Rule-Based Information Extraction for Rapid Deployment
Dayne Freitag | John Cadigan | Robert Sasseen | Paul Kalmar

We present VALET, a framework for rule-based information extraction written in Python. VALET departs from legacy approaches predicated on cascading finite-state transducers, instead offering direct support for mixing heterogeneous information–lexical, orthographic, syntactic, corpus-analytic–in a succinct syntax that supports context-free idioms. We show how a handful of rules suffices to implement sophisticated matching, and describe a user interface that facilitates exploration for development and maintenance of rule sets. Arguing that rule-based information extraction is an important methodology early in the development cycle, we describe an experiment in which a VALET model is used to annotate examples for a machine learning extraction model. While learning to emulate the extraction rules, the resulting model generalizes them, recognizing valid extraction targets the rules failed to detect.

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Negation Detection in Dutch Spoken Human-Computer Conversations
Tom Sweers | Iris Hendrickx | Helmer Strik

Proper recognition and interpretation of negation signals in text or communication is crucial for any form of full natural language understanding. It is also essential for computational approaches to natural language processing. In this study we focus on negation detection in Dutch spoken human-computer conversations. Since there exists no Dutch (dialogue) corpus annotated for negation we have annotated a Dutch corpus sample to evaluate our method for automatic negation detection. We use transfer learning and trained NegBERT (an existing BERT implementation used for negation detection) on English data with multilingual BERT to detect negation in Dutch dialogues. Our results show that adding in-domain training material improves the results. We show that we can detect both negation cues and scope in Dutch dialogues with high precision and recall. We provide a detailed error analysis and discuss the effects of cross-lingual and cross-domain transfer learning on automatic negation detection.

The Linguistic Data Consortium was founded in 1992 to solve the problem that limitations in access to shareable data was impeding progress in Human Language Technology research and development. At the time, DARPA had adopted the common task research management paradigm to impose additional rigor on their programs by also providing shared objectives, data and evaluation methods. Early successes underscored the promise of this paradigm but also the need for a standing infrastructure to host and distribute the shared data. During LDC’s initial five year grant, it became clear that the demand for linguistic data could not easily be met by the existing providers and that a dedicated data center could add capacity first for data collection and shortly thereafter for annotation. The expanding purview required expansions of LDC’s technical infrastructure including systems support and software development. An open question for the center would be its role in other kinds of research beyond data development. Over its 30 years history, LDC has performed multiple roles ranging from neutral, independent data provider to multisite programs, to creator of exploratory data in tight collaboration with system developers, to research group focused on data intensive investigations.

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Language Resources to Support Language Diversity – the ELRA Achievements
Valérie Mapelli | Victoria Arranz | Khalid Choukri | Hélène Mazo

This article highlights ELRA’s latest achievements in the field of Language Resources (LRs) identification, sharing and production. It also reports on ELRA’s involvement in several national and international projects, as well as in the organization of events for the support of LRs and related Language Technologies, including for under-resourced languages. Over the past few years, ELRA, together with its operational agency ELDA, has continued to increase its catalogue offer of LRs, establishing worldwide partnerships for the production of various types of LRs (SMS, tweets, crawled data, MT aligned data, speech LRs, sentiment-based data, etc.). Through their consistent involvement in EU-funded projects, ELRA and ELDA have contributed to improve the access to multilingual information in the context of the pandemic, develop tools for the de-identification of texts in the legal and medical domains, support the EU eTranslation Machine Translation system, and set up a European platform providing access to both resources and services. In December 2019, ELRA co-organized the LT4All conference, whose main topics were Language Technologies for enabling linguistic diversity and multilingualism worldwide. Moreover, although LREC was cancelled in 2020, ELRA published the LREC 2020 proceedings for the Main conference and Workshops papers, and carried on its dissemination activities while targeting the new LREC edition for 2022.

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Ethical Issues in Language Resources and Language Technology – Tentative Categorisation
Pawel Kamocki | Andreas Witt

Ethical issues in Language Resources and Language Technology are often invoked, but rarely discussed. This is at least partly because little work has been done to systematize ethical issues and principles applicable in the fields of Language Resources and Language Technology. This paper provides an overview of ethical issues that arise at different stages of Language Resources and Language Technology development, from the conception phase through the construction phase to the use phase. Based on this overview, the authors propose a tentative taxonomy of ethical issues in Language Resources and Language Technology, built around five principles: Privacy, Property, Equality, Transparency and Freedom. The authors hope that this tentative taxonomy will facilitate ethical assessment of projects in the field of Language Resources and Language Technology, and structure the discussion on ethical issues in this domain, which may eventually lead to the adoption of a universally accepted Code of Ethics of the Language Resources and Language Technology community.

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Do we Name the Languages we Study? The #BenderRule in LREC and ACL articles
Fanny Ducel | Karën Fort | Gaël Lejeune | Yves Lepage

This article studies the application of the #BenderRule in Natural Language Processing (NLP) articles according to two dimensions. Firstly, in a contrastive manner, by considering two major international conferences, LREC and ACL, and secondly, in a diachronic manner, by inspecting nearly 14,000 articles over a period of time ranging from 2000 to 2020 for LREC and from 1979 to 2020 for ACL. For this purpose, we created a corpus from LREC and ACL articles from the above-mentioned periods, from which we manually annotated nearly 1,000. We then developed two classifiers to automatically annotate the rest of the corpus. Our results show that LREC articles tend to respect the #BenderRule (80 to 90% of them respect it), whereas 30 to 40% of ACL articles do not. Interestingly, over the considered periods, the results appear to be stable for the two conferences, even though a rebound in ACL 2020 could be a sign of the influence of the blog post about the #BenderRule.

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Aspect-Based Emotion Analysis and Multimodal Coreference: A Case Study of Customer Comments on Adidas Instagram Posts
Luna De Bruyne | Akbar Karimi | Orphee De Clercq | Andrea Prati | Veronique Hoste

While aspect-based sentiment analysis of user-generated content has received a lot of attention in the past years, emotion detection at the aspect level has been relatively unexplored. Moreover, given the rise of more visual content on social media platforms, we want to meet the ever-growing share of multimodal content. In this paper, we present a multimodal dataset for Aspect-Based Emotion Analysis (ABEA). Additionally, we take the first steps in investigating the utility of multimodal coreference resolution in an ABEA framework. The presented dataset consists of 4,900 comments on 175 images and is annotated with aspect and emotion categories and the emotional dimensions of valence and arousal. Our preliminary experiments suggest that ABEA does not benefit from multimodal coreference resolution, and that aspect and emotion classification only requires textual information. However, when more specific information about the aspects is desired, image recognition could be essential.

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Multi-source Multi-domain Sentiment Analysis with BERT-based Models
Gabriel Roccabruna | Steve Azzolin | Giuseppe Riccardi

Sentiment analysis is one of the most widely studied tasks in natural language processing. While BERT-based models have achieved state-of-the-art results in this task, little attention has been given to its performance variability across class labels, multi-source and multi-domain corpora. In this paper, we present an improved state-of-the-art and comparatively evaluate BERT-based models for sentiment analysis on Italian corpora. The proposed model is evaluated over eight sentiment analysis corpora from different domains (social media, finance, e-commerce, health, travel) and sources (Twitter, YouTube, Facebook, Amazon, Tripadvisor, Opera and Personal Healthcare Agent) on the prediction of positive, negative and neutral classes. Our findings suggest that BERT-based models are confident in predicting positive and negative examples but not as much with neutral examples. We release the sentiment analysis model as well as a newly financial domain sentiment corpus.

Sentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data. We introduce the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria—Hausa, Igbo, Nigerian-Pidgin, and Yorùbá—consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets. We propose text collection, filtering, processing and labeling methods that enable us to create datasets for these low-resource languages. We evaluate a range of pre-trained models and transfer strategies on the dataset. We find that language-specific models and language-adaptive fine-tuning generally perform best. We release the datasets, trained models, sentiment lexicons, and code to incentivize research on sentiment analysis in under-represented languages.

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A (Psycho-)Linguistically Motivated Scheme for Annotating and Exploring Emotions in a Genre-Diverse Corpus
Aline Etienne | Delphine Battistelli | Gwénolé Lecorvé

This paper presents a scheme for emotion annotation and its manual application on a genre-diverse corpus of texts written in French. The methodology introduced here emphasizes the necessity of clarifying the main concepts implied by the analysis of emotions as they are expressed in texts, before conducting a manual annotation campaign. After explaining whatentails a deeply linguistic perspective on emotion expression modeling, we present a few NLP works that share some common points with this perspective and meticulously compare our approach with them. We then highlight some interesting quantitative results observed on our annotated corpus. The most notable interactions are on the one hand between emotion expression modes and genres of texts, and on the other hand between emotion expression modes and emotional categories. These observation corroborate and clarify some of the results already mentioned in other NLP works on emotion annotation.

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Integrating a Phrase Structure Corpus Grammar and a Lexical-Semantic Network: the HOLINET Knowledge Graph
Jean-Philippe Prost

In this paper we address the question of how to integrate grammar and lexical-semantic knowledge within a single and homogeneous knowledge graph. We introduce a graph modelling of grammar knowledge which enables its merging with a lexical-semantic network. Such an integrated representation is expected, for instance, to provide new material for language-related graph embeddings in order to model interactions between Syntax and Semantics. Our base model relies on a phrase structure grammar. The phrase structure is accounted for by both a Proof-Theoretical representation, through a Context-Free Grammar, and a Model-Theoretical one, through a constraint-based grammar. The constraint types colour the grammar layer with syntactic relationships such as Immediate Dominance, Linear Precedence, and more. We detail a creation process which infers the grammar layer from a corpus annotated in constituency and integrates it with a lexical-semantic network through a shared POS tagset. We implement the process, and experiment with the French Treebank and the JeuxDeMots lexical-semantic network. The outcome is the HOLINET knowledge graph.

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On the Impact of Temporal Representations on Metaphor Detection
Giorgio Ottolina | Matteo Luigi Palmonari | Manuel Vimercati | Mehwish Alam

State-of-the-art approaches for metaphor detection compare their literal - or core - meaning and their contextual meaning using metaphor classifiers based on neural networks. However, metaphorical expressions evolve over time due to various reasons, such as cultural and societal impact. Metaphorical expressions are known to co-evolve with language and literal word meanings, and even drive, to some extent, this evolution. This poses the question of whether different, possibly time-specific, representations of literal meanings may impact the metaphor detection task. To the best of our knowledge, this is the first study that examines the metaphor detection task with a detailed exploratory analysis where different temporal and static word embeddings are used to account for different representations of literal meanings. Our experimental analysis is based on three popular benchmarks used for metaphor detection and word embeddings extracted from different corpora and temporally aligned using different state-of-the-art approaches. The results suggest that the usage of different static word embedding methods does impact the metaphor detection task and some temporal word embeddings slightly outperform static methods. However, the results also suggest that temporal word embeddings may provide representations of the core meaning of the metaphor even too close to their contextual meaning, thus confusing the classifier. Overall, the interaction between temporal language evolution and metaphor detection appears tiny in the benchmark datasets used in our experiments. This suggests that future work for the computational analysis of this important linguistic phenomenon should first start by creating a new dataset where this interaction is better represented.

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Analysis and Prediction of NLP Models via Task Embeddings
Damien Sileo | Marie-Francine Moens

Task embeddings are low-dimensional representations that are trained to capture task properties. In this paper, we propose MetaEval, a collection of 101 NLP tasks. We fit a single transformer to all MetaEval tasks jointly while conditioning it on learned embeddings. The resulting task embeddings enable a novel analysis of the space of tasks. We then show that task aspects can be mapped to task embeddings for new tasks without using any annotated examples. Predicted embeddings can modulate the encoder for zero-shot inference and outperform a zero-shot baseline on GLUE tasks. The provided multitask setup can function as a benchmark for future transfer learning research.

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Cross-lingual and Cross-domain Transfer Learning for Automatic Term Extraction from Low Resource Data
Amir Hazem | Merieme Bouhandi | Florian Boudin | Beatrice Daille

Automatic Term Extraction (ATE) is a key component for domain knowledge understanding and an important basis for further natural language processing applications. Even with persistent improvements, ATE still exhibits weak results exacerbated by small training data inherent to specialized domain corpora. Recently, transformers-based deep neural models, such as BERT, have proven to be efficient in many downstream NLP tasks. However, no systematic evaluation of ATE has been conducted so far. In this paper, we run an extensive study on fine-tuning pre-trained BERT models for ATE. We propose strategies that empirically show BERT’s effectiveness using cross-lingual and cross-domain transfer learning to extract single and multi-word terms. Experiments have been conducted on four specialized domains in three languages. The obtained results suggest that BERT can capture cross-domain and cross-lingual terminologically-marked contexts shared by terms, opening a new design-pattern for ATE.

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Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate
Lena Jurkschat | Gregor Wiedemann | Maximilian Heinrich | Mattes Ruckdeschel | Sunna Torge

We approach aspect-based argument mining as a supervised machine learning task to classify arguments into semantically coherent groups referring to the same defined aspect categories. As an exemplary use case, we introduce the Argument Aspect Corpus - Nuclear Energy that separates arguments about the topic of nuclear energy into nine major aspects. Since the collection of training data for further aspects and topics is costly, we investigate the potential for current transformer-based few-shot learning approaches to accurately classify argument aspects. The best approach is applied to a British newspaper corpus covering the debate on nuclear energy over the past 21 years. Our evaluation shows that a stable prediction of shares of argument aspects in this debate is feasible with 50 to 100 training samples per aspect. Moreover, we see signals for a clear shift in the public discourse in favor of nuclear energy in recent years. This revelation of changing patterns of pro and contra arguments related to certain aspects over time demonstrates the potential of supervised argument aspect detection for tracking issue-specific media discourses.

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MuLVE, A Multi-Language Vocabulary Evaluation Data Set
Anik Jacobsen | Salar Mohtaj | Sebastian Möller

Vocabulary learning is vital to foreign language learning. Correct and adequate feedback is essential to successful and satisfying vocabulary training. However, many vocabulary and language evaluation systems perform on simple rules and do not account for real-life user learning data. This work introduces Multi-Language Vocabulary Evaluation Data Set (MuLVE), a data set consisting of vocabulary cards and real-life user answers, labeled indicating whether the user answer is correct or incorrect. The data source is user learning data from the Phase6 vocabulary trainer. The data set contains vocabulary questions in German and English, Spanish, and French as target language and is available in four different variations regarding pre-processing and deduplication. We experiment to fine-tune pre-trained BERT language models on the downstream task of vocabulary evaluation with the proposed MuLVE data set. The results provide outstanding results of > 95.5 accuracy and F2-score. The data set is available on the European Language Grid.

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PLOD: An Abbreviation Detection Dataset for Scientific Documents
Leonardo Zilio | Hadeel Saadany | Prashant Sharma | Diptesh Kanojia | Constantin Orăsan

The detection and extraction of abbreviations from unstructured texts can help to improve the performance of Natural Language Processing tasks, such as machine translation and information retrieval. However, in terms of publicly available datasets, there is not enough data for training deep-neural-networks-based models to the point of generalising well over data. This paper presents PLOD, a large-scale dataset for abbreviation detection and extraction that contains 160k+ segments automatically annotated with abbreviations and their long forms. We performed manual validation over a set of instances and a complete automatic validation for this dataset. We then used it to generate several baseline models for detecting abbreviations and long forms. The best models achieved an F1-score of 0.92 for abbreviations and 0.89 for detecting their corresponding long forms. We release this dataset along with our code and all the models publicly at https://github.com/surrey-nlp/PLOD-AbbreviationDetection

We present a fairly large, Potential Idiomatic Expression (PIE) dataset for Natural Language Processing (NLP) in English. The challenges with NLP systems with regards to tasks such as Machine Translation (MT), word sense disambiguation (WSD) and information retrieval make it imperative to have a labelled idioms dataset with classes such as it is in this work. To the best of the authors’ knowledge, this is the first idioms corpus with classes of idioms beyond the literal and the general idioms classification. In particular, the following classes are labelled in the dataset: metaphor, simile, euphemism, parallelism, personification, oxymoron, paradox, hyperbole, irony and literal. We obtain an overall inter-annotator agreement (IAA) score, between two independent annotators, of 88.89%. Many past efforts have been limited in the corpus size and classes of samples but this dataset contains over 20,100 samples with almost 1,200 cases of idioms (with their meanings) from 10 classes (or senses). The corpus may also be extended by researchers to meet specific needs. The corpus has part of speech (PoS) tagging from the NLTK library. Classification experiments performed on the corpus to obtain a baseline and comparison among three common models, including the BERT model, give good results. We also make publicly available the corpus and the relevant codes for working with it for NLP tasks.

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LeSpell - A Multi-Lingual Benchmark Corpus of Spelling Errors to Develop Spellchecking Methods for Learner Language
Marie Bexte | Ronja Laarmann-Quante | Andrea Horbach | Torsten Zesch

Spellchecking text written by language learners is especially challenging because errors made by learners differ both quantitatively and qualitatively from errors made by already proficient learners. We introduce LeSpell, a multi-lingual (English, German, Italian, and Czech) evaluation data set of spelling mistakes in context that we compiled from seven underlying learner corpora. Our experiments show that existing spellcheckers do not work well with learner data. Thus, we introduce a highly customizable spellchecking component for the DKPro architecture, which improves performance in many settings.

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Subjective Text Complexity Assessment for German
Laura Seiffe | Fares Kallel | Sebastian Möller | Babak Naderi | Roland Roller

For different reasons, text can be difficult to read and understand for many people, especially if the text’s language is too complex. In order to provide suitable text for the target audience, it is necessary to measure its complexity. In this paper we describe subjective experiments to assess the readability of German text. We compile a new corpus of sentences provided by a German IT service provider. The sentences are annotated with the subjective complexity ratings by two groups of participants, namely experts and non-experts for that text domain. We then extract an extensive set of linguistically motivated features that are supposedly interacting with complexity perception. We show that a linear regression model with a subset of these features can be a very good predictor of text complexity.

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Querying Interaction Structure: Approaches to Overlap in Spoken Language Corpora
Elena Frick | Thomas Schmidt | Henrike Helmer

In this paper, we address two problems in indexing and querying spoken language corpora with overlapping speaker contributions. First, we look into how token distance and token precedence can be measured when multiple primary data streams are available and when transcriptions happen to be tokenized, but are not synchronized with the sound at the level of individual tokens. We propose and experiment with a speaker-based search mode that enables any speaker’s transcription tier to be the basic tokenization layer whereby the contributions of other speakers are mapped to this given tier. Secondly, we address two distinct methods of how speaker overlaps can be captured in the TEI-based ISO Standard for Spoken Language Transcriptions (ISO 24624:2016) and how they can be queried by MTAS – an open source Lucene-based search engine for querying text with multilevel annotations. We illustrate the problems, introduce possible solutions and discuss their benefits and drawbacks.

This paper introduces DiaBiz, a large, annotated, multimodal corpus of Polish telephone conversations conducted in varied business settings, comprising 4036 call centre interactions from nine different domains, i.e. banking, energy services, telecommunications, insurance, medical care, debt collection, tourism, retail and car rental. The corpus was developed to boost the development of third-party speech recognition engines, dialog systems and conversational intelligence tools for Polish. Its current size amounts to nearly 410 hours of recordings and over 3 million words of transcribed speech. We present the structure of the corpus, data collection and transcription procedures, challenges of punctuating and truecasing speech transcripts, dialog structure annotation and discuss some of the ecological validity considerations involved in the development of such resources.

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LaVA – Latvian Language Learner corpus
Roberts Darģis | Ilze Auziņa | Inga Kaija | Kristīne Levāne-Petrova | Kristīne Pokratniece

This paper presents the Latvian Language Learner Corpus (LaVA) developed at the Institute of Mathematics and Computer Science, University of Latvia. LaVA corpus contains 1015 essays (190k tokens and 790k characters excluding whitespaces) from foreigners studying at Latvian higher education institutions and who are learning Latvian as a foreign language in the first or second semester, reaching the A1 (possibly A2) Latvian language proficiency level. The corpus has morphological and error annotations. Error analysis and the statistics of the LaVA corpus are also provided in the paper. The corpus is publicly available at: http://www.korpuss.lv/id/LaVA.

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The EuroPat Corpus: A Parallel Corpus of European Patent Data
Kenneth Heafield | Elaine Farrow | Jelmer van der Linde | Gema Ramírez-Sánchez | Dion Wiggins

We present the EuroPat corpus of patent-specific parallel data for 6 official European languages paired with English: German, Spanish, French, Croatian, Norwegian, and Polish. The filtered parallel corpora range in size from 51 million sentences (Spanish-English) to 154k sentences (Croatian-English), with the unfiltered (raw) corpora being up to 2 times larger. Access to clean, high quality, parallel data in technical domains such as science, engineering, and medicine is needed for training neural machine translation systems for tasks like online dispute resolution and eProcurement. Our evaluation found that the addition of EuroPat data to a generic baseline improved the performance of machine translation systems on in-domain test data in German, Spanish, French, and Polish; and in translating patent data from Croatian to English. The corpus has been released under Creative Commons Zero, and is expected to be widely useful for training high-quality machine translation systems, and particularly for those targeting technical documents such as patents and contracts.

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“Beste Grüße, Maria Meyer” — Pseudonymization of Privacy-Sensitive Information in Emails
Elisabeth Eder | Michael Wiegand | Ulrike Krieg-Holz | Udo Hahn

The exploding amount of user-generated content has spurred NLP research to deal with documents from various digital communication formats (tweets, chats, emails, etc.). Using these texts as language resources implies complying with legal data privacy regulations. To protect the personal data of individuals and preclude their identification, we employ pseudonymization. More precisely, we identify those text spans that carry information revealing an individual’s identity (e.g., names of persons, locations, phone numbers, or dates) and subsequently substitute them with synthetically generated surrogates. Based on CodE Alltag, a German-language email corpus, we address two tasks. The first task is to evaluate various architectures for the automatic recognition of privacy-sensitive entities in raw data. The second task examines the applicability of pseudonymized data as training data for such systems since models learned on original data cannot be published for reasons of privacy protection. As outputs of both tasks, we, first, generate a new pseudonymized version of CodE Alltag compliant with the legal requirements of the General Data Protection Regulation (GDPR). Second, we make accessible a tagger for recognizing privacy-sensitive information in German emails and similar text genres, which is trained on already pseudonymized data.

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Criteria for the Annotation of Implicit Stereotypes
Wolfgang Schmeisser-Nieto | Montserrat Nofre | Mariona Taulé

The growth of social media has brought with it a massive channel for spreading and reinforcing stereotypes. This issue becomes critical when the affected targets are minority groups such as women, the LGBT+ community and immigrants. Although from the perspective of computational linguistics, the detection of this kind of stereotypes is steadily improving, most stereotypes are expressed implicitly and identifying them automatically remains a challenge. One of the problems we found for tackling this issue is the lack of an operationalised definition of implicit stereotypes that would allow us to annotate consistently new corpora by characterising the different forms in which stereotypes appear. In this paper, we present thirteen criteria for annotating implicitness which were elaborated to facilitate the subjective task of identifying the presence of stereotypes. We also present NewsCom-Implicitness, a corpus of 1,911 sentences, of which 426 comprise explicit and implicit racial stereotypes. An experiment was carried out to evaluate the applicability of these criteria. The results indicate that different criteria obtain different inter-annotator agreement values and that there is a greater agreement when more criteria can be identified in one sentence.

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Common Phone: A Multilingual Dataset for Robust Acoustic Modelling
Philipp Klumpp | Tomas Arias | Paula Andrea Pérez-Toro | Elmar Noeth | Juan Orozco-Arroyave

Current state of the art acoustic models can easily comprise more than 100 million parameters. This growing complexity demands larger training datasets to maintain a decent generalization of the final decision function. An ideal dataset is not necessarily large in size, but large with respect to the amount of unique speakers, utilized hardware and varying recording conditions. This enables a machine learning model to explore as much of the domain-specific input space as possible during parameter estimation. This work introduces Common Phone, a gender-balanced, multilingual corpus recorded from more than 76.000 contributors via Mozilla’s Common Voice project. It comprises around 116 hours of speech enriched with automatically generated phonetic segmentation. A Wav2Vec 2.0 acoustic model was trained with the Common Phone to perform phonetic symbol recognition and validate the quality of the generated phonetic annotation. The architecture achieved a PER of 18.1 % on the entire test set, computed with all 101 unique phonetic symbols, showing slight differences between the individual languages. We conclude that Common Phone provides sufficient variability and reliable phonetic annotation to help bridging the gap between research and application of acoustic models.

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Curras + Baladi: Towards a Levantine Corpus
Karim Al-Haff | Mustafa Jarrar | Tymaa Hammouda | Fadi Zaraket

This paper presents two-fold contributions: a full revision of the Palestinian morphologically annotated corpus (Curras), and a newly annotated Lebanese corpus (Baladi). Both corpora can be used as a more general Levantine corpus. Baladi consists of around 9.6K morphologically annotated tokens. Each token was manually annotated with several morphological features and using LDC’s SAMA lemmas and tags. The inter-annotator evaluation on most features illustrates 78.5% Kappa and 90.1% F1-Score. Curras was revised by refining all annotations for accuracy, normalization and unification of POS tags, and linking with SAMA lemmas. This revision was also important to ensure that both corpora are compatible and can help to bridge the nuanced linguistic gaps that exist between the two highly mutually intelligible dialects. Both corpora are publicly available through a web portal.

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Annotation Study of Japanese Judgments on Tort for Legal Judgment Prediction with Rationales
Hiroaki Yamada | Takenobu Tokunaga | Ryutaro Ohara | Keisuke Takeshita | Mihoko Sumida

This paper describes a comprehensive annotation study on Japanese judgment documents in civil cases. We aim to build an annotated corpus designed for Legal Judgment Prediction (LJP), especially for torts. Our annotation scheme contains annotations of whether tort is accepted by judges as well as its corresponding rationales for explainability purpose. Our annotation scheme extracts decisions and rationales at character-level. Moreover, the scheme can capture the explicit causal relation between judge’s decisions and their corresponding rationales, allowing multiple decisions in a document. To obtain high-quality annotation, we developed an annotation scheme with legal experts, and confirmed its reliability by agreement studies with Krippendorff’s alpha metric. The result of the annotation study suggests the proposed annotation scheme can produce a dataset of Japanese LJP at reasonable reliability.

Even though hate speech (HS) online has been an important object of research in the last decade, most HS-related corpora over-simplify the phenomenon of hate by attempting to label user comments as “hate” or “neutral”. This ignores the complex and subjective nature of HS, which limits the real-life applicability of classifiers trained on these corpora. In this study, we present the M-Phasis corpus, a corpus of ~9k German and French user comments collected from migration-related news articles. It goes beyond the “hate”-“neutral” dichotomy and is instead annotated with 23 features, which in combination become descriptors of various types of speech, ranging from critical comments to implicit and explicit expressions of hate. The annotations are performed by 4 native speakers per language and achieve high (0.77 <= k <= 1) inter-annotator agreements. Besides describing the corpus creation and presenting insights from a content, error and domain analysis, we explore its data characteristics by training several classification baselines.

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ParCorFull2.0: a Parallel Corpus Annotated with Full Coreference
Ekaterina Lapshinova-Koltunski | Pedro Augusto Ferreira | Elina Lartaud | Christian Hardmeier

In this paper, we describe ParCorFull2.0, a parallel corpus annotated with full coreference chains for multiple languages, which is an extension of the existing corpus ParCorFull (Lapshinova-Koltunski et al., 2018). Similar to the previous version, this corpus has been created to address translation of coreference across languages, a phenomenon still challenging for machine translation (MT) and other multilingual natural language processing (NLP) applications. The current version of the corpus that we present here contains not only parallel texts for the language pair English-German, but also for English-French and English-Portuguese, which are all major European languages. The new language pairs belong to the Romance languages. The addition of a new language group creates a need of extension not only in terms of texts added, but also in terms of the annotation guidelines. Both French and Portuguese contain structures not found in English and German. Moreover, Portuguese is a pro-drop language bringing even more systemic differences in the realisation of coreference into our cross-lingual resources. These differences cause problems for multilingual coreference resolution and machine translation. Our parallel corpus with full annotation of coreference will be a valuable resource with a variety of uses not only for NLP applications, but also for contrastive linguists and researchers in translation studies.

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A Multi-Party Dialogue Ressource in French
Maria Boritchev | Maxime Amblard

We presentDialogues in Games(DinG), a corpus of manual transcriptions of real-life, oral, spontaneous multi-party dialogues between French-speaking players of the board game Catan. Our objective is to make available a quality resource for French, composed of long dialogues, to facilitate their study in the style of (Asher et al., 2016). In a general dialogue setting, participants share personal information, which makes it impossible to disseminate the resource freely and openly. In DinG, the attention of the participants is focused on the game, which prevents them from talking about themselves. In addition, we are conducting a study on the nature of the questions in dialogue, through annotation (Cruz Blandon et al., 2019), in order to develop more natural automatic dialogue systems

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Bicleaner AI: Bicleaner Goes Neural
Jaume Zaragoza-Bernabeu | Gema Ramírez-Sánchez | Marta Bañón | Sergio Ortiz Rojas

This paper describes the experiments carried out during the development of the latest version of Bicleaner, named Bicleaner AI, a tool that aims at detecting noisy sentences in parallel corpora. The tool, which now implements a new neural classifier, uses state-of-the-art techniques based on pre-trained transformer-based language models fine-tuned on a binary classification task. After that, parallel corpus filtering is performed, discarding the sentences that have lower probability of being mutual translations. Our experiments, based on the training of neural machine translation (NMT) with corpora filtered using Bicleaner AI for two different scenarios, show significant improvements in translation quality compared to the previous version of the tool which implemented a classifier based on Extremely Randomized Trees.

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Semi-automatically Annotated Learner Corpus for Russian
Anisia Katinskaia | Maria Lebedeva | Jue Hou | Roman Yangarber

We present ReLCo— the Revita Learner Corpus—a new semi-automatically annotated learner corpus for Russian. The corpus was collected while several thousand L2 learners were performing exercises using the Revita language-learning system. All errors were detected automatically by the system and annotated by type. Part of the corpus was annotated manually—this part was created for further experiments on automatic assessment of grammatical correctness. The Learner Corpus provides valuable data for studying patterns of grammatical errors, experimenting with grammatical error detection and grammatical error correction, and developing new exercises for language learners. Automating the collection and annotation makes the process of building the learner corpus much cheaper and faster, in contrast to the traditional approach of building learner corpora. We make the data publicly available.

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet.

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Textinator: an Internationalized Tool for Annotation and Human Evaluation in Natural Language Processing and Generation
Dmytro Kalpakchi | Johan Boye

We release an internationalized annotation and human evaluation bundle, called Textinator, along with documentation and video tutorials. Textinator allows annotating data for a wide variety of NLP tasks, and its user interface is offered in multiple languages, lowering the entry threshold for domain experts. The latter is, in fact, quite a rare feature among the annotation tools, that allows controlling for possible unintended biases introduced due to hiring only English-speaking annotators. We illustrate the rarity of this feature by presenting a thorough systematic comparison of Textinator to previously published annotation tools along 9 different axes (with internationalization being one of them). To encourage researchers to design their human evaluation before starting to annotate data, Textinator offers an easy-to-use tool for human evaluations allowing importing surveys with potentially hundreds of evaluation items in one click. We finish by presenting several use cases of annotation and evaluation projects conducted using pre-release versions of Textinator. The presented use cases do not represent Textinator’s full annotation or evaluation capabilities, and interested readers are referred to the online documentation for more information.

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CyberAgressionAdo-v1: a Dataset of Annotated Online Aggressions in French Collected through a Role-playing Game
Anaïs Ollagnier | Elena Cabrio | Serena Villata | Catherine Blaya

Over the past decades, the number of episodes of cyber aggression occurring online has grown substantially, especially among teens. Most solutions investigated by the NLP community to curb such online abusive behaviors consist of supervised approaches relying on annotated data extracted from social media. However, recent studies have highlighted that private instant messaging platforms are major mediums of cyber aggression among teens. As such interactions remain invisible due to the app privacy policies, very few datasets collecting aggressive conversations are available for the computational analysis of language. In order to overcome this limitation, in this paper we present the CyberAgressionAdo-V1 dataset, containing aggressive multiparty chats in French collected through a role-playing game in high-schools, and annotated at different layers. We describe the data collection and annotation phases, carried out in the context of a EU and a national research projects, and provide insightful analysis on the different types of aggression and verbal abuse depending on the targeted victims (individuals or communities) emerging from the collected data.

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Finnish Hate-Speech Detection on Social Media Using CNN and FinBERT
Md Saroar Jahan | Mourad Oussalah | Nabil Arhab

There has been a lot of research in identifying hate posts from social media because of their detrimental effects on both individuals and society. The majority of this research has concentrated on English, although one notices the emergence of multilingual detection tools such as multilingual-BERT (mBERT). However, there is a lack of hate speech datasets compared to English, and a multilingual pre-trained model often contains fewer tokens for other languages. This paper attempts to contribute to hate speech identification in Finnish by constructing a new hate speech dataset that is collected from a popular forum (Suomi24). Furthermore, we have experimented with FinBERT pre-trained model performance for Finnish hate speech detection compared to state-of-the-art mBERT and other practices. In addition, we tested the performance of FinBERT compared to fastText as embedding, which employed with Convolution Neural Network (CNN). Our results showed that FinBERT yields a 91.7% accuracy and 90.8% F1 score value, which outperforms all state-of-art models, including multilingual-BERT and CNN.

Automatic post-editing (APE) refers to a research field that aims to automatically correct errors included in the translation sentences derived by the machine translation system. This study has several limitations, considering the data acquisition, because there is no official dataset for most language pairs. Moreover, the amount of data is restricted even for language pairs in which official data has been released, such as WMT. To solve this problem and promote universal APE research regardless of APE data existence, this study proposes a method for automatically generating APE data based on a noising scheme from a parallel corpus. Particularly, we propose a human mimicking errors-based noising scheme that considers a practical correction process at the human level. We propose a precise inspection to attain high performance, and we derived the optimal noising schemes that show substantial effectiveness. Through these, we also demonstrate that depending on the type of noise, the noising scheme-based APE data generation may lead to inferior performance. In addition, we propose a dynamic noise injection strategy that enables the acquisition of a robust error correction capability and demonstrated its effectiveness by comparative analysis. This study enables obtaining a high performance APE model without human-generated data and can promote universal APE research for all language pairs targeting English.

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Domain Mismatch Doesn’t Always Prevent Cross-lingual Transfer Learning
Daniel Edmiston | Phillip Keung | Noah A. Smith

Cross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering, unsupervised machine translation, etc. However, some recent publications have claimed that domain mismatch prevents cross-lingual transfer, and their results show that unsupervised bilingual lexicon induction (UBLI) and unsupervised neural machine translation (UNMT) do not work well when the underlying monolingual corpora come from different domains (e.g., French text from Wikipedia but English text from UN proceedings). In this work, we show how a simple initialization regimen can overcome much of the effect of domain mismatch in cross-lingual transfer. We pre-train word and contextual embeddings on the concatenated domain-mismatched corpora, and use these as initializations for three tasks: MUSE UBLI, UN Parallel UNMT, and the SemEval 2017 cross-lingual word similarity task. In all cases, our results challenge the conclusions of prior work by showing that proper initialization can recover a large portion of the losses incurred by domain mismatch.

Clinical phenotyping enables the automatic extraction of clinical conditions from patient records, which can be beneficial to doctors and clinics worldwide. However, current state-of-the-art models are mostly applicable to clinical notes written in English. We therefore investigate cross-lingual knowledge transfer strategies to execute this task for clinics that do not use the English language and have a small amount of in-domain data available. Our results reveal two strategies that outperform the state-of-the-art: Translation-based methods in combination with domain-specific encoders and cross-lingual encoders plus adapters. We find that these strategies perform especially well for classifying rare phenotypes and we advise on which method to prefer in which situation. Our results show that using multilingual data overall improves clinical phenotyping models and can compensate for data sparseness.

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The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text
Paul McNamee | Kevin Duh

Translation of the noisy, informal language found in social media has been an understudied problem, with a principal factor being the limited availability of translation corpora in many languages. To address this need we have developed a new corpus containing over 200,000 translations of microblog posts that supports translation of thirteen languages into English. The languages are: Arabic, Chinese, Farsi, French, German, Hindi, Korean, Pashto, Portuguese, Russian, Spanish, Tagalog, and Urdu. We are releasing these data as the Multilingual Microblog Translation Corpus to support futher research in translation of informal language. We establish baselines using this new resource, and we further demonstrate the utility of the corpus by conducting experiments with fine-tuning to improve translation quality from a high performing neural machine translation (NMT) system. Fine-tuning provided substantial gains, ranging from +3.4 to +11.1 BLEU. On average, a relative gain of 21% was observed, demonstrating the utility of the corpus.

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Multilingual and Multimodal Learning for Brazilian Portuguese
Júlia Sato | Helena Caseli | Lucia Specia

Humans constantly deal with multimodal information, that is, data from different modalities, such as texts and images. In order for machines to process information similarly to humans, they must be able to process multimodal data and understand the joint relationship between these modalities. This paper describes the work performed on the VTLM (Visual Translation Language Modelling) framework from (Caglayan et al., 2021) to test its generalization ability for other language pairs and corpora. We use the multimodal and multilingual corpus How2 (Sanabria et al., 2018) in three parallel streams with aligned English-Portuguese-Visual information to investigate the effectiveness of the model for this new language pair and in more complex scenarios, where the sentence associated with each image is not a simple description of it. Our experiments on the Portuguese-English multimodal translation task using the How2 dataset demonstrate the efficacy of cross-lingual visual pretraining. We achieved a BLEU score of 51.8 and a METEOR score of 78.0 on the test set, outperforming the MMT baseline by about 14 BLEU and 14 METEOR. The good BLEU and METEOR values obtained for this new language pair, regarding the original English-German VTLM, establish the suitability of the model to other languages.

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LibriS2S: A German-English Speech-to-Speech Translation Corpus
Pedro Jeuris | Jan Niehues

Recently, we have seen an increasing interest in the area of speech-to-text translation. This has led to astonishing improvements in this area. In contrast, the activities in the area of speech-to-speech translation is still limited, although it is essential to overcome the language barrier. We believe that one of the limiting factors is the availability of appropriate training data. We address this issue by creating LibriS2S, to our knowledge the first publicly available speech-to-speech training corpus between German and English. For this corpus, we used independently created audio for German and English leading to an unbiased pronunciation of the text in both languages. This allows the creation of a new text-to-speech and speech-to-speech translation model that directly learns to generate the speech signal based on the pronunciation of the source language. Using this created corpus, we propose Text-to-Speech models based on the example of the recently proposed FastSpeech 2 model that integrates source language information. We do this by adapting the model to take information such as the pitch, energy or transcript from the source speech as additional input.

This paper presents a fine-grained test suite for the language pair German–English. The test suite is based on a number of linguistically motivated categories and phenomena and the semi-automatic evaluation is carried out with regular expressions. We describe the creation and implementation of the test suite in detail, providing a full list of all categories and phenomena. Furthermore, we present various exemplary applications of our test suite that have been implemented in the past years, like contributions to the Conference of Machine Translation, the usage of the test suite and MT outputs for quality estimation, and the expansion of the test suite to the language pair Portuguese–English. We describe how we tracked the development of the performance of various systems MT systems over the years with the help of the test suite and which categories and phenomena are prone to resulting in MT errors. For the first time, we also make a large part of our test suite publicly available to the research community.

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Cross-lingual Transfer of Monolingual Models
Evangelia Gogoulou | Ariel Ekgren | Tim Isbister | Magnus Sahlgren

Recent studies in cross-lingual learning using multilingual models have cast doubt on the previous hypothesis that shared vocabulary and joint pre-training are the keys to cross-lingual generalization. We introduce a method for transferring monolingual models to other languages through continuous pre-training and study the effects of such transfer from four different languages to English. Our experimental results on GLUE show that the transferred models outperform an English model trained from scratch, independently of the source language. After probing the model representations, we find that model knowledge from the source language enhances the learning of syntactic and semantic knowledge in English.

We present a dataset containing source code solutions to algorithmic programming exercises solved by hundreds of Bachelor-level students at the University of Hamburg. These solutions were collected during the winter semesters 2019/2020, 2020/2021 and 2021/2022. The dataset contains a set of solutions to a total of 21 tasks written in Java as well as Python and a total of over 1500 individual solutions. All solutions were submitted through Moodle and the Coderunner plugin and passed a number of test cases (including randomized tests), such that they can be considered as working correctly. All students whose solutions are included in the dataset gave their consent into publishing their solutions. The solutions are pseudonymized with a random solution ID. Included in this paper is a short analysis of the dataset containing statistical data and highlighting a few anomalies (e.g. the number of solutions per task decreases for the last few tasks due to grading rules). We plan to extend the dataset with tasks and solutions from upcoming courses.

In this work, we conduct a quantitative linguistic analysis of the language usage patterns of multilingual peer supporters in two health-focused WhatsApp groups in Kenya comprising of youth living with HIV. Even though the language of communication for the group was predominantly English, we observe frequent use of Kiswahili, Sheng and code-mixing among the three languages. We present an analysis of language choice and its accommodation, different functions of code-mixing, and relationship between sentiment and code-mixing. To explore the effectiveness of off-the-shelf Language Technologies (LT) in such situations, we attempt to build a sentiment analyzer for this dataset. Our experiments demonstrate the challenges of developing LT and therefore effective interventions for such forums and languages. We provide recommendations for language resources that should be built to address these challenges.

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Frame Shift Prediction
Zheng Xin Yong | Patrick D. Watson | Tiago Timponi Torrent | Oliver Czulo | Collin Baker

Frame shift is a cross-linguistic phenomenon in translation which results in corresponding pairs of linguistic material evoking different frames. The ability to predict frame shifts would enable (semi-)automatic creation of multilingual frame annotations and thus speeding up FrameNet creation through annotation projection. Here, we first characterize how frame shifts result from other linguistic divergences such as translational divergences and construal differences. Our analysis also shows that many pairs of frames in frame shifts are multi-hop away from each other in Berkeley FrameNet’s net-like configuration. Then, we propose the Frame Shift Prediction task and demonstrate that our graph attention networks, combined with auxiliary training, can learn cross-linguistic frame-to-frame correspondence and predict frame shifts.

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CLeLfPC: a Large Open Multi-Speaker Corpus of French Cued Speech
Brigitte Bigi | Maryvonne Zimmermann | Carine André

Cued Speech is a communication system developed for deaf people to complement speechreading at the phonetic level with hands. This visual communication mode uses handshapes in different placements near the face in combination with the mouth movements of speech to make the phonemes of spoken language look different from each other. This paper describes CLeLfPC - Corpus de Lecture en Langue française Parlée Complétée, a corpus of French Cued Speech. It consists in about 4 hours of audio and HD video recordings of 23 participants. The recordings are 160 different isolated ‘CV’ syllables repeated 5 times, 320 words or phrases repeated 2-3 times and about 350 sentences repeated 2-3 times. The corpus is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. It can be used for any further research or teaching purpose. The corpus includes orthographic transliteration and other phonetic annotations on 5 of the recorded topics, i.e. syllables, words, isolated sentences and a text. The early results are encouraging: it seems that 1/ the hand position has a high influence on the key audio duration; and 2/ the hand shape has not.

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Samrómur Children: An Icelandic Speech Corpus
Carlos Daniel Hernandez Mena | David Erik Mollberg | Michal Borský | Jón Guðnason

Samrómur Children is an Icelandic speech corpus intended for the field of automatic speech recognition. It contains 131 hours of read speech from Icelandic children aged between 4 to 17 years. The test portion was meticulously selected to cover a wide range of ages as possible; we aimed to have exactly the same amount of data per age range. The speech was collected with the crowd-sourcing platform Samrómur.is, which is inspired on the “Mozilla’s Common Voice Project”. The corpus was developed within the framework of the “Language Technology Programme for Icelandic 2019 − 2023”; the goal of the project is to make Icelandic available in language-technology applications. Samrómur Children is the first corpus in Icelandic with children’s voices for public use under a Creative Commons license. Additionally, we present baseline experiments and results using Kaldi.

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The Norwegian Parliamentary Speech Corpus
Per Erik Solberg | Pablo Ortiz

The Norwegian Parliamentary Speech Corpus (NPSC) is a speech dataset with recordings of meetings from Stortinget, the Norwegian parliament. It is the first, publicly available dataset containing unscripted, Norwegian speech designed for training of automatic speech recognition (ASR) systems. The recordings are manually transcribed and annotated with language codes and speakers, and there are detailed metadata about the speakers. The transcriptions exist in both normalized and non-normalized form, and non-standardized words are explicitly marked and annotated with standardized equivalents. To test the usefulness of this dataset, we have compared an ASR system trained on the NPSC with a baseline system trained on only manuscript-read speech. These systems were tested on an independent dataset containing spontaneous, dialectal speech. The NPSC-trained system performed significantly better, with a 22.9% relative improvement in word error rate (WER). Moreover, training on the NPSC is shown to have a “democratizing” effects in terms of dialects, as improvements are generally larger for dialects with higher WER from the baseline system.

We developed a bilingual Frisian/Dutch speech recognizer for council meetings in Fryslân (the Netherlands). During these meetings both Frisian and Dutch are spoken, and code switching between both languages shows up frequently. The new speech recognizer is based on an existing speech recognizer for Frisian and Dutch named FAME!, which was trained and tested on historical radio broadcasts. Adapting a speech recognizer for the council meeting domain is challenging because of acoustic background noise, speaker overlap and the jargon typically used in council meetings. To train the new recognizer, we used the radio broadcast materials utilized for the development of the FAME! recognizer and added newly created manually transcribed audio recordings of council meetings from eleven Frisian municipalities, the Frisian provincial council and the Frisian water board. The council meeting recordings consist of 49 hours of speech, with 26 hours of Frisian speech and 23 hours of Dutch speech. Furthermore, from the same sources, we obtained texts in the domain of council meetings containing 11 million words; 1.1 million Frisian words and 9.9 million Dutch words. We describe the methods used to train the new recognizer, report the observed word error rates, and perform an error analysis on remaining errors.

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Elderly Conversational Speech Corpus with Cognitive Impairment Test and Pilot Dementia Detection Experiment Using Acoustic Characteristics of Speech in Japanese Dialects
Meiko Fukuda | Ryota Nishimura | Maina Umezawa | Kazumasa Yamamoto | Yurie Iribe | Norihide Kitaoka

There is a need for a simple method of detecting early signs of dementia which is not burdensome to patients, since early diagnosis and treatment can often slow the advance of the disease. Several studies have explored using only the acoustic and linguistic information of conversational speech as diagnostic material, with some success. To accelerate this research, we recorded natural conversations between 128 elderly people living in four different regions of Japan and interviewers, who also administered the Hasegawa’s Dementia Scale-Revised (HDS-R), a cognitive impairment test. Using our elderly speech corpus and dementia test results, we propose an SVM-based screening method which can detect dementia using the acoustic features of conversational speech even when regional dialects are present. We accomplish this by omitting some acoustic features, to limit the negative effect of differences between dialects. When using our proposed method, a dementia detection accuracy rate of about 91% was achieved for speakers from two regions. When speech from four regions was used in a second experiment, the discrimination rate fell to 76.6%, but this may have been due to using only sentence-level acoustic features in the second experiment, instead of sentence and phoneme-level features as in the previous experiment. This is an on-going research project, and additional investigation is needed to understand differences in the acoustic characteristics of phoneme units in the conversational speech collected from these four regions, to determine whether the removal of formants and other features can improve the dementia detection rate.

Spoken medical dialogue systems are increasingly attracting interest to enhance access to healthcare services and improve quality and traceability of patient care. In this paper, we focus on medical drug prescriptions acquired on smartphones through spoken dialogue. Such systems would facilitate the traceability of care and would free the clinicians’ time. However, there is a lack of speech corpora to develop such systems since most of the related corpora are in text form and in English. To facilitate the research and development of spoken medical dialogue systems, we present, to the best of our knowledge, the first spoken medical drug prescriptions corpus, named PxNLU. It contains 4 hours of transcribed and annotated dialogues of drug prescriptions in French acquired through an experiment with 55 participants experts and non-experts in prescriptions. We also present some experiments that demonstrate the interest of this corpus for the evaluation and development of medical dialogue systems.

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Towards an Open-Source Dutch Speech Recognition System for the Healthcare Domain
Cristian Tejedor-García | Berrie van der Molen | Henk van den Heuvel | Arjan van Hessen | Toine Pieters

The current largest open-source generic automatic speech recognition (ASR) system for Dutch, Kaldi_NL, does not include a domain-specific healthcare jargon in the lexicon. Commercial alternatives (e.g., Google ASR system) are also not suitable for this purpose, not only because of the lexicon issue, but they do not safeguard privacy of sensitive data sufficiently and reliably. These reasons motivate that just a small amount of medical staff employs speech technology in the Netherlands. This paper proposes an innovative ASR training method developed within the Homo Medicinalis (HoMed) project. On the semantic level it specifically targets automatic transcription of doctor-patient consultation recordings with a focus on the use of medicines. In the first stage of HoMed, the Kaldi_NL language model (LM) is fine-tuned with lists of Dutch medical terms and transcriptions of Dutch online healthcare news bulletins. Despite the acoustic challenges and linguistic complexity of the domain, we reduced the word error rate (WER) by 5.2%. The proposed method could be employed for ASR domain adaptation to other domains with sensitive and special category data. These promising results allow us to apply this methodology on highly sensitive audiovisual recordings of patient consultations at the Netherlands Institute for Health Services Research (Nivel).

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A Dataset for Speech Emotion Recognition in Greek Theatrical Plays
Maria Moutti | Sofia Eleftheriou | Panagiotis Koromilas | Theodoros Giannakopoulos

Machine learning methodologies can be adopted in cultural applications and propose new ways to distribute or even present the cultural content to the public. For instance, speech analytics can be adopted to automatically generate subtitles in theatrical plays, in order to (among other purposes) help people with hearing loss. Apart from a typical speech-to-text transcription with Automatic Speech Recognition (ASR), Speech Emotion Recognition (SER) can be used to automatically predict the underlying emotional content of speech dialogues in theatrical plays, and thus to provide a deeper understanding how the actors utter their lines. However, real-world datasets from theatrical plays are not available in the literature. In this work we present GreThE, the Greek Theatrical Emotion dataset, a new publicly available data collection for speech emotion recognition in Greek theatrical plays. The dataset contains utterances from various actors and plays, along with respective valence and arousal annotations. Towards this end, multiple annotators have been asked to provide their input for each speech recording and inter-annotator agreement is taken into account in the final ground truth generation. In addition, we discuss the results of some indicative experiments that have been conducted with machine and deep learning frameworks, using the dataset, along with some widely used databases in the field of speech emotion recognition.

Synthetic voices are increasingly used in applications that require a conversational speaking style, raising the question as to which type of training data yields the most suitable speaking style for such applications. This study compares voices trained on three corpora of equal size recorded by the same speaker: an audiobook character speech (dialogue) corpus, an audiobook narrator speech corpus, and a neutral-style sentence-based corpus. The voices were trained with three text-to-speech synthesisers: two hidden Markov model-based synthesisers and a neural synthesiser. An evaluation study tested the suitability of their speaking style for use in customer service voice chatbots. Independently of the synthesiser used, the voices trained on the character speech corpus received the lowest, and those trained on the neutral-style corpus the highest scores. However, the evaluation results may have been confounded by the greater acoustic variability, less balanced sentence length distribution, and poorer phonemic coverage of the character speech corpus, especially compared to the neutral-style corpus. Therefore, the next step will be the creation of a more uniform, balanced, and representative audiobook dialogue corpus, and the evaluation of its suitability for further conversational-style applications besides customer service chatbots.

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Using a Knowledge Base to Automatically Annotate Speech Corpora and to Identify Sociolinguistic Variation
Yaru Wu | Fabian Suchanek | Ioana Vasilescu | Lori Lamel | Martine Adda-Decker

Speech characteristics vary from speaker to speaker. While some variation phenomena are due to the overall communication setting, others are due to diastratic factors such as gender, provenance, age, and social background. The analysis of these factors, although relevant for both linguistic and speech technology communities, is hampered by the need to annotate existing corpora or to recruit, categorise, and record volunteers as a function of targeted profiles. This paper presents a methodology that uses a knowledge base to provide speaker-specific information. This can facilitate the enrichment of existing corpora with new annotations extracted from the knowledge base. The method also helps the large scale analysis by automatically extracting instances of speech variation to correlate with diastratic features. We apply our method to an over 120-hour corpus of broadcast speech in French and investigate variation patterns linked to reduction phenomena and/or specific to connected speech such as disfluencies. We find significant differences in speech rate, the use of filler words, and the rate of non-canonical realisations of frequent segments as a function of different professional categories and age groups.

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Phone Inventories and Recognition for Every Language
Xinjian Li | Florian Metze | David R. Mortensen | Alan W Black | Shinji Watanabe

Identifying phone inventories is a crucial component in language documentation and the preservation of endangered languages. However, even the largest collection of phone inventory only covers about 2000 languages, which is only 1/4 of the total number of languages in the world. A majority of the remaining languages are endangered. In this work, we attempt to solve this problem by estimating the phone inventory for any language listed in Glottolog, which contains phylogenetic information regarding 8000 languages. In particular, we propose one probabilistic model and one non-probabilistic model, both using phylogenetic trees (“language family trees”) to measure the distance between languages. We show that our best model outperforms baseline models by 6.5 F1. Furthermore, we demonstrate that, with the proposed inventories, the phone recognition model can be customized for every language in the set, which improved the PER (phone error rate) in phone recognition by 25%.

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Constructing Parallel Corpora from COVID-19 News using MediSys Metadata
Dimitrios Roussis | Vassilis Papavassiliou | Sokratis Sofianopoulos | Prokopis Prokopidis | Stelios Piperidis

This paper presents a collection of parallel corpora generated by exploiting the COVID-19 related dataset of metadata created with the Europe Media Monitor (EMM) / Medical Information System (MediSys) processing chain of news articles. We describe how we constructed comparable monolingual corpora of news articles related to the current pandemic and used them to mine about 11.2 million segment alignments in 26 EN-X language pairs, covering most official EU languages plus Albanian, Arabic, Icelandic, Macedonian, and Norwegian. Subsets of this collection have been used in shared tasks (e.g. Multilingual Semantic Search, Machine Translation) aimed at accelerating the creation of resources and tools needed to facilitate access to information in the COVID-19 emergency situation.

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A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes
Dongxu Zhang | Sunil Mohan | Michaela Torkar | Andrew McCallum

We introduce ChemDisGene, a new dataset for training and evaluating multi-class multi-label biomedical relation extraction models. Our dataset contains 80k biomedical research abstracts labeled with mentions of chemicals, diseases, and genes, portions of which human experts labeled with 18 types of biomedical relationships between these entities (intended for evaluation), and the remainder of which (intended for training) has been distantly labeled via the CTD database with approximately 78% accuracy. In comparison to similar preexisting datasets, ours is both substantially larger and cleaner; it also includes annotations linking mentions to their entities. We also provide three baseline deep neural network relation extraction models trained and evaluated on our new dataset.

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DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries
Jayetri Bardhan | Anthony Colas | Kirk Roberts | Daisy Zhe Wang

This paper develops the first question answering dataset (DrugEHRQA) containing question-answer pairs from both structured tables and unstructured notes from a publicly available Electronic Health Record (EHR). EHRs contain patient records, stored in structured tables and unstructured clinical notes. The information in structured and unstructured EHRs is not strictly disjoint: information may be duplicated, contradictory, or provide additional context between these sources. Our dataset has medication-related queries, containing over 70,000 question-answer pairs. To provide a baseline model and help analyze the dataset, we have used a simple model (MultimodalEHRQA) which uses the predictions of a modality selection network to choose between EHR tables and clinical notes to answer the questions. This is used to direct the questions to the table-based or text-based state-of-the-art QA model. In order to address the problem arising from complex, nested queries, this is the first time Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers (RAT-SQL) has been used to test the structure of query templates in EHR data. Our goal is to provide a benchmark dataset for multi-modal QA systems, and to open up new avenues of research in improving question answering over EHR structured data by using context from unstructured clinical data.

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Efficiently and Thoroughly Anonymizing a Transformer Language Model for Dutch Electronic Health Records: a Two-Step Method
Stella Verkijk | Piek Vossen

Neural Network (NN) architectures are used more and more to model large amounts of data, such as text data available online. Transformer-based NN architectures have shown to be very useful for language modelling. Although many researchers study how such Language Models (LMs) work, not much attention has been paid to the privacy risks of training LMs on large amounts of data and publishing them online. This paper presents a new method for anonymizing a language model by presenting the way in which MedRoBERTa.nl, a Dutch language model for hospital notes, was anonymized. The two-step method involves i) automatic anonymization of the training data and ii) semi-automatic anonymization of the LM’s vocabulary. Adopting the fill-mask task where the model predicts what tokens are most probable in a certain context, it was tested how often the model will predict a name in a context where a name should be. It was shown that it predicts a name-like token 0.2% of the time. Any name-like token that was predicted was never the name originally present in the training data. By explaining how a LM trained on highly private real-world medical data can be published, we hope that more language resources will be published openly and responsibly so the scientific community can profit from them.

The successes of contextual word embeddings learned by training large-scale language models, while remarkable, have mostly occurred for languages where significant amounts of raw texts are available and where annotated data in downstream tasks have a relatively regular spelling. Conversely, it is not yet completely clear if these models are also well suited for lesser-resourced and more irregular languages. We study the case of Old French, which is in the interesting position of having relatively limited amount of available raw text, but enough annotated resources to assess the relevance of contextual word embedding models for downstream NLP tasks. In particular, we use POS-tagging and dependency parsing to evaluate the quality of such models in a large array of configurations, including models trained from scratch from small amounts of raw text and models pre-trained on other languages but fine-tuned on Medieval French data.

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Out-of-Domain Evaluation of Finnish Dependency Parsing
Jenna Kanerva | Filip Ginter

The prevailing practice in the academia is to evaluate the model performance on in-domain evaluation data typically set aside from the training corpus. However, in many real world applications the data on which the model is applied may very substantially differ from the characteristics of the training data. In this paper, we focus on Finnish out-of-domain parsing by introducing a novel UD Finnish-OOD out-of-domain treebank including five very distinct data sources (web documents, clinical, online discussions, tweets, and poetry), and a total of 19,382 syntactic words in 2,122 sentences released under the Universal Dependencies framework. Together with the new treebank, we present extensive out-of-domain parsing evaluation utilizing the available section-level information from three different Finnish UD treebanks (TDT, PUD, OOD). Compared to the previously existing treebanks, the new Finnish-OOD is shown include sections more challenging for the general parser, creating an interesting evaluation setting and yielding valuable information for those applying the parser outside of its training domain.

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TArC: Tunisian Arabish Corpus, First complete release
Elisa Gugliotta | Marco Dinarelli

In this paper we present the final result of a project focused on Tunisian Arabic encoded in Arabizi, the Latin-based writing system for digital conversations. The project led to the realization of two integrated and independent tools: a linguistic corpus and a neural network architecture created to annotate the former with various levels of linguistic information (code-switching classification, transliteration, tokenization, POS-tagging, lemmatization). We discuss the choices made in terms of computational and linguistic methodology and the strategies adopted to improve our results. We report on the experiments performed in order to outline our research path. Finally, we explain the reasons why we believe in the potential of these tools for both computational and linguistic researches.

Our work aims at developing a multilingual data resource for morphological segmentation. We present a survey of 17 existing data resources relevant for segmentation in 32 languages, and analyze diversity of how individual linguistic phenomena are captured across them. Inspired by the success of Universal Dependencies, we propose a harmonized scheme for segmentation representation, and convert the data from the studied resources into this common scheme. Harmonized versions of resources available under free licenses are published as a collection called UniSegments 1.0.

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TeDDi Sample: Text Data Diversity Sample for Language Comparison and Multilingual NLP
Steven Moran | Christian Bentz | Ximena Gutierrez-Vasques | Olga Pelloni | Tanja Samardzic

We present the TeDDi sample, a diversity sample of text data for language comparison and multilingual Natural Language Processing. The TeDDi sample currently features 89 languages based on the typological diversity sample in the World Atlas of Language Structures. It consists of more than 20k texts and is accompanied by open-source corpus processing tools. The aim of TeDDi is to facilitate text-based quantitative analysis of linguistic diversity. We describe in detail the TeDDi sample, how it was created, data availability, and its added value through for NLP and linguistic research.

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Leveraging a Bilingual Dictionary to Learn Wolastoqey Word Representations
Diego Bear | Paul Cook

Word embeddings (Mikolov et al., 2013; Pennington et al., 2014) have been used to bolster the performance of natural language processing systems in a wide variety of tasks, including information retrieval (Roy et al., 2018) and machine translation (Qi et al., 2018). However, approaches to learning word embeddings typically require large corpora of running text to learn high quality representations. For many languages, such resources are unavailable. This is the case for Wolastoqey, also known as Passamaquoddy-Maliseet, an endangered low-resource Indigenous language. As there exist no large corpora of running text for Wolastoqey, in this paper, we leverage a bilingual dictionary to learn Wolastoqey word embeddings by encoding their corresponding English definitions into vector representations using pretrained English word and sequence representation models. Specifically, we consider representations based on pretrained word2vec (Mikolov et al., 2013), RoBERTa (Liu et al., 2019) and sentence-BERT (Reimers and Gurevych, 2019) models. We evaluate these embeddings in word prediction tasks focused on part-of-speech, animacy, and transitivity; semantic clustering; and reverse dictionary search. In all evaluations we demonstrate that approaches using these embeddings outperform task-specific baselines, without requiring any language-specific training or fine-tuning.

Machine learning (ML) approaches have dominated NLP during the last two decades. From machine translation and speech technology, ML tools are now also in use for spellchecking and grammar checking, with a blurry distinction between the two. We unmask the myth of effortless big data by illuminating the efforts and time that lay behind building a multi-purpose corpus with regard to collecting, mark-up and building from scratch. We also discuss what kind of language technology minority languages actually need, and to what extent the dominating paradigm has been able to deliver these tools. In this context we present our alternative to corpus-based language technology, which is knowledge-based language technology, and we show how this approach can provide language technology solutions for languages being outside the reach of machine learning procedures. We present a stable and mature infrastructure (GiellaLT) containing more than hundred languages and building a number of language technology tools that are useful for language communities.

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Building and curating conversational corpora for diversity-aware language science and technology
Andreas Liesenfeld | Mark Dingemanse

We present an analysis pipeline and best practice guidelines for building and curating corpora of everyday conversation in diverse languages. Surveying language documentation corpora and other resources that cover 67 languages and varieties from 28 phyla, we describe the compilation and curation process, specify minimal properties of a unified format for interactional data, and develop methods for quality control that take into account turn-taking and timing. Two case studies show the broad utility of conversational data for (i) charting human interactional infrastructure and (ii) tracing challenges and opportunities for current ASR solutions. Linguistically diverse conversational corpora can provide new insights for the language sciences and stronger empirical foundations for language technology.

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EPIC UdS - Creation and Applications of a Simultaneous Interpreting Corpus
Heike Przybyl | Ekaterina Lapshinova-Koltunski | Katrin Menzel | Stefan Fischer | Elke Teich

In this paper, we describe the creation and annotation of EPIC UdS, a multilingual corpus of simultaneous interpreting for English, German and Spanish. We give an overview of the comparable and parallel, aligned corpus variants and explore various applications of the corpus. What makes EPIC UdS relevant is that it is one of the rare interpreting corpora that includes transcripts suitable for research on more than one language pair and on interpreting with regard to German. It not only contains transcribed speeches, but also rich metadata and fine-grained linguistic annotations tailored for diverse applications across a broad range of linguistic subfields.

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Development of a Benchmark Corpus to Support Entity Recognition in Job Descriptions
Thomas Green | Diana Maynard | Chenghua Lin

We present the development of a benchmark suite consisting of an annotation schema, training corpus and baseline model for Entity Recognition (ER) in job descriptions, published under a Creative Commons license. This was created to address the distinct lack of resources available to the community for the extraction of salient entities, such as skills, from job descriptions. The dataset contains 18.6k entities comprising five types (Skill, Qualification, Experience, Occupation, and Domain). We include a benchmark CRF-based ER model which achieves an F1 score of 0.59. Through the establishment of a standard definition of entities and training/testing corpus, the suite is designed as a foundation for future work on tasks such as the development of job recommender systems.

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CAMIO: A Corpus for OCR in Multiple Languages
Michael Arrigo | Stephanie Strassel | Nolan King | Thao Tran | Lisa Mason

CAMIO (Corpus of Annotated Multilingual Images for OCR) is a new corpus created by Linguistic Data Consortium to serve as a resource to support the development and evaluation of optical character recognition (OCR) and related technologies for 35 languages across 24 unique scripts. The corpus comprises nearly 70,000 images of machine printed text, covering a wide variety of topics and styles, document domains, attributes and scanning/capture artifacts. Most images have been exhaustively annotated for text localization, resulting in over 2.3M line-level bounding boxes. For 13 of the 35 languages, 1250 images/language have been further annotated with orthographic transcriptions of each line plus specification of reading order, yielding over 2.4M tokens of transcribed text. The resulting annotations are represented in a comprehensive XML output format defined for this corpus. The paper discusses corpus design and implementation, challenges encountered, baseline performance results obtained on the corpus for text localization and OCR decoding, and plans for corpus publication.

In this paper, we present the FABRA: readability toolkit based on the aggregation of a large number of readability predictor variables. The toolkit is implemented as a service-oriented architecture, which obviates the need for installation, and simplifies its integration into other projects. We also perform a set of experiments to show which features are most predictive on two different corpora, and how the use of aggregators improves performance over standard feature-based readability prediction. Our experiments show that, for the explored corpora, the most important predictors for native texts are measures of lexical diversity, dependency counts and text coherence, while the most important predictors for foreign texts are syntactic variables illustrating language development, as well as features linked to lexical sophistication. FABRA: have the potential to support new research on readability assessment for French.

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Towards Building a Spoken Dialogue System for Argument Exploration
Annalena Aicher | Nadine Gerstenlauer | Isabel Feustel | Wolfgang Minker | Stefan Ultes

Speech interfaces for argumentative dialogue systems (ADS) are rather scarce. The complex task they pursue hinders the application of common natural language understanding (NLU) approaches in this domain. To address this issue we include an adaption of a recently introduced NLU framework tailored to argumentative tasks into a complete ADS. We evaluate the likeability and motivation of users to interact with the new system in a user study. Therefore, we compare it to a solid baseline utilizing a drop-down menu. The results indicate that the integration of a flexible NLU framework enables a far more natural and satisfying interaction with human users in real-time. Even though the drop-down menu convinces regarding its robustness, the willingness to use the new system is significantly higher. Hence, the featured NLU framework provides a sound basis to build an intuitive interface which can be extended to adapt its behavior to the individual user.

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FreeTalky: Don’t Be Afraid! Conversations Made Easier by a Humanoid Robot using Persona-based Dialogue
Chanjun Park | Yoonna Jang | Seolhwa Lee | Sungjin Park | Heuiseok Lim

We propose a deep learning-based foreign language learning platform, named FreeTalky, for people who experience anxiety dealing with foreign languages, by employing a humanoid robot NAO and various deep learning models. A persona-based dialogue system that is embedded in NAO provides an interesting and consistent multi-turn dialogue for users. Also, an grammar error correction system promotes improvement in grammar skills of the users. Thus, our system enables personalized learning based on persona dialogue and facilitates grammar learning of a user using grammar error feedback. Furthermore, we verified whether FreeTalky provides practical help in alleviating xenoglossophobia by replacing the real human in the conversation with a NAO robot, through human evaluation.

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Self-Contained Utterance Description Corpus for Japanese Dialog
Yuta Hayashibe

Often both an utterance and its context must be read to understand its intent in a dialog. Herein we propose a task, Self- Contained Utterance Description (SCUD), to describe the intent of an utterance in a dialog with multiple simple natural sentences without the context. If a task can be performed concurrently with high accuracy as the conversation continues such as in an accommodation search dialog, the operator can easily suggest candidates to the customer by inputting SCUDs of the customer’s utterances to the accommodation search system. SCUDs can also describe the transition of customer requests from the dialog log. We construct a Japanese corpus to train and evaluate automatic SCUD generation. The corpus consists of 210 dialogs containing 10,814 sentences. We conduct an experiment to verify that SCUDs can be automatically generated. Additionally, we investigate the influence of the amount of training data on the automatic generation performance using 8,200 additional examples.

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DialCrowd 2.0: A Quality-Focused Dialog System Crowdsourcing Toolkit
Jessica Huynh | Ting-Rui Chiang | Jeffrey Bigham | Maxine Eskenazi

Dialog system developers need high-quality data to train, fine-tune and assess their systems. They often use crowdsourcing for this since it provides large quantities of data from many workers. However, the data may not be of sufficiently good quality. This can be due to the way that the requester presents a task and how they interact with the workers. This paper introduces DialCrowd 2.0 to help requesters obtain higher quality data by, for example, presenting tasks more clearly and facilitating effective communication with workers. DialCrowd 2.0 guides developers in creating improved Human Intelligence Tasks (HITs) and is directly applicable to the workflows used currently by developers and researchers.

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A Brief Survey of Textual Dialogue Corpora
Hugo Gonçalo Oliveira | Patrícia Ferreira | Daniel Martins | Catarina Silva | Ana Alves

Several dialogue corpora are currently available for research purposes, but they still fall short for the growing interest in the development of dialogue systems with their own specific requirements. In order to help those requiring such a corpus, this paper surveys a range of available options, in terms of aspects like speakers, size, languages, collection, annotations, and domains. Some trends are identified and possible approaches for the creation of new corpora are also discussed.

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A Unified Approach to Entity-Centric Context Tracking in Social Conversations
Ulrich Rückert | Srinivas Sunkara | Abhinav Rastogi | Sushant Prakash | Pranav Khaitan

In human-human conversations, Context Tracking deals with identifying important entities and keeping track of their properties and relationships. This is a challenging problem that encompasses several subtasks such as slot tagging, coreference resolution, resolving plural mentions and entity linking. We approach this problem as an end-to-end modeling task where the conversational context is represented by an entity repository containing the entity references mentioned so far, their properties and the relationships between them. The repository is updated turn-by-turn, thus making training and inference computationally efficient even for long conversations. This paper lays the groundwork for an investigation of this framework in two ways. First, we release Contrack, a large scale human-human conversation corpus for context tracking with people and location annotations. It contains over 7000 conversations with an average of 11.8 turns, 5.8 entities and 15.2 references per conversation. Second, we open-source a neural network architecture for context tracking. Finally we compare this network to state-of-the-art approaches for the subtasks it subsumes and report results on the involved tradeoffs.

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DIASER: A Unifying View On Task-oriented Dialogue Annotation
Vojtěch Hudeček | Léon-Paul Schaub | Daniel Stancl | Patrick Paroubek | Ondřej Dušek

Every model is only as strong as the data that it is trained on. In this paper, we present a new dataset, obtained by merging four publicly available annotated corpora for task-oriented dialogues in several domains (MultiWOZ 2.2, CamRest676, DSTC2 and Schema-Guided Dialogue Dataset). This way, we assess the feasibility of providing a unified ontology and annotation schema covering several domains with a relatively limited effort. We analyze the characteristics of the resulting dataset along three main dimensions: language, information content and performance. We focus on aspects likely to be pertinent for improving dialogue success, e.g. dialogue consistency. Furthermore, to assess the usability of this new corpus, we thoroughly evaluate dialogue generation performance under various conditions with the help of two prominent recent end-to-end dialogue models: MarCo and GPT-2. These models were selected as popular open implementations representative of the two main dimensions of dialogue modelling. While we did not observe a significant gain for dialogue state tracking performance, we show that using more training data from different sources can improve language modelling capabilities and positively impact dialogue flow (consistency). In addition, we provide the community with one of the largest open dataset for machine learning experiments.

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A Multi-source Graph Representation of the Movie Domain for Recommendation Dialogues Analysis
Antonio Origlia | Martina Di Bratto | Maria Di Maro | Sabrina Mennella

In dialogue analysis, characterising named entities in the domain of interest is relevant in order to understand how people are making use of them for argumentation purposes. The movie recommendation domain is a frequently considered case study for many applications and by linguistic studies and, since many different resources have been collected throughout the years to describe it, a single database combining all these data sources is a valuable asset for cross-disciplinary investigations. We propose an integrated graph-based structure of multiple resources, enriched with the results of the application of graph analytics approaches to provide an encompassing view of the domain and of the way people talk about it during the recommendation task. While we cannot distribute the final resource because of licensing issues, we share the code to assemble and process it once the reference data have been obtained from the original sources.

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SHARE: A Lexicon of Harmful Expressions by Spanish Speakers
Flor Miriam Plaza-del-Arco | Ana Belén Parras Portillo | Pilar López Úbeda | Beatriz Gil | María-Teresa Martín-Valdivia

In this paper we present SHARE, a new lexical resource with 10,125 offensive terms and expressions collected from Spanish speakers. We retrieve this vocabulary using an existing chatbot developed to engage a conversation with users and collect insults via Telegram, named Fiero. This vocabulary has been manually labeled by five annotators obtaining a kappa coefficient agreement of 78.8%. In addition, we leverage the lexicon to release the first corpus in Spanish for offensive span identification research named OffendES_spans. Finally, we show the utility of our resource as an interpretability tool to explain why a comment may be considered offensive.

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Wiktextract: Wiktionary as Machine-Readable Structured Data
Tatu Ylonen

We present a machine-readable structured data version of Wiktionary. Unlike previous Wiktionary extractions, the new extractor, Wiktextract, fully interprets and expands templates and Lua modules in Wiktionary. This enables it to perform a more complete, robust, and maintainable extraction. The extracted data is multilingual and includes lemmas, inflected forms, translations, etymology, usage examples, pronunciations (including URLs of sound files), lexical and semantic relations, and various morphological, syntactic, semantic, topical, and dialectal annotations. We extract all data from the English Wiktionary. Comparing against previous extractions from language-specific dictionaries, we find that its coverage for non-English languages often matches or exceeds the coverage in the language-specific editions, with the added benefit that all glosses are in English. The data is freely available and regularly updated, enabling anyone to add more data and correct errors by editing Wiktionary. The extracted data is in JSON format and designed to be easy to use by researchers, downstream resources, and application developers.

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NyLLex: A Novel Resource of Swedish Words Annotated with Reading Proficiency Level
Daniel Holmer | Evelina Rennes

What makes a text easy to read or not, depends on a variety of factors. One of the most prominent is, however, if the text contains easy, and avoids difficult, words. Deciding if a word is easy or difficult is not a trivial task, since it depends on characteristics of the word in itself as well as the reader, but it can be facilitated by the help of a corpus annotated with word frequencies and reading proficiency levels. In this paper, we present NyLLex, a novel lexical resource derived from books published by Sweden’s largest publisher for easy language texts. NyLLex consists of 6,668 entries, with frequency counts distributed over six reading proficiency levels. We show that NyLLex, with its novel source material aimed at individuals of different reading proficiency levels, can serve as a complement to already existing resources for Swedish.

We present an extension of the SynSemClass Event-type Ontology, originally conceived as a bilingual Czech-English resource. We added German entries to the classes representing the concepts of the ontology. Having a different starting point than the original work (unannotated parallel corpus without links to a valency lexicon and, of course, different existing lexical resources), it was a challenge to adapt the annotation guidelines, the data model and the tools used for the original version. We describe the process and results of working in such a setup. We also show the next steps to adapt the annotation process, data structures and formats and tools necessary to make the addition of a new language in the future more smooth and efficient, and possibly to allow for various teams to work on SynSemClass extensions to many languages concurrently. We also present the latest release which contains the results of adding German, freely available for download as well as for online access.

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NomVallex: A Valency Lexicon of Czech Nouns and Adjectives
Veronika Kolářová | Anna Vernerová

We present NomVallex, a manually annotated valency lexicon of Czech nouns and adjectives. The lexicon is created in the theoretical framework of the Functional Generative Description and based on corpus data. In total, NomVallex 2.0 is comprised of 1027 lexical units contained in 570 lexemes, covering the following part-of-speech and derivational categories: deverbal and deadjectival nouns, and deverbal, denominal, deadjectival and primary adjectives. Valency properties of a lexical unit are captured in a valency frame which is modeled as a sequence of valency slots, supplemented with a list of morphemic forms. In order to make it possible to study the relationship between valency behavior of base words and their derivatives, lexical units of nouns and adjectives in NomVallex are linked to their respective base words, contained either in NomVallex itself or, in case of verbs, in a valency lexicon of Czech verbs called VALLEX. NomVallex enables a comparison of valency properties of a significant number of Czech nominals with their base words, both manually and in an automatic way; as such, we can address the theoretical question of argument inheritance, concentrating on systemic and non-systemic valency behavior.

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TZOS: an Online Terminology Database Aimed at Working on Basque Academic Terminology Collaboratively
Izaskun Aldezabal | Jose Mari Arriola | Arantxa Otegi

Terminology databases are highly useful for the dissemination of specialized knowledge. In this paper we present TZOS, an online terminology database to work on Basque academic terminology collaboratively. We show how this resource integrates the Communicative Theory of Terminology, together with the methodological matters, how it is connected with real corpus GARATERM, which terminology issues arise when terms are collected and future perspectives. The main objectives of this work are to develop basic tools to research academic registers and make the terminology collected by expert users available to the community. Even though TZOS has been designed for an educational context, its flexible structure makes possible to extend it also to the professional area. In this way, we have built IZIBI-TZOS which is a Civil Engineering oriented version of TZOS. These resources are already publicly available, and the ongoing work is towards the interlinking with other lexical resources by applying linking data principles.

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Animacy Denoting German Nouns: Annotation and Classification
Manfred Klenner | Anne Göhring

In this paper, we introduce a gold standard for animacy detection comprising almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities. We present inter-annotator agreement of our crowd-sourced seed annotations (9,000 nouns) and discuss the results of machine learning models applied to this data.

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x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations
Enrica Troiano | Laura Ana Maria Oberlaender | Maximilian Wegge | Roman Klinger

Emotion classification is often formulated as the task to categorize texts into a predefined set of emotion classes. So far, this task has been the recognition of the emotion of writers and readers, as well as that of entities mentioned in the text. We argue that a classification setup for emotion analysis should be performed in an integrated manner, including the different semantic roles that participate in an emotion episode. Based on appraisal theories in psychology, which treat emotions as reactions to events, we compile an English corpus of written event descriptions. The descriptions depict emotion-eliciting circumstances, and they contain mentions of people who responded emotionally. We annotate all experiencers, including the original author, with the emotions they likely felt. In addition, we link them to the event they found salient (which can be different for different experiencers in a text) by annotating event properties, or appraisals (e.g., the perceived event undesirability, the uncertainty of its outcome). Our analysis reveals patterns in the co-occurrence of people’s emotions in interaction. Hence, this richly-annotated resource provides useful data to study emotions and event evaluations from the perspective of different roles, and it enables the development of experiencer-specific emotion and appraisal classification systems.

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Polar Quantification of Actor Noun Phrases for German
Anne Göhring | Manfred Klenner

In this paper, we discuss work that strives to measure the degree of negativity - the negative polar load - of noun phrases, especially those denoting actors. Since no gold standard data is available for German for this quantification task, we generated a silver standard and used it to fine-tune a BERT-based intensity regressor. We evaluated the quality of the silver standard empirically and found that our lexicon-based quantification metric showed a strong correlation with human annotators.

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Czech Dataset for Cross-lingual Subjectivity Classification
Pavel Přibáň | Josef Steinberger

In this paper, we introduce a new Czech subjectivity dataset of 10k manually annotated subjective and objective sentences from movie reviews and descriptions. Our prime motivation is to provide a reliable dataset that can be used with the existing English dataset as a benchmark to test the ability of pre-trained multilingual models to transfer knowledge between Czech and English and vice versa. Two annotators annotated the dataset reaching 0.83 of the Cohen’s K inter-annotator agreement. To the best of our knowledge, this is the first subjectivity dataset for the Czech language. We also created an additional dataset that consists of 200k automatically labeled sentences. Both datasets are freely available for research purposes. Furthermore, we fine-tune five pre-trained BERT-like models to set a monolingual baseline for the new dataset and we achieve 93.56% of accuracy. We fine-tune models on the existing English dataset for which we obtained results that are on par with the current state-of-the-art results. Finally, we perform zero-shot cross-lingual subjectivity classification between Czech and English to verify the usability of our dataset as the cross-lingual benchmark. We compare and discuss the cross-lingual and monolingual results and the ability of multilingual models to transfer knowledge between languages.

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RED v2: Enhancing RED Dataset for Multi-Label Emotion Detection
Alexandra Ciobotaru | Mihai Vlad Constantinescu | Liviu P. Dinu | Stefan Dumitrescu

RED (Romanian Emotion Dataset) is a machine learning-based resource developed for the automatic detection of emotions in Romanian texts, containing single-label annotated tweets with one of the following emotions: joy, fear, sadness, anger and neutral. In this work, we propose REDv2, an open-source extension of RED by adding two more emotions, trust and surprise, and by widening the annotation schema so that the resulted novel dataset is multi-label. We show the overall reliability of our dataset by computing inter-annotator agreements per tweet using a formula suitable for our annotation setup and we aggregate all annotators’ opinions into two variants of ground truth, one suitable for multi-label classification and the other suitable for text regression. We propose strong baselines with two transformer models, the Romanian BERT and the multilingual XLM-Roberta model, in both categorical and regression settings.

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Fine-Grained Error Analysis and Fair Evaluation of Labeled Spans
Katrin Ortmann

The traditional evaluation of labeled spans with precision, recall, and F1-score has undesirable effects due to double penalties. Annotations with incorrect label or boundaries count as two errors instead of one, despite being closer to the target annotation than false positives or false negatives. In this paper, new error types are introduced, which more accurately reflect true annotation quality and ensure that every annotation counts only once. An algorithm for error identification in flat and multi-level annotations is presented and complemented with a proposal on how to calculate meaningful precision, recall, and F1-scores based on the more fine-grained error types. The exemplary application to three different annotation tasks (NER, chunking, parsing) shows that the suggested procedure not only prevents double penalties but also allows for a more detailed error analysis, thereby providing more insight into the actual weaknesses of a system.

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Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition
Elena V. Epure | Romain Hennequin

Multiple works have proposed to probe language models (LMs) for generalization in named entity (NE) typing (NET) and recognition (NER). However, little has been done in this direction for auto-regressive models despite their popularity and potential to express a wide variety of NLP tasks in the same unified format. We propose a new methodology to probe auto-regressive LMs for NET and NER generalization, which draws inspiration from human linguistic behavior, by resorting to meta-learning. We study NEs of various types individually by designing a zero-shot transfer strategy for NET. Then, we probe the model for NER by providing a few examples at inference. We introduce a novel procedure to assess the model’s memorization of NEs and report the memorization’s impact on the results. Our findings show that: 1) GPT2, a common pre-trained auto-regressive LM, without any fine-tuning for NET or NER, performs the tasksfairly well; 2) name irregularity when common for a NE type could be an effective exploitable cue; 3) the model seems to rely more on NE than contextual cues in few-shot NER; 4) NEs with words absent during LM pre-training are very challenging for both NET and NER.

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Frustratingly Easy Performance Improvements for Low-resource Setups: A Tale on BERT and Segment Embeddings
Rob van der Goot | Max Müller-Eberstein | Barbara Plank

As input representation for each sub-word, the original BERT architecture proposes the sum of the sub-word embedding, position embedding and a segment embedding. Sub-word and position embeddings are well-known and studied, and encode lexical information and word position, respectively. In contrast, segment embeddings are less known and have so far received no attention, despite being ubiquitous in large pre-trained language models. The key idea of segment embeddings is to encode to which of the two sentences (segments) a word belongs to — the intuition is to inform the model about the separation of sentences for the next sentence prediction pre-training task. However, little is known on whether the choice of segment impacts performance. In this work, we try to fill this gap and empirically study the impact of the segment embedding during inference time for a variety of pre-trained embeddings and target tasks. We hypothesize that for single-sentence prediction tasks performance is not affected — neither in mono- nor multilingual setups — while it matters when swapping segment IDs in paired-sentence tasks. To our surprise, this is not the case. Although for classification tasks and monolingual BERT models no large differences are observed, particularly word-level multilingual prediction tasks are heavily impacted. For low-resource syntactic tasks, we observe impacts of segment embedding and multilingual BERT choice. We find that the default setting for the most used multilingual BERT model underperforms heavily, and a simple swap of the segment embeddings yields an average improvement of 2.5 points absolute LAS score for dependency parsing over 9 different treebanks.

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The Subject Annotations of the Danish Parliament Corpus (2009-2017) - Evaluated with Automatic Multi-label Classification
Costanza Navarretta | Dorte Haltrup Hansen

This paper addresses the semi-automatic annotation of subjects, also called policy areas, in the Danish Parliament Corpus (2009-2017) v.2. Recently, the corpus has been made available through the CLARIN-DK repository, the Danish node of the European CLARIN infrastructure. The paper also contains an analysis of the subjects in the corpus, and a description of multi-label classification experiments act to verify the consistency of the subject annotation and the utility of the corpus for training classifiers on this type of data. The analysis of the corpus comprises an investigation of how often the parliament members addressed each subject and the relation between subjects and gender of the speaker. The classification experiments show that classifiers can determine the two co-occurring subjects of the speeches from the agenda titles with a performance similar to that of human annotators. Moreover, a multilayer perceptron achieved an F1-score of 0.68 on the same task when trained on bag of words vectors obtained from the speeches’ lemmas. This is an improvement of more than 0.6 with respect to the baseline, a majority classifier that accounts for the frequency of the classes. The result is promising given the high number of subject combinations (186) and the skewness of the data.

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A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation
Ashleigh Richardson | Janet Wiles

A significant challenge in developing translation systems for the world’s ∼7,000 languages is that very few have sufficient data for state-of-the-art techniques. Transfer learning is a promising direction for low-resource neural machine translation (NMT), but introduces many new variables which are often selected through ablation studies, costly trial-and-error, or niche expertise. When pre-training an NMT system for low-resource translation, the pre-training task is often chosen based on data abundance and similarity to the main task. Factors such as dataset sizes and similarity have typically been analysed independently in previous studies, due to the computational cost associated with systematic studies. However, these factors are not independent. We conducted a three-factor experiment to examine how language similarity, pre-training dataset size and main dataset size interacted in their effect on performance in pre-trained transformer-based low-resource NMT. We replicated the common finding that more data was beneficial in bilingual systems, but also found a statistically significant interaction between the three factors, which reduced the effectiveness of large pre-training datasets for some main task dataset sizes (p-value < 0.0018). The surprising trends identified in these interactions indicate that systematic studies of interactions may be a promising long-term direction for guiding research in low-resource neural methods.

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Holistic Evaluation of Automatic TimeML Annotators
Mustafa Ocal | Adrian Perez | Antonela Radas | Mark Finlayson

TimeML is a scheme for representing temporal information (times, events, & temporal relations) in texts. Although automatic TimeML annotation is challenging, there has been notable progress, with F1s of 0.8–0.9 for events and time detection subtasks, and F1s of 0.5–0.7 for relation extraction. Individually, these subtask results are reasonable, even good, but when combined to generate a full TimeML graph, is overall performance still acceptable? We present a novel suite of eight metrics, combined with a new graph-transformation experimental design, for holistic evaluation of TimeML graphs. We apply these metrics to four automatic TimeML annotation systems (CAEVO, TARSQI, CATENA, and ClearTK). We show that on average 1/3 of the TimeML graphs produced using these systems are inconsistent, and there is on average 1/5 more temporal indeterminacy than the gold-standard. We also show that the automatically generated graphs are on average 109 edits from the gold-standard, which is 1/3 toward complete replacement. Finally, we show that the relationship individual subtask performance and graph quality is non-linear: small errors in TimeML subtasks result in rapid degradation of final graph quality. These results suggest current automatic TimeML annotators are far from optimal and significant further improvement would be useful.

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Measuring Uncertainty in Translation Quality Evaluation (TQE)
Serge Gladkoff | Irina Sorokina | Lifeng Han | Alexandra Alekseeva

From both human translators (HT) and machine translation (MT) researchers’ point of view, translation quality evaluation (TQE) is an essential task. Translation service providers (TSPs) have to deliver large volumes of translations which meet customer specifications with harsh constraints of required quality level in tight time-frames and costs. MT researchers strive to make their models better, which also requires reliable quality evaluation. While automatic machine translation evaluation (MTE) metrics and quality estimation (QE) tools are widely available and easy to access, existing automated tools are not good enough, and human assessment from professional translators (HAP) are often chosen as the golden standard (CITATION). Human evaluations, however, are often accused of having low reliability and agreement. Is this caused by subjectivity or statistics is at play? How to avoid the entire text to be checked and be more efficient with TQE from cost and efficiency perspectives, and what is the optimal sample size of the translated text, so as to reliably estimate the translation quality of the entire material? This work carries out such a motivated research to correctly estimate the confidence intervals (CITATION) depending on the sample size of translated text, e.g. the amount of words or sentences, that needs to be processed on TQE workflow step for confident and reliable evaluation of overall translation quality. The methodology we applied for this work is from Bernoulli Statistical Distribution Modelling (BSDM) and Monte Carlo Sampling Analysis (MCSA).

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Challenging the Transformer-based models with a Classical Arabic dataset: Quran and Hadith
Shatha Altammami | Eric Atwell

Transformer-based models showed near-perfect results on several downstream tasks. However, their performance on classical Arabic texts is largely unexplored. To fill this gap, we evaluate monolingual, bilingual, and multilingual state-of-the-art models to detect relatedness between the Quran (Muslim holy book) and the Hadith (Prophet Muhammed teachings), which are complex classical Arabic texts with underlying meanings that require deep human understanding. To do this, we carefully built a dataset of Quran-verse and Hadith-teaching pairs by consulting sources of reputable religious experts. This study presents the methodology of creating the dataset, which we make available on our repository, and discusses the models’ performance that calls for the imminent need to explore avenues for improving the quality of these models to capture the semantics in such complex, low-resource texts.

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Question Modifiers in Visual Question Answering
William Britton | Somdeb Sarkhel | Deepak Venugopal

Visual Question Answering (VQA) is a challenge problem that can advance AI by integrating several important sub-disciplines including natural language understanding and computer vision. Large VQA datasets that are publicly available for training and evaluation have driven the growth of VQA models that have obtained increasingly larger accuracy scores. However, it is also important to understand how much a model understands the details that are provided in a question. For example, studies in psychology have shown that syntactic complexity places a larger cognitive load on humans. Analogously, we want to understand if models have the perceptual capability to handle modifications to questions. Therefore, we develop a new dataset using Amazon Mechanical Turk where we asked workers to add modifiers to questions based on object properties and spatial relationships. We evaluate this data on LXMERT which is a state-of-the-art model in VQA that focuses more extensively on language processing. Our conclusions indicate that there is a significant negative impact on the performance of the model when the questions are modified to include more detailed information.

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Multimodal Pipeline for Collection of Misinformation Data from Telegram
Jose Sosa | Serge Sharoff

The paper presents the outcomes of AI-COVID19, our project aimed at better understanding of misinformation flow about COVID-19 across social media platforms. The specific focus of the study reported in this paper is on collecting data from Telegram groups which are active in promotion of COVID-related misinformation. Our corpus collected so far contains around 28 million words, from almost one million messages. Given that a substantial portion of misinformation flow in social media is spread via multimodal means, such as images and video, we have also developed a mechanism for utilising such channels via producing automatic transcripts for videos and automatic classification for images into such categories as memes, screenshots of posts and other kinds of images. The accuracy of the image classification pipeline is around 87%.

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Identifying Tension in Holocaust Survivors’ Interview: Code-switching/Code-mixing as Cues
Xinyuan Xia | Lu Xiao | Kun Yang | Yueyue Wang

In this study, we thrive on finding out how code-switching and code-mixing (CS/CM) as a linguistic phenomenon could be a sign of tension in Holocaust survivors’ interviews. We first created an interview corpus (a total of 39 interviews) that contains manually annotated CS/CM codes (a total of 802 quotations). We then compared our annotations with the tension places in the corpus. The tensions are identified by a computational tool. We found that most of our annotations were captured in the tension places, and it showed a relatively outstanding performance. The finding implies that CS/CM can be appropriate cues for detecting tension in this communication context. Our CS/CM annotated interview corpus is openly accessible. Aside from annotating and examining CS/CM occurrences, we annotated silence situations in this open corpus. Silence is shown to be an indicator of tension in interpersonal communications. Making this corpus openly accessible, we call for more research endeavors on tension detection.

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Fine-tuning vs From Scratch: Do Vision & Language Models Have Similar Capabilities on Out-of-Distribution Visual Question Answering?
Kristian Nørgaard Jensen | Barbara Plank

Fine-tuning general-purpose pre-trained models has become a de-facto standard, also for Vision and Language tasks such as Visual Question Answering (VQA). In this paper, we take a step back and ask whether a fine-tuned model has superior linguistic and reasoning capabilities than a prior state-of-the-art architecture trained from scratch on the training data alone. We perform a fine-grained evaluation on out-of-distribution data, including an analysis on robustness due to linguistic variation (rephrasings). Our empirical results confirm the benefit of pre-training on overall performance and rephrasing in particular. But our results also uncover surprising limitations, particularly for answering questions involving boolean operations. To complement the empirical evaluation, this paper also surveys relevant earlier work on 1) available VQA data sets, 2) models developed for VQA, 3) pre-trained Vision+Language models, and 4) earlier fine-grained evaluation of pre-trained Vision+Language models.

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Multilingual Image Corpus – Towards a Multimodal and Multilingual Dataset
Svetla Koeva | Ivelina Stoyanova | Jordan Kralev

One of the processing tasks for large multimodal data streams is automatic image description (image classification, object segmentation and classification). Although the number and the diversity of image datasets is constantly expanding, still there is a huge demand for more datasets in terms of variety of domains and object classes covered. The goal of the project Multilingual Image Corpus (MIC 21) is to provide a large image dataset with annotated objects and object descriptions in 24 languages. The Multilingual Image Corpus consists of an Ontology of visual objects (based on WordNet) and a collection of thematically related images whose objects are annotated with segmentation masks and labels describing the ontology classes. The dataset is designed both for image classification and object detection and for semantic segmentation. The main contributions of our work are: a) the provision of large collection of high quality copyright-free images; b) the formulation of the Ontology of visual objects based on WordNet noun hierarchies; c) the precise manual correction of automatic object segmentation within the images and the annotation of object classes; and d) the association of objects and images with extended multilingual descriptions based on WordNet inner- and interlingual relations. The dataset can be used also for multilingual image caption generation, image-to-text alignment and automatic question answering for images and videos.

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Sign Language Production With Avatar Layering: A Critical Use Case over Rare Words
Jung-Ho Kim | Eui Jun Hwang | Sukmin Cho | Du Hui Lee | Jong Park

Sign language production (SLP) is the process of generating sign language videos from spoken language expressions. Since sign languages are highly under-resourced, existing vision-based SLP approaches suffer from out-of-vocabulary (OOV) and test-time generalization problems and thus generate low-quality translations. To address these problems, we introduce an avatar-based SLP system composed of a sign language translation (SLT) model and an avatar animation generation module. Our Transformer-based SLT model utilizes two additional strategies to resolve these problems: named entity transformation to reduce OOV tokens and context vector generation using a pretrained language model (e.g., BERT) to reliably train the decoder. Our system is validated on a new Korean-Korean Sign Language (KSL) dataset of weather forecasts and emergency announcements. Our SLT model achieves an 8.77 higher BLEU-4 score and a 4.57 higher ROUGE-L score over those of our baseline model. In a user evaluation, 93.48% of named entities were successfully identified by participants, demonstrating marked improvement on OOV issues.

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The VoxWorld Platform for Multimodal Embodied Agents
Nikhil Krishnaswamy | William Pickard | Brittany Cates | Nathaniel Blanchard | James Pustejovsky

We present a five-year retrospective on the development of the VoxWorld platform, first introduced as a multimodal platform for modeling motion language, that has evolved into a platform for rapidly building and deploying embodied agents with contextual and situational awareness, capable of interacting with humans in multiple modalities, and exploring their environments. In particular, we discuss the evolution from the theoretical underpinnings of the VoxML modeling language to a platform that accommodates both neural and symbolic inputs to build agents capable of multimodal interaction and hybrid reasoning. We focus on three distinct agent implementations and the functionality needed to accommodate all of them: Diana, a virtual collaborative agent; Kirby, a mobile robot; and BabyBAW, an agent who self-guides its own exploration of the world.

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MemoSen: A Multimodal Dataset for Sentiment Analysis of Memes
Eftekhar Hossain | Omar Sharif | Mohammed Moshiul Hoque

Posting and sharing memes have become a powerful expedient of expressing opinions on social media in recent days. Analysis of sentiment from memes has gained much attention to researchers due to its substantial implications in various domains like finance and politics. Past studies on sentiment analysis of memes have primarily been conducted in English, where low-resource languages gain little or no attention. However, due to the proliferation of social media usage in recent years, sentiment analysis of memes is also a crucial research issue in low resource languages. The scarcity of benchmark datasets is a significant barrier to performing multimodal sentiment analysis research in resource-constrained languages like Bengali. This paper presents a novel multimodal dataset (named MemoSen) for Bengali containing 4417 memes with three annotated labels positive, negative, and neutral. A detailed annotation guideline is provided to facilitate further resource development in this domain. Additionally, a set of experiments are carried out on MemoSen by constructing twelve unimodal (i.e., visual, textual) and ten multimodal (image+text) models. The evaluation exhibits that the integration of multimodal information significantly improves (about 1.2%) the meme sentiment classification compared to the unimodal counterparts and thus elucidate the novel aspects of multimodality.

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RUSAVIC Corpus: Russian Audio-Visual Speech in Cars
Denis Ivanko | Alexandr Axyonov | Dmitry Ryumin | Alexey Kashevnik | Alexey Karpov

We present a new audio-visual speech corpus (RUSAVIC) recorded in a car environment and designed for noise-robust speech recognition. Our goal was to produce a speech corpus which is natural (recorded in real driving conditions), controlled (providing different SNR levels by windows open/closed, moving/parked vehicle, etc.), and adequate size (the amount of data is enough to train state-of-the-art NN approaches). We focus on the problem of audio-visual speech recognition: with the use of automated lip-reading to improve the performance of audio-based speech recognition in the presence of severe acoustic noise caused by road traffic. We also describe the equipment and procedures used to create RUSAVIC corpus. Data are collected in a synchronous way through several smartphones located at different angles and equipped with FullHD video camera and microphone. The corpus includes the recordings of 20 drivers with minimum of 10 recording sessions for each. Besides providing a detailed description of the dataset and its collection pipeline, we evaluate several popular audio and visual speech recognition methods and present a set of baseline recognition results. At the moment RUSAVIC is a unique audio-visual corpus for the Russian language that is recorded in-the-wild condition and we make it publicly available.

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A First Corpus of AZee Discourse Expressions
Camille Challant | Michael Filhol

This paper presents a corpus of AZee discourse expressions, i.e. expressions which formally describe Sign Language utterances of any length using the AZee approach and language. The construction of this corpus had two main goals: a first reference corpus for AZee, and a test of its coverage on a significant sample of real-life utterances. We worked on productions from an existing corpus, namely the “40 breves”, containing an hour of French Sign Language. We wrote the corresponding AZee discourse expressions for the entire video content, i.e. expressions capturing the forms produced by the signers and their associated meaning by combining known production rules, a basic building block for these expressions. These are made available as a version 2 extension of the “40 breves”. We explain the way in which these expressions can be built, present the resulting corpus and set of production rules used, and perform first measurements on it. We also propose an evaluation of our corpus: for one hour of discourse, AZee allows to describe 94% of it, while ongoing studies are increasing this coverage. This corpus offers a lot of future prospects, for instance concerning synthesis with virtual signers, machine translation or formal grammars for Sign Language.

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BERTHA: Video Captioning Evaluation Via Transfer-Learned Human Assessment
Luis Lebron | Yvette Graham | Kevin McGuinness | Konstantinos Kouramas | Noel E. O’Connor

Evaluating video captioning systems is a challenging task as there are multiple factors to consider; for instance: the fluency of the caption, multiple actions happening in a single scene, and the human bias of what is considered important. Most metrics try to measure how similar the system generated captions are to a single or a set of human-annotated captions. This paper presents a new method based on a deep learning model to evaluate these systems. The model is based on BERT, which is a language model that has been shown to work well in multiple NLP tasks. The aim is for the model to learn to perform an evaluation similar to that of a human. To do so, we use a dataset that contains human evaluations of system generated captions. The dataset consists of the human judgments of the captions produces by the system participating in various years of the TRECVid video to text task. BERTHA obtain favourable results, outperforming the commonly used metrics in some setups.

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Abstract Meaning Representation for Gesture
Richard Brutti | Lucia Donatelli | Kenneth Lai | James Pustejovsky

This paper presents Gesture AMR, an extension to Abstract Meaning Representation (AMR), that captures the meaning of gesture. In developing Gesture AMR, we consider how gesture form and meaning relate; how gesture packages meaning both independently and in interaction with speech; and how the meaning of gesture is temporally and contextually determined. Our case study for developing Gesture AMR is a focused human-human shared task to build block structures. We develop an initial taxonomy of gesture act relations that adheres to AMR’s existing focus on predicate-argument structure while integrating meaningful elements unique to gesture. Pilot annotation shows Gesture AMR to be more challenging than standard AMR, and illustrates the need for more work on representation of dialogue and multimodal meaning. We discuss challenges of adapting an existing meaning representation to non-speech-based modalities and outline several avenues for expanding Gesture AMR.

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The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild
Taja Kuzman | Peter Rupnik | Nikola Ljubešić

This paper presents a new training dataset for automatic genre identification GINCO, which is based on 1,125 crawled Slovenian web documents that consist of 650,000 words. Each document was manually annotated for genre with a new annotation schema that builds upon existing schemata, having primarily clarity of labels and inter-annotator agreement in mind. The dataset consists of various challenges related to web-based data, such as machine translated content, encoding errors, multiple contents presented in one document etc., enabling evaluation of classifiers in realistic conditions. The initial machine learning experiments on the dataset show that (1) pre-Transformer models are drastically less able to model the phenomena, with macro F1 metrics ranging around 0.22, while Transformer-based models achieve scores of around 0.58, and (2) multilingual Transformer models work as well on the task as the monolingual models that were previously proven to be superior to multilingual models on standard NLP tasks.

With the emergence of neural end-to-end approaches for spoken language understanding (SLU), a growing number of studies have been presented during these last three years on this topic. The major part of these works addresses the spoken language understanding domain through a simple task like speech intent detection. In this context, new benchmark datasets have also been produced and shared with the community related to this task. In this paper, we focus on the French MEDIA SLU dataset, distributed since 2005 and used as a benchmark dataset for a large number of research works. This dataset has been shown as being the most challenging one among those accessible to the research community. Distributed by ELRA, this corpus is free for academic research since 2019. Unfortunately, the MEDIA dataset is not really used beyond the French research community. To facilitate its use, a complete recipe, including data preparation, training and evaluation scripts, has been built and integrated to SpeechBrain, an already popular open-source and all-in-one conversational AI toolkit based on PyTorch. This recipe is presented in this paper. In addition, based on the feedback of some researchers who have worked on this dataset for several years, some corrections have been brought to the initial manual annotation: the new version of the data will also be integrated into the ELRA catalogue, as the original one. More, a significant amount of data collected during the construction of the MEDIA corpus in the 2000s was never used until now: we present the first results reached on this subset — also included in the MEDIA SpeechBrain recipe — , that will be used for now as the MEDIA test2. Last, we discuss evaluation issues.

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BasqueGLUE: A Natural Language Understanding Benchmark for Basque
Gorka Urbizu | Iñaki San Vicente | Xabier Saralegi | Rodrigo Agerri | Aitor Soroa

Natural Language Understanding (NLU) technology has improved significantly over the last few years and multitask benchmarks such as GLUE are key to evaluate this improvement in a robust and general way. These benchmarks take into account a wide and diverse set of NLU tasks that require some form of language understanding, beyond the detection of superficial, textual clues. However, they are costly to develop and language-dependent, and therefore they are only available for a small number of languages. In this paper, we present BasqueGLUE, the first NLU benchmark for Basque, a less-resourced language, which has been elaborated from previously existing datasets and following similar criteria to those used for the construction of GLUE and SuperGLUE. We also report the evaluation of two state-of-the-art language models for Basque on BasqueGLUE, thus providing a strong baseline to compare upon. BasqueGLUE is freely available under an open license.

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Resources and Experiments on Sentiment Classification for Georgian
Nicolas Stefanovitch | Jakub Piskorski | Sopho Kharazi

This paper presents, to the best of our knowledge, the first ever publicly available annotated dataset for sentiment classification and semantic polarity dictionary for Georgian. The characteristics of these resources and the process of their creation are described in detail. The results of various experiments on the performance of both lexicon- and machine learning-based models for Georgian sentiment classification are also reported. Both 3-label (positive, neutral, negative) and 4-label settings (same labels + mixed) are considered. The machine learning models explored include, i.a., logistic regression, SVMs, and transformed-based models. We also explore transfer learning- and translation-based (to a well-supported language) approaches. The obtained results for Georgian are on par with the state-of-the-art results in sentiment classification for well studied languages when using training data of comparable size.

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CoFiF Plus: A French Financial Narrative Summarisation Corpus
Nadhem Zmandar | Tobias Daudert | Sina Ahmadi | Mahmoud El-Haj | Paul Rayson

Natural Language Processing is increasingly being applied in the finance and business industry to analyse the text of many different types of financial documents. Given the increasing growth of firms around the world, the volume of financial disclosures and financial texts in different languages and forms is increasing sharply and therefore the study of language technology methods that automatically summarise content has grown rapidly into a major research area. Corpora for financial narrative summarisation exists in English, but there is a significant lack of financial text resources in the French language. To remedy this, we present CoFiF Plus, the first French financial narrative summarisation dataset providing a comprehensive set of financial text written in French. The dataset has been extracted from French financial reports published in PDF file format. It is composed of 1,703 reports from the most capitalised companies in France (Euronext Paris) covering a time frame from 1995 to 2021. This paper describes the collection, annotation and validation of the financial reports and their summaries. It also describes the dataset and gives the results of some baseline summarisers. Our datasets will be openly available upon the acceptance of the paper.

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Generating Extended and Multilingual Summaries with Pre-trained Transformers
Rémi Calizzano | Malte Ostendorff | Qian Ruan | Georg Rehm

Almost all summarisation methods and datasets focus on a single language and short summaries. We introduce a new dataset called WikinewsSum for English, German, French, Spanish, Portuguese, Polish, and Italian summarisation tailored for extended summaries of approx. 11 sentences. The dataset comprises 39,626 summaries which are news articles from Wikinews and their sources. We compare three multilingual transformer models on the extractive summarisation task and three training scenarios on which we fine-tune mT5 to perform abstractive summarisation. This results in strong baselines for both extractive and abstractive summarisation on WikinewsSum. We also show how the combination of an extractive model with an abstractive one can be used to create extended abstractive summaries from long input documents. Finally, our results show that fine-tuning mT5 on all the languages combined significantly improves the summarisation performance on low-resource languages.

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MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases
Louis Martin | Angela Fan | Éric de la Clergerie | Antoine Bordes | Benoît Sagot

Progress in sentence simplification has been hindered by a lack of labeled parallel simplification data, particularly in languages other than English. We introduce MUSS, a Multilingual Unsupervised Sentence Simplification system that does not require labeled simplification data. MUSS uses a novel approach to sentence simplification that trains strong models using sentence-level paraphrase data instead of proper simplification data. These models leverage unsupervised pretraining and controllable generation mechanisms to flexibly adjust attributes such as length and lexical complexity at inference time. We further present a method to mine such paraphrase data in any language from Common Crawl using semantic sentence embeddings, thus removing the need for labeled data. We evaluate our approach on English, French, and Spanish simplification benchmarks and closely match or outperform the previous best supervised results, despite not using any labeled simplification data. We push the state of the art further by incorporating labeled simplification data.

Women are often perceived as junior to their male counterparts, even within the same job titles. While there has been significant progress in the evaluation of gender bias in natural language processing (NLP), existing studies seldom investigate how biases toward gender groups change when compounded with other societal biases. In this work, we investigate how seniority impacts the degree of gender bias exhibited in pretrained neural generation models by introducing a novel framework for probing compound bias. We contribute a benchmark robustness-testing dataset spanning two domains, U.S. senatorship and professorship, created using a distant-supervision method. Our dataset includes human-written text with underlying ground truth and paired counterfactuals. We then examine GPT-2 perplexity and the frequency of gendered language in generated text. Our results show that GPT-2 amplifies bias by considering women as junior and men as senior more often than the ground truth in both domains. These results suggest that NLP applications built using GPT-2 may harm women in professional capacities.

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Combining ELECTRA and Adaptive Graph Encoding for Frame Identification
Fabio Tamburini

This paper presents contributions in two directions: first we propose a new system for Frame Identification (FI), based on pre-trained text encoders trained discriminatively and graphs embedding, producing state of the art performance and, second, we take in consideration all the extremely different procedures used to evaluate systems for this task performing a complete evaluation over two benchmarks and all possible splits and cleaning procedures used in the FI literature.

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Polysemy in Spoken Conversations and Written Texts
Aina Garí Soler | Matthieu Labeau | Chloé Clavel

Our discourses are full of potential lexical ambiguities, due in part to the pervasive use of words having multiple senses. Sometimes, one word may even be used in more than one sense throughout a text. But, to what extent is this true for different kinds of texts? Does the use of polysemous words change when a discourse involves two people, or when speakers have time to plan what to say? We investigate these questions by comparing the polysemy level of texts of different nature, with a focus on spontaneous spoken dialogs; unlike previous work which examines solely scripted, written, monolog-like data. We compare multiple metrics that presuppose different conceptualizations of text polysemy, i.e., they consider the observed or the potential number of senses of words, or their sense distribution in a discourse. We show that the polysemy level of texts varies greatly depending on the kind of text considered, with dialog and spoken discourses having generally a higher polysemy level than written monologs. Additionally, our results emphasize the need for relaxing the popular “one sense per discourse” hypothesis.

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Cross-Level Semantic Similarity for Serbian Newswire Texts
Vuk Batanović | Maja Miličević Petrović

Cross-Level Semantic Similarity (CLSS) is a measure of the level of semantic overlap between texts of different lengths. Although this problem was formulated almost a decade ago, research on it has been sparse, and limited exclusively to the English language. In this paper, we present the first CLSS dataset in another language, in the form of CLSS.news.sr – a corpus of 1000 phrase-sentence and 1000 sentence-paragraph newswire text pairs in Serbian, manually annotated with fine-grained semantic similarity scores using a 0–4 similarity scale. We describe the methodology of data collection and annotation, and compare the resulting corpus to its preexisting counterpart in English, SemEval CLSS, following up with a preliminary linguistic analysis of the newly created dataset. State-of-the-art pre-trained language models are then fine-tuned and evaluated on the CLSS task in Serbian using the produced data, and their settings and results are discussed. The CLSS.news.sr corpus and the guidelines used in its creation are made publicly available.

Semantic role labeling (SRL) represents the meaning of a sentence in the form of predicate-argument structures. Such shallow semantic analysis is helpful in a wide range of downstream NLP tasks and real-world applications. As treebanks enabled the development of powerful syntactic parsers, the accurate predicate-argument analysis demands training data in the form of propbanks. Unfortunately, most languages simply do not have corresponding propbanks due to the high cost required to construct such resources. To overcome such challenges, Universal Proposition Bank 1.0 (UP1.0) was released in 2017, with high-quality propbank data generated via a two-stage method exploiting monolingual SRL and multilingual parallel data. In this paper, we introduce Universal Proposition Bank 2.0 (UP2.0), with significant enhancements over UP1.0: (1) propbanks with higher quality by using a state-of-the-art monolingual SRL and improved auto-generation of annotations; (2) expanded language coverage (from 7 to 9 languages); (3) span annotation for the decoupling of syntactic analysis; and (4) Gold data for a subset of the languages. We also share our experimental results that confirm the significant quality improvements of the generated propbanks. In addition, we present a comprehensive experimental evaluation on how different implementation choices impact the quality of the resulting data. We release these resources to the research community and hope to encourage more research on cross-lingual SRL.

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The Copenhagen Corpus of Eye Tracking Recordings from Natural Reading of Danish Texts
Nora Hollenstein | Maria Barrett | Marina Björnsdóttir

Eye movement recordings from reading are one of the richest signals of human language processing. Corpora of eye movements during reading of contextualized running text is a way of making such records available for natural language processing purposes. Such corpora already exist in some languages. We present CopCo, the Copenhagen Corpus of eye tracking recordings from natural reading of Danish texts. It is the first eye tracking corpus of its kind for the Danish language. CopCo includes 1,832 sentences with 34,897 tokens of Danish text extracted from a collection of speech manuscripts. This first release of the corpus contains eye tracking data from 22 participants. It will be extended continuously with more participants and texts from other genres. We assess the data quality of the recorded eye movements and find that the extracted features are in line with related research. The dataset available here: https://osf.io/ud8s5/.

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The Brooklyn Multi-Interaction Corpus for Analyzing Variation in Entrainment Behavior
Andreas Weise | Matthew McNeill | Rivka Levitan

We present the Brooklyn Multi-Interaction Corpus (B-MIC), a collection of dyadic conversations designed to identify speaker traits and conversation contexts that cause variations in entrainment behavior. B-MIC pairs each participant with multiple partners for an object placement game and open-ended discussions, as well as with a Wizard of Oz for a baseline of their speech. In addition to fully transcribed recordings, it includes demographic information and four completed psychological questionnaires for each subject and turn annotations for perceived emotion and acoustic outliers. This enables the study of speakers’ entrainment behavior in different contexts and the sources of variation in this behavior. In this paper, we introduce B-MIC and describe our collection, annotation, and preprocessing methodologies. We report a preliminary study demonstrating varied entrainment behavior across different conversation types and discuss the rich potential for future work on the corpus.

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Pro-TEXT: an Annotated Corpus of Keystroke Logs
Aleksandra Miletic | Christophe Benzitoun | Georgeta Cislaru | Santiago Herrera-Yanez

Pro-TEXT is a corpus of keystroke logs written in French. Keystroke logs are recordings of the writing process executed through a keyboard, which keep track of all actions taken by the writer (character additions, deletions, substitutions). As such, the Pro-TEXT corpus offers new insights into text genesis and underlying cognitive processes from the production perspective. A subset of the corpus is linguistically annotated with parts of speech, lemmas and syntactic dependencies, making it suitable for the study of interactions between linguistic and behavioural aspects of the writing process. The full corpus contains 202K tokens, while the annotated portion is currently 30K tokens large. The annotated content is progressively being made available in a database-like CSV format and in CoNLL format, and the work on an HTML-based visualisation tool is currently under way. To the best of our knowledge, Pro-TEXT is the first corpus of its kind in French.

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Work Hard, Play Hard: Collecting Acceptability Annotations through a 3D Game
Federico Bonetti | Elisa Leonardelli | Daniela Trotta | Raffaele Guarasci | Sara Tonelli

Corpus-based studies on acceptability judgements have always stimulated the interest of researchers, both in theoretical and computational fields. Some approaches focused on spontaneous judgements collected through different types of tasks, others on data annotated through crowd-sourcing platforms, still others relied on expert annotated data available from the literature. The release of CoLA corpus, a large-scale corpus of sentences extracted from linguistic handbooks as examples of acceptable/non acceptable phenomena in English, has revived interest in the reliability of judgements of linguistic experts vs. non-experts. Several issues are still open. In this work, we contribute to this debate by presenting a 3D video game that was used to collect acceptability judgments on Italian sentences. We analyse the resulting annotations in terms of agreement among players and by comparing them with experts’ acceptability judgments. We also discuss different game settings to assess their impact on participants’ motivation and engagement. The final dataset containing 1,062 sentences, which were selected based on majority voting, is released for future research and comparisons.

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DiHuTra: a Parallel Corpus to Analyse Differences between Human Translations
Ekaterina Lapshinova-Koltunski | Maja Popović | Maarit Koponen

This paper describes a new corpus of human translations which contains both professional and students translations. The data consists of English sources – texts from news and reviews – and their translations into Russian and Croatian, as well as of the subcorpus containing translations of the review texts into Finnish. All target languages represent mid-resourced and less or mid-investigated ones. The corpus will be valuable for studying variation in translation as it allows a direct comparison between human translations of the same source texts. The corpus will also be a valuable resource for evaluating machine translation systems. We believe that this resource will facilitate understanding and improvement of the quality issues in both human and machine translation. In the paper, we describe how the data was collected, provide information on translator groups and summarise the differences between the human translations at hand based on our preliminary results with shallow features.

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Data Expansion Using WordNet-based Semantic Expansion and Word Disambiguation for Cyberbullying Detection
Md Saroar Jahan | Djamila Romaissa Beddiar | Mourad Oussalah | Muhidin Mohamed

Automatic identification of cyberbullying from textual content is known to be a challenging task. The challenges arise from the inherent structure of cyberbullying and the lack of labeled large-scale corpus, enabling efficient machine-learning-based tools including neural networks. This paper advocates a data augmentation-based approach that could enhance the automatic detection of cyberbullying in social media texts. We use both word sense disambiguation and synonymy relation in WordNet lexical database to generate coherent equivalent utterances of cyberbullying input data. The disambiguation and semantic expansion are intended to overcome the inherent limitations of social media posts, such as an abundance of unstructured constructs and limited semantic content. Besides, to test the feasibility, a novel protocol has been employed to collect cyberbullying traces data from AskFm forum, where about a 10K-size dataset has been manually labeled. Next, the problem of cyberbullying identification is viewed as a binary classification problem using an elaborated data augmentation strategy and an appropriate classifier. For the latter, a Convolutional Neural Network (CNN) architecture with FastText and BERT was put forward, whose results were compared against commonly employed Naïve Bayes (NB) and Logistic Regression (LR) classifiers with and without data augmentation. The research outcomes were promising and yielded almost 98.4% of classifier accuracy, an improvement of more than 4% over baseline results.

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ALIGNMEET: A Comprehensive Tool for Meeting Annotation, Alignment, and Evaluation
Peter Polák | Muskaan Singh | Anna Nedoluzhko | Ondřej Bojar

Summarization is a challenging problem, and even more challenging is to manually create, correct, and evaluate the summaries. The severity of the problem grows when the inputs are multi-party dialogues in a meeting setup. To facilitate the research in this area, we present ALIGNMEET, a comprehensive tool for meeting annotation, alignment, and evaluation. The tool aims to provide an efficient and clear interface for fast annotation while mitigating the risk of introducing errors. Moreover, we add an evaluation mode that enables a comprehensive quality evaluation of meeting minutes. To the best of our knowledge, there is no such tool available. We release the tool as open source. It is also directly installable from PyPI.

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KSoF: The Kassel State of Fluency Dataset – A Therapy Centered Dataset of Stuttering
Sebastian Bayerl | Alexander Wolff von Gudenberg | Florian Hönig | Elmar Noeth | Korbinian Riedhammer

Stuttering is a complex speech disorder that negatively affects an individual’s ability to communicate effectively. Persons who stutter (PWS) often suffer considerably under the condition and seek help through therapy. Fluency shaping is a therapy approach where PWSs learn to modify their speech to help them to overcome their stutter. Mastering such speech techniques takes time and practice, even after therapy. Shortly after therapy, success is evaluated highly, but relapse rates are high. To be able to monitor speech behavior over a long time, the ability to detect stuttering events and modifications in speech could help PWSs and speech pathologists to track the level of fluency. Monitoring could create the ability to intervene early by detecting lapses in fluency. To the best of our knowledge, no public dataset is available that contains speech from people who underwent stuttering therapy that changed the style of speaking. This work introduces the Kassel State of Fluency (KSoF), a therapy-based dataset containing over 5500 clips of PWSs. The clips were labeled with six stuttering-related event types: blocks, prolongations, sound repetitions, word repetitions, interjections, and – specific to therapy – speech modifications. The audio was recorded during therapy sessions at the Institut der Kasseler Stottertherapie. The data will be made available for research purposes upon request.

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EZCAT: an Easy Conversation Annotation Tool
Gaël Guibon | Luce Lefeuvre | Matthieu Labeau | Chloé Clavel

Users generate content constantly, leading to new data requiring annotation. Among this data, textual conversations are created every day and come with some specificities: they are mostly private through instant messaging applications, requiring the conversational context to be labeled. These specificities led to several annotation tools dedicated to conversation, and mostly dedicated to dialogue tasks, requiring complex annotation schemata, not always customizable and not taking into account conversation-level labels. In this paper, we present EZCAT, an easy-to-use interface to annotate conversations in a two-level configurable schema, leveraging message-level labels and conversation-level labels. Our interface is characterized by the voluntary absence of a server and accounts management, enhancing its availability to anyone, and the control over data, which is crucial to confidential conversations. We also present our first usage of EZCAT along with our annotation schema we used to annotate confidential customer service conversations. EZCAT is freely available at https://gguibon.github.io/ezcat.

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Spoken Language Treebanks in Universal Dependencies: an Overview
Kaja Dobrovoljc

Given the benefits of syntactically annotated collections of transcribed speech in spoken language research and applications, many spoken language treebanks have been developed in the last decades, with divergent annotation schemes posing important limitations to cross-resource explorations, such as comparing data across languages, grammatical frameworks, and language domains. As a consequence, there has been a growing number of spoken language treebanks adopting the Universal Dependencies (UD) annotation scheme, aimed at cross-linguistically consistent morphosyntactic annotation. In view of the non-central role of spoken language data within the scheme and with little in-domain consolidation to date, this paper presents a comparative overview of spoken language treebanks in UD to support cross-treebank data explorations on the one hand, and encourage further treebank harmonization on the other. Our results show that the spoken language treebanks differ considerably with respect to the inventory and the format of transcribed phenomena, as well as the principles adopted in their morphosyntactic annotation. This is particularly true for the dependency annotation of speech disfluencies, where conflicting data annotations suggest an underspecification of the guidelines pertaining to speech repairs in general and the reparandum dependency relation in particular.

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LeConTra: A Learner Corpus of English-to-Dutch News Translation
Bram Vanroy | Lieve Macken

We present LeConTra, a learner corpus consisting of English-to-Dutch news translations enriched with translation process data. Three students of a Master’s programme in Translation were asked to translate 50 different English journalistic texts of approximately 250 tokens each. Because we also collected translation process data in the form of keystroke logging, our dataset can be used as part of different research strands such as translation process research, learner corpus research, and corpus-based translation studies. Reference translations, without process data, are also included. The data has been manually segmented and tokenized, and manually aligned at both segment and word level, leading to a high-quality corpus with token-level process data. The data is freely accessible via the Translation Process Research DataBase, which emphasises our commitment of distributing our dataset. The tool that was built for manual sentence segmentation and tokenization, Mantis, is also available as an open-source aid for data processing.

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Annotating Attribution in Czech News Server Articles
Barbora Hladka | Jiří Mírovský | Matyáš Kopp | Václav Moravec

This paper focuses on detection of sources in the Czech articles published on a news server of Czech public radio. In particular, we search for attribution in sentences and we recognize attributed sources and their sentence context (signals). We organized a crowdsourcing annotation task that resulted in a data set of 2,167 stories with manually recognized signals and sources. In addition, the sources were classified into the classes of named and unnamed sources.

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Xposition: An Online Multilingual Database of Adpositional Semantics
Luke Gessler | Austin Blodgett | Joseph C. Ledford | Nathan Schneider

We present Xposition, an online platform for documenting adpositional semantics across languages in terms of supersenses (Schneider et al., 2018). More than just a lexical database, Xposition houses annotation guidelines, structured lexicographic documentation, and annotated corpora. Guidelines and documentation are stored as wiki pages for ease of editing, and described elements (supersenses, adpositions, etc.) are hyperlinked for ease of browsing. We describe how the platform structures information; its current contents across several languages; and aspects of the design of the web application that supports it, with special attention to how it supports datasets and standards that evolve over time.

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A Study in Contradiction: Data and Annotation for AIDA Focusing on Informational Conflict in Russia-Ukraine Relations
Jennifer Tracey | Ann Bies | Jeremy Getman | Kira Griffitt | Stephanie Strassel

This paper describes data resources created for Phase 1 of the DARPA Active Interpretation of Disparate Alternatives (AIDA) program, which aims to develop language technology that can help humans manage large volumes of sometimes conflicting information to develop a comprehensive understanding of events around the world, even when such events are described in multiple media and languages. Especially important is the need for the technology to be capable of building multiple hypotheses to account for alternative interpretations of data imbued with informational conflict. The corpus described here is designed to support these goals. It focuses on the domain of Russia-Ukraine relations and contains multimedia source data in English, Russian and Ukrainian, annotated to support development and evaluation of systems that perform extraction of entities, events, and relations from individual multimedia documents, aggregate the information across documents and languages, and produce multiple “hypotheses” about what has happened. This paper describes source data collection, annotation, and assessment.

This paper describes our efforts to extend the PARSEME framework to Modern Standard Arabic. Theapplicability of the PARSEME guidelines was tested by measuring the inter-annotator agreement in theearly annotation stage. A subset of 1,062 sentences from the Prague Arabic Dependency Treebank PADTwas selected and annotated by two Arabic native speakers independently. Following their annotations, anew Arabic corpus with over 1,250 annotated VMWEs has been built. This corpus already exceeds thesmallest corpora of the PARSEME suite, and enables first observations. We discuss our annotation guide-line schema that shows full MWE annotation is realizable in Arabic where we get good inter-annotator agreement.

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Annotation of Communicative Functions of Short Feedback Tokens in Switchboard
Carol Figueroa | Adaeze Adigwe | Magalie Ochs | Gabriel Skantze

There has been a lot of work on predicting the timing of feedback in conversational systems. However, there has been less focus on predicting the prosody and lexical form of feedback given their communicative function. Therefore, in this paper we present our preliminary annotations of the communicative functions of 1627 short feedback tokens from the Switchboard corpus and an analysis of their lexical realizations and prosodic characteristics. Since there is no standard scheme for annotating the communicative function of feedback we propose our own annotation scheme. Although our work is ongoing, our preliminary analysis revealed lexical tokens such as “yeah” are ambiguous and therefore lexical forms alone are not indicative of the function. Both the lexical form and prosodic characteristics need to be taken into account in order to predict the communicative function. We also found that feedback functions have distinguishable prosodic characteristics in terms of duration, mean pitch, pitch slope, and pitch range.

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A Dataset of Offensive Language in Kosovo Social Media
Adem Ajvazi | Christian Hardmeier

Social media are a central part of people’s lives. Unfortunately, many public social media spaces are rife with bullying and offensive language, creating an unsafe environment for their users. In this paper, we present a new dataset for offensive language detection in Albanian. The dataset is composed of user-generated comments on Facebook and YouTube from the channels of selected Kosovo news platforms. It is annotated according to the three levels of the OLID annotation scheme. We also show results of a baseline system for offensive language classification based on a fine-tuned BERT model and compare with the Danish DKhate dataset, which is similar in scope and size. In a transfer learning setting, we find that merging the Albanian and Danish training sets leads to improved performance for prediction on Danish, but not Albanian, on both offensive language recognition and distinguishing targeted and untargeted offence.

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The Arabic Parallel Gender Corpus 2.0: Extensions and Analyses
Bashar Alhafni | Nizar Habash | Houda Bouamor

Gender bias in natural language processing (NLP) applications, particularly machine translation, has been receiving increasing attention. Much of the research on this issue has focused on mitigating gender bias in English NLP models and systems. Addressing the problem in poorly resourced, and/or morphologically rich languages has lagged behind, largely due to the lack of datasets and resources. In this paper, we introduce a new corpus for gender identification and rewriting in contexts involving one or two target users (I and/or You) – first and second grammatical persons with independent grammatical gender preferences. We focus on Arabic, a gender-marking morphologically rich language. The corpus has multiple parallel components: four combinations of 1st and 2nd person in feminine and masculine grammatical genders, as well as English, and English to Arabic machine translation output. This corpus expands on Habash et al. (2019)’s Arabic Parallel Gender Corpus (APGC v1.0) by adding second person targets as well as increasing the total number of sentences over 6.5 times, reaching over 590K words. Our new dataset will aid the research and development of gender identification, controlled text generation, and post-editing rewrite systems that could be used to personalize NLP applications and provide users with the correct outputs based on their grammatical gender preferences. We make the Arabic Parallel Gender Corpus (APGC v2.0) publicly available

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The Engage Corpus: A Social Media Dataset for Text-Based Recommender Systems
Daniel Cheng | Kyle Yan | Phillip Keung | Noah A. Smith

Social media platforms play an increasingly important role as forums for public discourse. Many platforms use recommendation algorithms that funnel users to online groups with the goal of maximizing user engagement, which many commentators have pointed to as a source of polarization and misinformation. Understanding the role of NLP in recommender systems is an interesting research area, given the role that social media has played in world events. However, there are few standardized resources which researchers can use to build models that predict engagement with online groups on social media; each research group constructs datasets from scratch without releasing their version for reuse. In this work, we present a dataset drawn from posts and comments on the online message board Reddit. We develop baseline models for recommending subreddits to users, given the user’s post and comment history. We also study the behavior of our recommender models on subreddits that were banned in June 2020 as part of Reddit’s efforts to stop the dissemination of hate speech.

Interest in argument mining has resulted in an increasing number of argument annotated corpora. However, most focus on English texts with explicit argumentative discourse markers, such as persuasive essays or legal documents. Conversely, we report on the first extensive and consolidated Portuguese argument annotation project focused on opinion articles. We briefly describe the annotation guidelines based on a multi-layered process and analyze the manual annotations produced, highlighting the main challenges of this textual genre. We then conduct a comprehensive inter-annotator agreement analysis, including argumentative discourse units, their classes and relations, and resulting graphs. This analysis reveals that each of these aspects tackles very different kinds of challenges. We observe differences in annotator profiles, motivating our aim of producing a non-aggregated corpus containing the insights of every annotator. We note that the interpretation and identification of token-level arguments is challenging; nevertheless, tasks that focus on higher-level components of the argument structure can obtain considerable agreement. We lay down perspectives on corpus usage, exploiting its multi-faceted nature.

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German Parliamentary Corpus (GerParCor)
Giuseppe Abrami | Mevlüt Bagci | Leon Hammerla | Alexander Mehler

Parliamentary debates represent a large and partly unexploited treasure trove of publicly accessible texts. In the German-speaking area, there is a certain deficit of uniformly accessible and annotated corpora covering all German-speaking parliaments at the national and federal level. To address this gap, we introduce the German Parliamentary Corpus (GerParCor). GerParCor is a genre-specific corpus of (predominantly historical) German-language parliamentary protocols from three centuries and four countries, including state and federal level data. In addition, GerParCor contains conversions of scanned protocols and, in particular, of protocols in Fraktur converted via an OCR process based on Tesseract. All protocols were preprocessed by means of the NLP pipeline of spaCy3 and automatically annotated with metadata regarding their session date. GerParCor is made available in the XMI format of the UIMA project. In this way, GerParCor can be used as a large corpus of historical texts in the field of political communication for various tasks in NLP.

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NerKor+Cars-OntoNotes++
Attila Novák | Borbála Novák

In this paper, we present an upgraded version of the Hungarian NYTK-NerKor named entity corpus, which contains about twice as many annotated spans and 7 times as many distinct entity types as the original version. We used an extended version of the OntoNotes 5 annotation scheme including time and numerical expressions. NerKor is the newest and biggest NER corpus for Hungarian containing diverse domains. We applied cross-lingual transfer of NER models trained for other languages based on multilingual contextual language models to preannotate the corpus. We corrected the annotation semi-automatically and manually. Zero-shot preannotation was very effective with about 0.82 F1 score for the best model. We also added a 12000-token subcorpus on cars and other motor vehicles. We trained and release a transformer-based NER tagger for Hungarian using the annotation in the new corpus version, which provides similar performance to an identical model trained on the original version of the corpus.

Since several decades emotional databases have been recorded by various laboratories. Many of them contain acted portrays of Darwin’s famous “big four” basic emotions. In this paper, we investigate in how far a selection of them are comparable by two approaches: on the one hand modeling similarity as performance in cross database machine learning experiments and on the other by analyzing a manually picked set of four acoustic features that represent different phonetic areas. It is interesting to see in how far specific databases (we added a synthetic one) perform well as a training set for others while some do not. Generally speaking, we found indications for both similarity as well as specificiality across languages.

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Nkululeko: A Tool For Rapid Speaker Characteristics Detection
Felix Burkhardt | Johannes Wagner | Hagen Wierstorf | Florian Eyben | Björn Schuller

We present advancements with a software tool called Nkululeko, that lets users perform (semi-) supervised machine learning experiments in the speaker characteristics domain. It is based on audformat, a format for speech database metadata description. Due to an interface based on configurable templates, it supports best practise and very fast setup of experiments without the need to be proficient in the underlying language: Python. The paper explains the handling of Nkululeko and presents two typical experiments: comparing the expert acoustic features with artificial neural net embeddings for emotion classification and speaker age regression.

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Speech Aerodynamics Database, Tools and Visualisation
Shi Yu | Clara Ponchard | Roland Trouville | Sergio Hassid | Didier Demolin

Aerodynamic processes underlie the characteristics of the acoustic signal of speech sounds. The aerodynamics of speech give insights on acoustic outcome and help explain the mechanisms of speech production. This database was designed during an ARC project ”Dynamique des systèmes phonologiques” in which the study of aerodynamic constraints on speech production was an important target. Data were recorded between 1996 and 1999 at the Erasmus Hospital (Hôpital Erasme) of Université Libre de Bruxelles, Belgium and constitute one of the few datasets available on direct measurement of subglottal pressure and other aerodynamic parameters. The goal was to obtain a substantial amount of data with simultaneous recording, in various context, of the speech acoustic signal, subglottal pressure (Ps), intraoral pressure (Po), oral airflow (Qo) and nasal airflow (Qn). This database contains recordings of 2 English, 1 Amharic, and 7 French speakers and is provided with data conversion and visualisation tools. Another aim of this project was to obtain some reference values of the aerodynamics of speech production for female and male speakers uttering different types of segments and sentences in French.

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PATATRA and PATAFreq: two French databases for the documentation of within-speaker variability in speech
Cécile Fougeron | Nicolas Audibert | Cedric Gendrot | Estelle Chardenon | Louise Wohmann

Our knowledge on speech is historically built on data comparing different speakers or data averaged across speakers. Consequently, little is known on the variability in the speech of a single individual. Experimental studies have shown that speakers adapt to the linguistic and the speaking contexts, and modify their speech according to their emotional or biological condition, etc. However, it is unclear how much speakers vary from one repetition to the next, and how comparable are recordings that are collected days, months or years apart. In this paper, we introduce two French databases which contain recordings of 9 to 11 speakers recorded over 9 to 18 sessions, allowing comparisons of speech tasks with a different delay between the repetitions: 3 repetitions within the same session, 6 to 10 repetitions on different days during a two months period, 5 to 9 repetitions on different years. Speakers are recorded on a large set of speech tasks including read and spontaneous speech as well as speech-like performance tasks. In this paper, we provide detailed descriptions of the two databases and available annotations. We conclude by an illustration on how these data can inform on within-speaker variability of speech.

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The Makerere Radio Speech Corpus: A Luganda Radio Corpus for Automatic Speech Recognition
Jonathan Mukiibi | Andrew Katumba | Joyce Nakatumba-Nabende | Ali Hussein | Joshua Meyer

Building a usable radio monitoring automatic speech recognition (ASR) system is a challenging task for under-resourced languages and yet this is paramount in societies where radio is the main medium of public communication and discussions. Initial efforts by the United Nations in Uganda have proved how understanding the perceptions of rural people who are excluded from social media is important in national planning. However, these efforts are being challenged by the absence of transcribed speech datasets. In this paper, The Makerere Artificial Intelligence research lab releases a Luganda radio speech corpus of 155 hours. To our knowledge, this is the first publicly available radio dataset in sub-Saharan Africa. The paper describes the development of the voice corpus and presents baseline Luganda ASR performance results using Coqui STT toolkit, an open-source speech recognition toolkit.

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Far-Field Speaker Recognition Benchmark Derived From The DiPCo Corpus
Mickael Rouvier | Mohammad Mohammadamini

In this paper, we present a far-field speaker verification benchmark derived from the publicly-available DiPCo corpus. This corpus comprise three different tasks that involve enrollment and test conditions with single- and/or multi-channels recordings. The main goal of this corpus is to foster research in far-field and multi-channel text-independent speaker verification. Also, it can be used for other speaker recognition tasks such as dereverberation, denoising and speech enhancement. In addition, we release a Kaldi and SpeechBrain system to facilitate further research. And we validate the evaluation design with a single-microphone state-of-the-art speaker recognition system (i.e. ResNet-101). The results show that the proposed tasks are very challenging. And we hope these resources will inspire the speech community to develop new methods and systems for this challenging domain.

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Evaluating Sampling-based Filler Insertion with Spontaneous TTS
Siyang Wang | Joakim Gustafson | Éva Székely

Inserting fillers (such as “um”, “like”) to clean speech text has a rich history of study. One major application is to make dialogue systems sound more spontaneous. The ambiguity of filler occurrence and inter-speaker difference make both modeling and evaluation difficult. In this paper, we study sampling-based filler insertion, a simple yet unexplored approach to inserting fillers. We propose an objective score called Filler Perplexity (FPP). We build three models trained on two single-speaker spontaneous corpora, and evaluate them with FPP and perceptual tests. We implement two innovations in perceptual tests, (1) evaluating filler insertion on dialogue systems output, (2) synthesizing speech with neural spontaneous TTS engines. FPP proves to be useful in analysis but does not correlate well with perceptual MOS. Perceptual results show little difference between compared filler insertion models including with ground-truth, which may be due to the ambiguity of what is good filler insertion and a strong neural spontaneous TTS that produces natural speech irrespective of input. Results also show preference for filler-inserted speech synthesized with spontaneous TTS. The same test using TTS based on read speech obtains the opposite results, which shows the importance of using spontaneous TTS in evaluating filler insertions. Audio samples: www.speech.kth.se/tts-demos/LREC22

Hungarian is spoken by 15 million people, still, easily accessible Automatic Speech Recognition (ASR) benchmark datasets – especially for spontaneous speech – have been practically unavailable. In this paper, we introduce BEA-Base, a subset of the BEA spoken Hungarian database comprising mostly spontaneous speech of 140 speakers. It is built specifically to assess ASR, primarily for conversational AI applications. After defining the speech recognition subsets and task, several baselines – including classic HMM-DNN hybrid and end-to-end approaches augmented by cross-language transfer learning – are developed using open-source toolkits. The best results obtained are based on multilingual self-supervised pretraining, achieving a 45% recognition error rate reduction as compared to the classical approach – without the application of an external language model or additional supervised data. The results show the feasibility of using BEA-Base for training and evaluation of Hungarian speech recognition systems.

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SNuC: The Sheffield Numbers Spoken Language Corpus
Emma Barker | Jon Barker | Robert Gaizauskas | Ning Ma | Monica Lestari Paramita

We present SNuC, the first published corpus of spoken alphanumeric identifiers of the sort typically used as serial and part numbers in the manufacturing sector. The dataset contains recordings and transcriptions of over 50 native British English speakers, speaking over 13,000 multi-character alphanumeric sequences and totalling almost 20 hours of recorded speech. We describe requirements taken into account in the designing the corpus and the methodology used to construct it. We present summary statistics describing the corpus contents, as well as a preliminary investigation into errors in spoken alphanumeric identifiers. We validate the corpus by showing how it can be used to adapt a deep learning neural network based ASR system, resulting in improved recognition accuracy on the task of spoken alphanumeric identifier recognition. Finally, we discuss further potential uses for the corpus and for the tools developed to construct it.

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The ManDi Corpus: A Spoken Corpus of Mandarin Regional Dialects
Liang Zhao | Eleanor Chodroff

In the present paper, we introduce the ManDi Corpus, a spoken corpus of regional Mandarin dialects and Standard Mandarin. The corpus currently contains 357 recordings (about 9.6 hours) of monosyllabic words, disyllabic words, short sentences, a short passage and a poem, each produced in Standard Mandarin and in one of six regional Mandarin dialects: Beijing, Chengdu, Jinan, Taiyuan, Wuhan, and Xi’an Mandarin from 36 speakers. The corpus was collected remotely using participant-controlled smartphone recording apps. Word- and phone-level alignments were generated using Praat and the Montreal Forced Aligner. The pilot study of dialect-specific tone systems showed that with practicable design and decent recording quality, remotely collected speech data can be suitable for analysis of relative patterns in acoustic-phonetic realization. The corpus is available on OSF (https://osf.io/fgv4w/) for non-commercial use under a CC BY-NC 3.0 license.

Conversations (normal speech) or professional interactions (e.g., projected speech in the classroom) have been identified as situations with increased risk of exposure to SARS-CoV-2 due to the high production of droplets in the exhaled air. However, it is still unclear to what extent speech properties influence droplets emission during everyday life conversations. Here, we report the experimental protocol of three experiments aiming at measuring the velocity and the direction of the airflow, the number and size of droplets spread during speech interactions in French. We consider different phonetic conditions, potentially leading to a modulation of speech droplets production, such as voice intensity (normal vs. loud voice), articulation manner of phonemes (type of consonants and vowels) and prosody (i.e., the melody of the speech). Findings from these experiments will allow future simulation studies to predict the transport, dispersion and evaporation of droplets emitted under different speech conditions.

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Towards Speech-only Opinion-level Sentiment Analysis
Annalena Aicher | Alisa Gazizullina | Aleksei Gusev | Yuri Matveev | Wolfgang Minker

The growing popularity of various forms of Spoken Dialogue Systems (SDS) raises the demand for their capability of implicitly assessing the speaker’s sentiment from speech only. Mapping the latter on user preferences enables to adapt to the user and individualize the requested information while increasing user satisfaction. In this paper, we explore the integration of rank consistent ordinal regression into a speech-only sentiment prediction task performed by ResNet-like systems. Furthermore, we use speaker verification extractors trained on larger datasets as low-level feature extractors. An improvement of performance is shown by fusing sentiment and pre-extracted speaker embeddings reducing the speaker bias of sentiment predictions. Numerous experiments on Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) databases show that we beat the baselines of state-of-the-art unimodal approaches. Using speech as the only modality combined with optimizing an order-sensitive objective function gets significantly closer to the sentiment analysis results of state-of-the-art multimodal systems.

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At the Intersection of NLP and Sustainable Development: Exploring the Impact of Demographic-Aware Text Representations in Modeling Value on a Corpus of Interviews
Goya van Boven | Stephanie Hirmer | Costanza Conforti

This research explores automated text classification using data from Low– and Middle–Income Countries (LMICs). In particular, we explore enhancing text representations with demographic information of speakers in a privacy-preserving manner. We introduce the Demographic-Rich Qualitative UPV-Interviews Dataset (DR-QI), a rich dataset of qualitative interviews from rural communities in India and Uganda. The interviews were conducted following the latest standards for respectful interactions with illiterate speakers (Hirmer et al., 2021a). The interviews were later sentence-annotated for Automated User-Perceived Value (UPV) Classification (Conforti et al., 2020), a schema that classifies values expressed by speakers, resulting in a dataset of 5,333 sentences. We perform the UPV classification task, which consists of predicting which values are expressed in a given sentence, on the new DR-QI dataset. We implement a classification model using DistilBERT (Sanh et al., 2019), which we extend with demographic information. In order to preserve the privacy of speakers, we investigate encoding demographic information using autoencoders. We find that adding demographic information improves performance, even if such information is encoded. In addition, we find that the performance per UPV is linked to the number of occurrences of that value in our data.

For research in audiovisual interview archives often it is not only of interest what is said but also how. Sentiment analysis and emotion recognition can help capture, categorize and make these different facets searchable. In particular, for oral history archives, such indexing technologies can be of great interest. These technologies can help understand the role of emotions in historical remembering. However, humans often perceive sentiments and emotions ambiguously and subjectively. Moreover, oral history interviews have multi-layered levels of complex, sometimes contradictory, sometimes very subtle facets of emotions. Therefore, the question arises of the chance machines and humans have capturing and assigning these into predefined categories. This paper investigates the ambiguity in human perception of emotions and sentiment in German oral history interviews and the impact on machine learning systems. Our experiments reveal substantial differences in human perception for different emotions. Furthermore, we report from ongoing machine learning experiments with different modalities. We show that the human perceptual ambiguity and other challenges, such as class imbalance and lack of training data, currently limit the opportunities of these technologies for oral history archives. Nonetheless, our work uncovers promising observations and possibilities for further research.

Finding the polarity of feelings in texts is a far-reaching task. Whilst the field of natural language processing has established sentiment analysis as an alluring problem, many feelings are left uncharted. In this study, we analyze the optimism and pessimism concepts from Twitter posts to effectively understand the broader dimension of psychological phenomenon. Towards this, we carried a systematic study by first exploring the linguistic peculiarities of optimism and pessimism in user-generated content. Later, we devised a multi-task knowledge distillation framework to simultaneously learn the target task of optimism detection with the help of the auxiliary task of sentiment analysis and hate speech detection. We evaluated the performance of our proposed approach on the benchmark Optimism/Pessimism Twitter dataset. Our extensive experiments show the superior- ity of our approach in correctly differentiating between optimistic and pessimistic users. Our human and automatic evaluation shows that sentiment analysis and hate speech detection are beneficial for optimism/pessimism detection.

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Dataset and Baseline for Automatic Student Feedback Analysis
Missaka Herath | Kushan Chamindu | Hashan Maduwantha | Surangika Ranathunga

In this paper, we present a student feedback corpus, which contains 3000 instances of feedback written by university students. This dataset has been annotated for aspect terms, opinion terms, polarities of the opinion terms towards targeted aspects, document-level opinion polarities and sentence separations. We develop a hierarchical taxonomy for aspect categorization, which covers all the areas of the teaching-learning process. We annotated both implicit and explicit aspects using this taxonomy. Annotation methodology, difficulties faced during the annotation, and the details about the aspect term categorization have been discussed in detail. This annotated corpus can be used for Aspect Extraction, Aspect Level Sentiment Analysis, and Document Level Sentiment Analysis. Also the baseline results for all three tasks are given in the paper.

Motivated by the sparsity of NLP resources for Eastern European languages, we present a broad index of existing Eastern European language resources (90+ datasets and 45+ models) published as a github repository open for updates from the community. Furthermore, to support the evaluation of commonsense reasoning tasks, we provide hand-crafted cross-lingual datasets for five different semantic tasks (namely news categorization, paraphrase detection, Natural Language Inference (NLI) task, tweet sentiment detection, and news sentiment detection) for some of the Eastern European languages. We perform several experiments with the existing multilingual models on these datasets to define the performance baselines and compare them to the existing results for other languages.

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Slovene SuperGLUE Benchmark: Translation and Evaluation
Aleš Žagar | Marko Robnik-Šikonja

We present SuperGLUE benchmark adapted and translated into Slovene using a combination of human and machine translation. We describe the translation process and problems arising due to differences in morphology and grammar. We evaluate the translated datasets in several modes: monolingual, cross-lingual, and multilingual, taking into account differences between machine and human translated training sets. The results show that the monolingual Slovene SloBERTa model is superior to massively multilingual and trilingual BERT models, but these also show a good cross-lingual performance on certain tasks. The performance of Slovene models still lags behind the best English models.

In this paper we present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger. These two datasets were made available for the IWSLT 2022 low-resource speech translation track, and they consist of collections of radio recordings from daily broadcast news in Niger (Studio Kalangou) and Mali (Studio Tamani). We share (i) a massive amount of unlabeled audio data (671 hours) in five languages: French from Niger, Fulfulde, Hausa, Tamasheq and Zarma, and (ii) a smaller 17 hours parallel corpus of audio recordings in Tamasheq, with utterance-level translations in the French language. All this data is shared under the Creative Commons BY-NC-ND 3.0 license. We hope these resources will inspire the speech community to develop and benchmark models using the Tamasheq language.

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Aesop’s fable “The North Wind and the Sun” Used as a Rosetta Stone to Extract and Map Spoken Words in Under-resourced Languages
Elena Knyazeva | Philippe Boula de Mareüil | Frédéric Vernier

This paper describes a method of semi-automatic word spotting in minority languages, from one and the same Aesop fable “The North Wind and the Sun” translated in Romance languages/dialects from Hexagonal (i.e. Metropolitan) France and languages from French Polynesia. The first task consisted of finding out how a dozen words such as “wind” and “sun” were translated in over 200 versions collected in the field — taking advantage of orthographic similarity, word position and context. Occurrences of the translations were then extracted from the phone-aligned recordings. The results were judged accurate in 96–97% of cases, both on the development corpus and a test set of unseen data. Corrected alignments were then mapped and basemaps were drawn to make various linguistic phenomena immediately visible. The paper exemplifies how regular expressions may be used for this purpose. The final result, which takes the form of an online speaking atlas (enriching the https://atlas.limsi.fr website), enables us to illustrate lexical, morphological or phonetic variation.

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Multilingual Open Text Release 1: Public Domain News in 44 Languages
Chester Palen-Michel | June Kim | Constantine Lignos

We present a Multilingual Open Text (MOT), a new multilingual corpus containing text in 44 languages, many of which have limited existing text resources for natural language processing. The first release of the corpus contains over 2.8 million news articles and an additional 1 million short snippets (photo captions, video descriptions, etc.) published between 2001–2022 and collected from Voice of America’s news websites. We describe our process for collecting, filtering, and processing the data. The source material is in the public domain, our collection is licensed using a creative commons license (CC BY 4.0), and all software used to create the corpus is released under the MIT License. The corpus will be regularly updated as additional documents are published.

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TweetTaglish: A Dataset for Investigating Tagalog-English Code-Switching
Megan Herrera | Ankit Aich | Natalie Parde

Deploying recent natural language processing innovations to low-resource settings allows for state-of-the-art research findings and applications to be accessed across cultural and linguistic borders. One low-resource setting of increasing interest is code-switching, the phenomenon of combining, swapping, or alternating the use of two or more languages in continuous dialogue. In this paper, we introduce a large dataset (20k+ instances) to facilitate investigation of Tagalog-English code-switching, which has become a popular mode of discourse in Philippine culture. Tagalog is an Austronesian language and former official language of the Philippines spoken by over 23 million people worldwide, but it and Tagalog-English are under-represented in NLP research and practice. We describe our methods for data collection, as well as our labeling procedures. We analyze our resulting dataset, and finally conclude by providing results from a proof-of-concept regression task to establish dataset validity, achieving a strong performance benchmark (R2=0.797-0.909; RMSE=0.068-0.057).

This work presents a parallel corpus of Guarani-Spanish text aligned at sentence level. The corpus contains about 30,000 sentence pairs, and is structured as a collection of subsets from different sources, further split into training, development and test sets. A sample of sentences from the test set was manually annotated by native speakers in order to incorporate meta-linguistic annotations about the Guarani dialects present in the corpus and also the correctness of the alignment and translation. We also present some baseline MT experiments and analyze the results in terms of the subsets. We hope this corpus can be used as a benchmark for testing Guarani-Spanish MT systems, and aim to expand and improve the quality of the corpus in future iterations.

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Assessing Multilinguality of Publicly Accessible Websites
Rinalds Vīksna | Inguna Skadiņa | Raivis Skadiņš | Andrejs Vasiļjevs | Roberts Rozis

Although information on the Internet can be shared in many languages, the language presence on the World Wide Web is very disproportionate. The problem of multilingualism on the Web, in particular access, availability and quality of information in the world’s languages, has been the subject of UNESCO focus for several decades. Making European websites more multilingual is also one of the focal targets of the Connecting Europe Facility Automated Translation (CEF AT) digital service infrastructure. In order to monitor this goal, alongside other possible solutions, CEF AT needs a methodology and easy to use tool to assess the degree of multilingualism of a given website. In this paper we investigate methods and tools that automatically analyse the language diversity of the Web and propose indicators and methodology on how to measure the multilingualism of European websites. We also introduce a prototype tool based on open-source software that helps to assess multilingualism of the Web and can be independently run at set intervals. We also present initial results obtained with our tool that allows us to conclude that multilingualism on the Web is still a problem not only at the world level, but also at the European and regional level.

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A Methodology for Building a Diachronic Dataset of Semantic Shifts and its Application to QC-FR-Diac-V1.0, a Free Reference for French
David Kletz | Philippe Langlais | François Lareau | Patrick Drouin

Different algorithms have been proposed to detect semantic shifts (changes in a word meaning over time) in a diachronic corpus. Yet, and somehow surprisingly, no reference corpus has been designed so far to evaluate them, leaving researchers to fallback to troublesome evaluation strategies. In this work, we introduce a methodology for the construction of a reference dataset for the evaluation of semantic shift detection, that is, a list of words where we know for sure whether they present a word meaning change over a period of interest. We leverage a state-of-the-art word-sense disambiguation model to associate a date of first appearance to all the senses of a word. Significant changes in sense distributions as well as clear stability are detected and the resulting words are inspected by experts using a dedicated interface before populating a reference dataset. As a proof of concept, we apply this methodology to a corpus of newspapers from Quebec covering the whole 20th century. We manually verified a subset of candidates, leading to QC-FR-Diac-V1.0, a corpus of 151 words allowing one to evaluate the identification of semantic shifts in French between 1910 and 1990.

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CRASS: A Novel Data Set and Benchmark to Test Counterfactual Reasoning of Large Language Models
Jörg Frohberg | Frank Binder

We introduce the CRASS (counterfactual reasoning assessment) data set and benchmark utilizing questionized counterfactual conditionals as a novel and powerful tool to evaluate large language models. We present the data set design and benchmark. We test six state-of-the-art models against our benchmark. Our results show that it poses a valid challenge for these models and opens up considerable room for their improvement.

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Evaluating Gender Bias in Speech Translation
Marta R. Costa-jussà | Christine Basta | Gerard I. Gállego

The scientific community is increasingly aware of the necessity to embrace pluralism and consistently represent major and minor social groups. Currently, there are no standard evaluation techniques for different types of biases. Accordingly, there is an urgent need to provide evaluation sets and protocols to measure existing biases in our automatic systems. Evaluating the biases should be an essential step towards mitigating them in the systems. This paper introduces WinoST, a new freely available challenge set for evaluating gender bias in speech translation. WinoST is the speech version of WinoMT, an MT challenge set, and both follow an evaluation protocol to measure gender accuracy. Using an S-Transformer end-to-end speech translation system, we report the gender bias evaluation on four language pairs, and we reveal the inaccuracies in translations generating gender-stereotyped translations.

Obtaining linguistic annotation from novice crowdworkers is far from trivial. A case in point is the annotation of discourse relations, which is a complicated task. Recent methods have obtained promising results by extracting relation labels from either discourse connectives (DCs) or question-answer (QA) pairs that participants provide. The current contribution studies the effect of worker selection and training on the agreement on implicit relation labels between workers and gold labels, for both the DC and the QA method. In Study 1, workers were not specifically selected or trained, and the results show that there is much room for improvement. Study 2 shows that a combination of selection and training does lead to improved results, but the method is cost- and time-intensive. Study 3 shows that a selection-only approach is a viable alternative; it results in annotations of comparable quality compared to annotations from trained participants. The results generalized over both the DC and QA method and therefore indicate that a selection-only approach could also be effective for other crowdsourced discourse annotation tasks.

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TBD3: A Thresholding-Based Dynamic Depression Detection from Social Media for Low-Resource Users
Hrishikesh Kulkarni | Sean MacAvaney | Nazli Goharian | Ophir Frieder

Social media are heavily used by many users to share their mental health concerns and diagnoses. This trend has turned social media into a large-scale resource for researchers focused on detecting mental health conditions. Social media usage varies considerably across individuals. Thus, classification of patterns, including detecting signs of depression, must account for such variation. We address the disparity in classification effectiveness for users with little activity (e.g., new users). Our evaluation, performed on a large-scale dataset, shows considerable detection discrepancy based on user posting frequency. For instance, the F1 detection score of users with an above-median versus below-median number of posts is greater than double (0.803 vs 0.365) using a conventional CNN-based model; similar results were observed on lexical and transformer-based classifiers. To complement this evaluation, we propose a dynamic thresholding technique that adjusts the classifier’s sensitivity as a function of the number of posts a user has. This technique alone reduces the margin between users with many and few posts, on average, by 45% across all methods and increases overall performance, on average, by 33%. These findings emphasize the importance of evaluating and tuning natural language systems for potentially vulnerable populations.

Can NLP assist in building formal models for verifying complex systems? We study this challenge in the context of parsing Network File System (NFS) specifications. We define a semantic-dependency problem over SpecIR, a representation language we introduce to model sentences appearing in NFS specification documents (RFCs) as IF-THEN statements, and present an annotated dataset of 1,198 sentences. We develop and evaluate semantic-dependency parsing systems for this problem. Evaluations show that even when using a state-of-the-art language model, there is significant room for improvement, with the best models achieving an F1 score of only 60.5 and 33.3 in the named-entity-recognition and dependency-link-prediction sub-tasks, respectively. We also release additional unlabeled data and other domain-related texts. Experiments show that these additional resources increase the F1 measure when used for simple domain-adaption and transfer-learning-based approaches, suggesting fruitful directions for further research

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Argument Similarity Assessment in German for Intelligent Tutoring: Crowdsourced Dataset and First Experiments
Xiaoyu Bai | Manfred Stede

NLP technologies such as text similarity assessment, question answering and text classification are increasingly being used to develop intelligent educational applications. The long-term goal of our work is an intelligent tutoring system for German secondary schools, which will support students in a school exercise that requires them to identify arguments in an argumentative source text. The present paper presents our work on a central subtask, viz. the automatic assessment of similarity between a pair of argumentative text snippets in German. In the designated use case, students write out key arguments from a given source text; the tutoring system then evaluates them against a target reference, assessing the similarity level between student work and the reference. We collect a dataset for our similarity assessment task through crowdsourcing as authentic German student data are scarce; we label the collected text pairs with similarity scores on a 5-point scale and run first experiments on the task. We see that a model based on BERT shows promising results, while we also discuss some challenges that we observe.

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Leveraging Pre-trained Language Models for Gender Debiasing
Nishtha Jain | Declan Groves | Lucia Specia | Maja Popović

Studying and mitigating gender and other biases in natural language have become important areas of research from both algorithmic and data perspectives. This paper explores the idea of reducing gender bias in a language generation context by generating gender variants of sentences. Previous work in this field has either been rule-based or required large amounts of gender balanced training data. These approaches are however not scalable across multiple languages, as creating data or rules for each language is costly and time-consuming. This work explores a light-weight method to generate gender variants for a given text using pre-trained language models as the resource, without any task-specific labelled data. The approach is designed to work on multiple languages with minimal changes in the form of heuristics. To showcase that, we have tested it on a high-resourced language, namely Spanish, and a low-resourced language from a different family, namely Serbian. The approach proved to work very well on Spanish, and while the results were less positive for Serbian, it showed potential even for languages where pre-trained models are less effective.

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Unsupervised Embeddings with Graph Auto-Encoders for Multi-domain and Multilingual Hate Speech Detection
Gretel Liz De la Peña Sarracén | Paolo Rosso

Hate speech detection is a prominent and challenging task, since hate messages are often expressed in subtle ways and with characteristics that may vary depending on the author. Hence, many models suffer from the generalization problem. However, retrieving and monitoring hateful content on social media is a current necessity. In this paper, we propose an unsupervised approach using Graph Auto-Encoders (GAE), which allows us to avoid using labeled data when training the representation of the texts. Specifically, we represent texts as nodes of a graph, and use a transformer layer together with a convolutional layer to encode these nodes in a low-dimensional space. As a result, we obtain embeddings that can be decoded into a reconstruction of the original network. Our main idea is to learn a model with a set of texts without supervision, in order to generate embeddings for the nodes: nodes with the same label should be close in the embedding space, which, in turn, should allow us to distinguish among classes. We employ this strategy to detect hate speech in multi-domain and multilingual sets of texts, where our method shows competitive results on small datasets.

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FQuAD2.0: French Question Answering and Learning When You Don’t Know
Quentin Heinrich | Gautier Viaud | Wacim Belblidia

Question Answering, including Reading Comprehension, is one of the NLP research areas that has seen significant scientific breakthroughs over the past few years, thanks to the concomitant advances in Language Modeling. Most of these breakthroughs, however, are centered on the English language. In 2020, as a first strong initiative to bridge the gap to the French language, Illuin Technology introduced FQuAD1.1, a French Native Reading Comprehension dataset composed of 60,000+ questions and answers samples extracted from Wikipedia articles. Nonetheless, Question Answering models trained on this dataset have a major drawback: they are not able to predict when a given question has no answer in the paragraph of interest, therefore making unreliable predictions in various industrial use-cases. We introduce FQuAD2.0, which extends FQuAD with 17,000+ unanswerable questions, annotated adversarially, in order to be similar to answerable ones. This new dataset, comprising a total of almost 80,000 questions, makes it possible to train French Question Answering models with the ability of distinguishing unanswerable questions from answerable ones. We benchmark several models with this dataset: our best model, a fine-tuned CamemBERT-large, achieves a F1 score of 82.3% on this classification task, and a F1 score of 83% on the Reading Comprehension task.

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Large-Scale Hate Speech Detection with Cross-Domain Transfer
Cagri Toraman | Furkan Şahinuç | Eyup Yilmaz

The performance of hate speech detection models relies on the datasets on which the models are trained. Existing datasets are mostly prepared with a limited number of instances or hate domains that define hate topics. This hinders large-scale analysis and transfer learning with respect to hate domains. In this study, we construct large-scale tweet datasets for hate speech detection in English and a low-resource language, Turkish, consisting of human-labeled 100k tweets per each. Our datasets are designed to have equal number of tweets distributed over five domains. The experimental results supported by statistical tests show that Transformer-based language models outperform conventional bag-of-words and neural models by at least 5% in English and 10% in Turkish for large-scale hate speech detection. The performance is also scalable to different training sizes, such that 98% of performance in English, and 97% in Turkish, are recovered when 20% of training instances are used. We further examine the generalization ability of cross-domain transfer among hate domains. We show that 96% of the performance of a target domain in average is recovered by other domains for English, and 92% for Turkish. Gender and religion are more successful to generalize to other domains, while sports fail most.

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GLoHBCD: A Naturalistic German Dataset for Language of Health Behaviour Change on Online Support Forums
Selina Meyer | David Elsweiler

Health behaviour change is a difficult and prolonged process that requires sustained motivation and determination. Conversa- tional agents have shown promise in supporting the change process in the past. One therapy approach that facilitates change and has been used as a framework for conversational agents is motivational interviewing. However, existing implementations of this therapy approach lack the deep understanding of user utterances that is essential to the spirit of motivational interviewing. To address this lack of understanding, we introduce the GLoHBCD, a German dataset of naturalistic language around health behaviour change. Data was sourced from a popular German weight loss forum and annotated using theoretically grounded motivational interviewing categories. We describe the process of dataset construction and present evaluation results. Initial experiments suggest a potential for broad applicability of the data and the resulting classifiers across different behaviour change domains. We make code to replicate the dataset and experiments available on Github.

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Creating a Data Set of Abstractive Summaries of Turn-labeled Spoken Human-Computer Conversations
Iris Hendrickx

Digital recorded written and spoken dialogues are becoming increasingly available as an effect of the technological advances such as online messenger services and the use of chatbots. Summaries are a natural way of presenting the important information gathered from dialogues. We present a unique data set that consists of Dutch spoken human-computer conversations, an annotation layer of turn labels, and conversational abstractive summaries of user answers. The data set is publicly available for research purposes.

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OpenEL: An Annotated Corpus for Entity Linking and Discourse in Open Domain Dialogue
Wen Cui | Leanne Rolston | Marilyn Walker | Beth Ann Hockey

Entity linking in dialogue is the task of mapping entity mentions in utterances to a target knowledge base. Prior work on entity linking has mainly focused on well-written articles such as Wikipedia, annotated newswire, or domain-specific datasets. We extend the study of entity linking to open domain dialogue by presenting the OpenEL corpus: an annotated multi-domain corpus for linking entities in natural conversation to Wikidata. Each dialogic utterance in 179 dialogues over 12 topics from the EDINA dataset has been annotated for entities realized by definite referring expressions as well as anaphoric forms such as he, she, it and they. This dataset supports training and evaluation of entity linking in open-domain dialogue, as well as analysis of the effect of using dialogue context and anaphora resolution in model training. It could also be used for fine-tuning a coreference resolution algorithm. To the best of our knowledge, this is the first substantial entity linking corpus publicly available for open-domain dialogue. We also establish baselines for this task using several existing entity linking systems. We found that the Transformer-based system Flair + BLINK has the best performance with a 0.65 F1 score. Our results show that dialogue context is extremely beneficial for entity linking in conversations, with Flair + Blink achieving an F1 of 0.61 without discourse context. These results also demonstrate the remaining performance gap between the baselines and human performance, highlighting the challenges of entity linking in open-domain dialogue, and suggesting many avenues for future research using OpenEL.

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Collecting Visually-Grounded Dialogue with A Game Of Sorts
Bram Willemsen | Dmytro Kalpakchi | Gabriel Skantze

An idealized, though simplistic, view of the referring expression production and grounding process in (situated) dialogue assumes that a speaker must merely appropriately specify their expression so that the target referent may be successfully identified by the addressee. However, referring in conversation is a collaborative process that cannot be aptly characterized as an exchange of minimally-specified referring expressions. Concerns have been raised regarding assumptions made by prior work on visually-grounded dialogue that reveal an oversimplified view of conversation and the referential process. We address these concerns by introducing a collaborative image ranking task, a grounded agreement game we call “A Game Of Sorts”. In our game, players are tasked with reaching agreement on how to rank a set of images given some sorting criterion through a largely unrestricted, role-symmetric dialogue. By putting emphasis on the argumentation in this mixed-initiative interaction, we collect discussions that involve the collaborative referential process. We describe results of a small-scale data collection experiment with the proposed task. All discussed materials, which includes the collected data, the codebase, and a containerized version of the application, are publicly available.

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CoRoSeOf - An Annotated Corpus of Romanian Sexist and Offensive Tweets
Diana Constantina Hoefels | Çağrı Çöltekin | Irina Diana Mădroane

This paper introduces CoRoSeOf, a large corpus of Romanian social media manually annotated for sexist and offensive language. We describe the annotation process of the corpus, provide initial analyses, and baseline classification results for sexism detection on this data set. The resulting corpus contains 39 245 tweets, annotated by multiple annotators (with an agreement rate of Fleiss’κ= 0.45), following the sexist label set of a recent study. The automatic sexism detection yields scores similar to some of the earlier studies (macro averaged F1 score of 83.07% on binary classification task). We release the corpus with a permissive license.

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ArMIS - The Arabic Misogyny and Sexism Corpus with Annotator Subjective Disagreements
Dina Almanea | Massimo Poesio

The use of misogynistic and sexist language has increased in recent years in social media, and is increasing in the Arabic world in reaction to reforms attempting to remove restrictions on women lives. However, there are few benchmarks for Arabic misogyny and sexism detection, and in those the annotations are in aggregated form even though misogyny and sexism judgments are found to be highly subjective. In this paper we introduce an Arabic misogyny and sexism dataset (ArMIS) characterized by providing annotations from annotators with different degree of religious beliefs, and provide evidence that such differences do result in disagreements. To the best of our knowledge, this is the first dataset to study in detail the effect of beliefs on misogyny and sexism annotation. We also discuss proof-of-concept experiments showing that a dataset in which disagreements have not been reconciled can be used to train state-of-the-art models for misogyny and sexism detection; and consider different ways in which such models could be evaluated.

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Annotating Interruption in Dyadic Human Interaction
Liu Yang | Catherine Achard | Catherine Pelachaud

Integrating the existing interruption and turn switch classification methods, we propose a new annotation schema to annotate different types of interruptions through timeliness, switch accomplishment and speech content level. The proposed method is able to distinguish smooth turn exchange, backchannel and interruption (including interruption types) and to annotate dyadic conversation. We annotated the French part of NoXi corpus with the proposed structure and use these annotations to study the probability distribution and duration of each turn switch type.

Despite the importance of understanding causality, corpora addressing causal relations are limited. There is a discrepancy between existing annotation guidelines of event causality and conventional causality corpora that focus more on linguistics. Many guidelines restrict themselves to include only explicit relations or clause-based arguments. Therefore, we propose an annotation schema for event causality that addresses these concerns. We annotated 3,559 event sentences from protest event news with labels on whether it contains causal relations or not. Our corpus is known as the Causal News Corpus (CNC). A neural network built upon a state-of-the-art pre-trained language model performed well with 81.20% F1 score on test set, and 83.46% in 5-folds cross-validation. CNC is transferable across two external corpora: CausalTimeBank (CTB) and Penn Discourse Treebank (PDTB). Leveraging each of these external datasets for training, we achieved up to approximately 64% F1 on the CNC test set without additional fine-tuning. CNC also served as an effective training and pre-training dataset for the two external corpora. Lastly, we demonstrate the difficulty of our task to the layman in a crowd-sourced annotation exercise. Our annotated corpus is publicly available, providing a valuable resource for causal text mining researchers.

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Samrómur: Crowd-sourcing large amounts of data
Staffan Hedström | David Erik Mollberg | Ragnheiður Þórhallsdóttir | Jón Guðnason

This contribution describes the collection of a large and diverse corpus for speech recognition and similar tools using crowd-sourced donations. We have built a collection platform inspired by Mozilla Common Voice and specialized it to our needs. We discuss the importance of engaging the community and motivating it to contribute, in our case through competitions. Given the incentive and a platform to easily read in large amounts of utterances, we have observed four cases of speakers freely donating over 10 thousand utterances. We have also seen that women are keener to participate in these events throughout all age groups. Manually verifying a large corpus is a monumental task and we attempt to automatically verify parts of the data using tools like Marosijo and the Montreal Forced Aligner. The method proved helpful, especially for detecting invalid utterances and halving the work needed from crowd-sourced verification.

In recent years, machine learning for clinical decision support has gained more and more attention. In order to introduce such applications into clinical practice, a good performance might be essential, however, the aspect of trust should not be underestimated. For the treating physician using such a system and being (legally) responsible for the decision made, it is particularly important to understand the system’s recommendation. To provide insights into a model’s decision, various techniques from the field of explainability (XAI) have been proposed whose output is often enough not targeted to the domain experts that want to use the model. To close this gap, in this work, we explore how explanations could possibly look like in future. To this end, this work presents a dataset of textual explanations in context of decision support. Within a reader study, human physicians estimated the likelihood of possible negative patient outcomes in the near future and justified each decision with a few sentences. Using those sentences, we created a novel corpus, annotated with different semantic layers. Moreover, we provide an analysis of how those explanations are constructed, and how they change depending on physician, on the estimated risk and also in comparison to an automatic clinical decision support system with feature importance.

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LARD: Large-scale Artificial Disfluency Generation
Tatiana Passali | Thanassis Mavropoulos | Grigorios Tsoumakas | Georgios Meditskos | Stefanos Vrochidis

Disfluency detection is a critical task in real-time dialogue systems. However, despite its importance, it remains a relatively unexplored field, mainly due to the lack of appropriate datasets. At the same time, existing datasets suffer from various issues, including class imbalance issues, which can significantly affect the performance of the model on rare classes, as it is demonstrated in this paper. To this end, we propose LARD, a method for generating complex and realistic artificial disfluencies with little effort. The proposed method can handle three of the most common types of disfluencies: repetitions, replacements, and restarts. In addition, we release a new large-scale dataset with disfluencies that can be used on four different tasks: disfluency detection, classification, extraction, and correction. Experimental results on the LARD dataset demonstrate that the data produced by the proposed method can be effectively used for detecting and removing disfluencies, while also addressing limitations of existing datasets.

We describe a new freely available Chinese multi-party dialogue dataset for automatic extraction of dialogue-based character relationships. The data has been extracted from the original TV scripts of a Chinese sitcom called “I Love My Home” with complex family-based human daily spoken conversations in Chinese. First, we introduced human annotation scheme for both global Character relationship map and character reference relationship. And then we generated the dialogue-based character relationship triples. The corpus annotates relationships between 140 entities in total. We also carried out a data exploration experiment by deploying a BERT-based model to extract character relationships on the CRECIL corpus and another existing relation extraction corpus (DialogRE (CITATION)).The results demonstrate that extracting character relationships is more challenging in CRECIL than in DialogRE.

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The Bahrain Corpus: A Multi-genre Corpus of Bahraini Arabic
Dana Abdulrahim | Go Inoue | Latifa Shamsan | Salam Khalifa | Nizar Habash

In recent years, the focus on developing natural language processing (NLP) tools for Arabic has shifted from Modern Standard Arabic to various Arabic dialects. Various corpora of various sizes and representing different genres, have been created for a number of Arabic dialects. As far as Gulf Arabic is concerned, Gumar Corpus (Khalifa et al., 2016) is the largest corpus, to date, that includes data representing the dialectal Arabic of the six Gulf Cooperation Council countries (Bahrain, Kuwait, Saudi Arabia, Qatar, United Arab Emirates, and Oman), particularly in the genre of “online forum novels”. In this paper, we present the Bahrain Corpus. Our objective is to create a specialized corpus of the Bahraini Arabic dialect, which includes written texts as well as transcripts of audio files, belonging to a different genre (folktales, comedy shows, plays, cooking shows, etc.). The corpus comprises 620K words, carefully curated. We provide automatic morphological annotations of the full corpus using state-of-the-art morphosyntactic disambiguation for Gulf Arabic. We validate the quality of the annotations on a 7.6K word sample. We plan to make the annotated sample as well as the full corpus publicly available to support researchers interested in Arabic NLP.

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A Universal Dependencies Treebank of Ancient Hebrew
Daniel Swanson | Francis Tyers

In this paper we present the initial construction of a Universal Dependencies treebank with morphological annotations of Ancient Hebrew containing portions of the Hebrew Scriptures (1579 sentences, 27K tokens) for use in comparative study with ancient translations and for analysis of the development of Hebrew syntax. We construct this treebank by applying a rule-based parser (300 rules) to an existing morphologically-annotated corpus with minimal constituency structure and manually verifying the output and present the results of this semi-automated annotation process and some of the annotation decisions made in the process of applying the UD guidelines to a new language.

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Hate Speech Dynamics Against African descent, Roma and LGBTQI Communities in Portugal
Paula Carvalho | Bernardo Cunha | Raquel Santos | Fernando Batista | Ricardo Ribeiro

This paper introduces FIGHT, a dataset containing 63,450 tweets, posted before and after the official declaration of Covid-19 as a pandemic by online users in Portugal. This resource aims at contributing to the analysis of online hate speech targeting the most representative minorities in Portugal, namely the African descent and the Roma communities, and the LGBTQI community, the most commonly reported target of hate speech in social media at the European context. We present the methods for collecting the data, and provide insightful statistics on the distribution of tweets included in FIGHT, considering both the temporal and spatial dimensions. We also analyze the availability over time of tweets targeting the above-mentioned communities, distinguishing public, private and deleted tweets. We believe this study will contribute to better understand the dynamics of online hate speech in Portugal, particularly in adverse contexts, such as a pandemic outbreak, allowing the development of more informed and accurate hate speech resources for Portuguese.

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Evolving Large Text Corpora: Four Versions of the Icelandic Gigaword Corpus
Starkaður Barkarson | Steinþór Steingrímsson | Hildur Hafsteinsdóttir

The Icelandic Gigaword Corpus was first published in 2018. Since then new versions have been published annually, containing new texts from additional sources as well as from previous sources. This paper describes the evolution of the corpus in its first four years. All versions are made available under permissive licenses and with each new version the texts are annotated with the latest and most accurate tools. We show how the corpus has grown almost 50% in size from the first version to the fourth and how it was restructured in order to better accommodate different meta-data for different subcorpora. Furthermore, other services have been set up to facilitate usage of the corpus for different use cases. These include a keyword-in-context concordance tool, an n-gram viewer, a word frequency database and pre-trained word embeddings.

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A Pragmatics-Centered Evaluation Framework for Natural Language Understanding
Damien Sileo | Philippe Muller | Tim Van de Cruys | Camille Pradel

New models for natural language understanding have recently made an unparalleled amount of progress, which has led some researchers to suggest that the models induce universal text representations. However, current benchmarks are predominantly targeting semantic phenomena; we make the case that pragmatics needs to take center stage in the evaluation of natural language understanding. We introduce PragmEval, a new benchmark for the evaluation of natural language understanding, that unites 11 pragmatics-focused evaluation datasets for English. PragmEval can be used as supplementary training data in a multi-task learning setup, and is publicly available, alongside the code for gathering and preprocessing the datasets. Using our evaluation suite, we show that natural language inference, a widely used pretraining task, does not result in genuinely universal representations, which presents a new challenge for multi-task learning.

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Conversational Analysis of Daily Dialog Data using Polite Emotional Dialogue Acts
Chandrakant Bothe | Stefan Wermter

Many socio-linguistic cues are used in conversational analysis, such as emotion, sentiment, and dialogue acts. One of the fundamental social cues is politeness, which linguistically possesses properties such as social manners useful in conversational analysis. This article presents findings of polite emotional dialogue act associations, where we can correlate the relationships between the socio-linguistic cues. We confirm our hypothesis that the utterances with the emotion classes Anger and Disgust are more likely to be impolite. At the same time, Happiness and Sadness are more likely to be polite. A less expectable phenomenon occurs with dialogue acts Inform and Commissive which contain more polite utterances than Question and Directive. Finally, we conclude on the future work of these findings to extend the learning of social behaviours using politeness.

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Inducing Discourse Marker Inventories from Lexical Knowledge Graphs
Christian Chiarcos

Discourse marker inventories are important tools for the development of both discourse parsers and corpora with discourse annotations. In this paper we explore the potential of massively multilingual lexical knowledge graphs to induce multilingual discourse marker lexicons using concept propagation methods as previously developed in the context of translation inference across dictionaries. Given one or multiple source languages with discourse marker inventories that discourse relations as senses of potential discourse markers, as well as a large number of bilingual dictionaries that link them – directly or indirectly – with the target language, we specifically study to what extent discourse marker induction can benefit from the integration of information from different sources, the impact of sense granularity and what limiting factors may need to be considered. Our study uses discourse marker inventories from nine European languages normalized against the discourse relation inventory of the Penn Discourse Treebank (PDTB), as well as three collections of machine-readable dictionaries with different characteristics, so that the interplay of a large number of factors can be studied.

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Story Trees: Representing Documents using Topological Persistence
Pantea Haghighatkhah | Antske Fokkens | Pia Sommerauer | Bettina Speckmann | Kevin Verbeek

Topological Data Analysis (TDA) focuses on the inherent shape of (spatial) data. As such, it may provide useful methods to explore spatial representations of linguistic data (embeddings) which have become central in NLP. In this paper we aim to introduce TDA to researchers in language technology. We use TDA to represent document structure as so-called story trees. Story trees are hierarchical representations created from semantic vector representations of sentences via persistent homology. They can be used to identify and clearly visualize prominent components of a story line. We showcase their potential by using story trees to create extractive summaries for news stories.

The study of metaphors in media discourse is an increasingly researched topic as media are an important shaper of social reality and metaphors are an indicator of how we think about certain issues through references to other things. We present a neural transfer learning method for detecting metaphorical sentences in Slovene and evaluate its performance on a gold standard corpus of metaphors (classification accuracy of 0.725), as well as on a sample of a domain specific corpus of migrations (precision of 0.40 for extracting domain metaphors and 0.74 if evaluated only on a set of migration related sentences). Based on empirical results and findings of our analysis, we propose a novel metaphor annotation scheme containing linguistic level, conceptual level, and stance information. The new scheme can be used for future metaphor annotations of other socially relevant topics.

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DDisCo: A Discourse Coherence Dataset for Danish
Linea Flansmose Mikkelsen | Oliver Kinch | Anders Jess Pedersen | Ophélie Lacroix

To date, there has been no resource for studying discourse coherence on real-world Danish texts. Discourse coherence has mostly been approached with the assumption that incoherent texts can be represented by coherent texts in which sentences have been shuffled. However, incoherent real-world texts rarely resemble that. We thus present DDisCo, a dataset including text from the Danish Wikipedia and Reddit annotated for discourse coherence. We choose to annotate real-world texts instead of relying on artificially incoherent text for training and testing models. Then, we evaluate the performance of several methods, including neural networks, on the dataset.

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LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of Attacks in Arguments
Farjana Sultana Mim | Naoya Inoue | Shoichi Naito | Keshav Singh | Kentaro Inui

In argumentative discourse, persuasion is often achieved by refuting or attacking others’ arguments. Attacking an argument is not always straightforward and often consists of complex rhetorical moves in which arguers may agree with a logic of an argument while attacking another logic. Furthermore, an arguer may neither deny nor agree with any logics of an argument, instead ignore them and attack the main stance of the argument by providing new logics and presupposing that the new logics have more value or importance than the logics presented in the attacked argument. However, there are no studies in computational argumentation that capture such complex rhetorical moves in attacks or the presuppositions or value judgments in them. To address this gap, we introduce LPAttack, a novel annotation scheme that captures the common modes and complex rhetorical moves in attacks along with the implicit presuppositions and value judgments. Our annotation study shows moderate inter-annotator agreement, indicating that human annotation for the proposed scheme is feasible. We publicly release our annotated corpus and the annotation guidelines.

We present the BeSt corpus, which records cognitive state: who believes what (i.e., factuality), and who has what sentiment towards what. This corpus is inspired by similar source-and-target corpora, specifically MPQA and FactBank. The corpus comprises two genres, newswire and discussion forums, in three languages, Chinese (Mandarin), English, and Spanish. The corpus is distributed through the LDC.

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MOTIF: Contextualized Images for Complex Words to Improve Human Reading
Xintong Wang | Florian Schneider | Özge Alacam | Prateek Chaudhury | Chris Biemann

MOTIF (MultimOdal ConTextualized Images For Language Learners) is a multimodal dataset that consists of 1125 comprehension texts retrieved from Wikipedia Simple Corpus. Allowing multimodal processing or enriching the context with multimodal information has proven imperative for many learning tasks, specifically for second language (L2) learning. In this respect, several traditional NLP approaches can assist L2 readers in text comprehension processes, such as simplifying text or giving dictionary descriptions for complex words. As nicely stated in the well-known proverb, sometimes “a picture is worth a thousand words” and an image can successfully complement the verbal message by enriching the representation, like in Pictionary books. This multimodal support can also assist on-the-fly text reading experience by providing a multimodal tool that chooses and displays the most relevant images for the difficult words, given the text context. This study mainly focuses on one of the key components to achieving this goal; collecting a multimodal dataset enriched with complex word annotation and validated image match.

Sign Languages (SLs) are the primary means of communication for at least half a million people in Europe alone. However, the development of SL recognition and translation tools is slowed down by a series of obstacles concerning resource scarcity and standardization issues in the available data. The former challenge relates to the volume of data available for machine learning as well as the time required to collect and process new data. The latter obstacle is linked to the variety of the data, i.e., annotation formats are not unified and vary amongst different resources. The available data formats are often not suitable for machine learning, obstructing the provision of automatic tools based on neural models. In the present paper, we give an overview of these challenges by comparing various SL corpora and SL machine learning datasets. Furthermore, we propose a framework to address the lack of standardization at format level, unify the available resources and facilitate SL research for different languages. Our framework takes ELAN files as inputs and returns textual and visual data ready to train SL recognition and translation models. We present a proof of concept, training neural translation models on the data produced by the proposed framework.

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A Low-Cost Motion Capture Corpus in French Sign Language for Interpreting Iconicity and Spatial Referencing Mechanisms
Clémence Mertz | Vincent Barreaud | Thibaut Le Naour | Damien Lolive | Sylvie Gibet

The automatic translation of sign language videos into transcribed texts is rarely approached in its whole, as it implies to finely model the grammatical mechanisms that govern these languages. The presented work is a first step towards the interpretation of French sign language (LSF) by specifically targeting iconicity and spatial referencing. This paper describes the LSF-SHELVES corpus as well as the original technology that was designed and implemented to collect it. Our goal is to use deep learning methods to circumvent the use of models in spatial referencing recognition. In order to obtain training material with sufficient variability, we designed a light-weight (and low-cost) capture protocol that enabled us to collect data from a large panel of LSF signers. This protocol involves the use of a portable device providing a 3D skeleton, and of a software developed specifically for this application to facilitate the post-processing of handshapes. The LSF-SHELVES includes simple and compound iconic and spatial dynamics, organized in 6 complexity levels, representing a total of 60 sequences signed by 15 LSF signers.

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The CLAMS Platform at Work: Processing Audiovisual Data from the American Archive of Public Broadcasting
Marc Verhagen | Kelley Lynch | Kyeongmin Rim | James Pustejovsky

The Computational Linguistics Applications for Multimedia Services (CLAMS) platform provides access to computational content analysis tools for multimedia material. The version we present here is a robust update of an initial prototype implementation from 2019. The platform now sports a variety of image, video, audio and text processing tools that interact via a common multi-modal representation language named MMIF (Multi-Media Interchange Format). We describe the overall architecture, the MMIF format, some of the tools included in the platform, the process to set up and run a workflow, visualizations included in CLAMS, and evaluate aspects of the platform on data from the American Archive of Public Broadcasting, showing how CLAMS can add metadata to mass-digitized multimedia collections, metadata that are typically only available implicitly in now largely unsearchable digitized media in archives and libraries.

Given our society’s increased exposure to multimedia formats on social media platforms, efforts to understand how digital content impacts people’s emotions are burgeoning. As such, we introduce a U.S. gun violence news dataset that contains news headline and image pairings from 840 news articles with 15K high-quality, crowdsourced annotations on emotional responses to the news pairings. We created three experimental conditions for the annotation process: two with a single modality (headline or image only), and one multimodal (headline and image together). In contrast to prior works on affectively-annotated data, our dataset includes annotations on the dominant emotion experienced with the content, the intensity of the selected emotion and an open-ended, written component. By collecting annotations on different modalities of the same news content pairings, we explore the relationship between image and text influence on human emotional response. We offer initial analysis on our dataset, showing the nuanced affective differences that appear due to modality and individual factors such as political leaning and media consumption habits. Our dataset is made publicly available to facilitate future research in affective computing.

We present RoomReader, a corpus of multimodal, multiparty conversational interactions in which participants followed a collaborative student-tutor scenario designed to elicit spontaneous speech. The corpus was developed within the wider RoomReader Project to explore multimodal cues of conversational engagement and behavioural aspects of collaborative interaction in online environments. However, the corpus can be used to study a wide range of phenomena in online multimodal interaction. The publicly-shared corpus consists of over 8 hours of video and audio recordings from 118 participants in 30 gender-balanced sessions, in the “in-the-wild” online environment of Zoom. The recordings have been edited, synchronised, and fully transcribed. Student participants have been continuously annotated for engagement with a novel continuous scale. We provide questionnaires measuring engagement and group cohesion collected from the annotators, tutors and participants themselves. We also make a range of accompanying data available such as personality tests and behavioural assessments. The dataset and accompanying psychometrics present a rich resource enabling the exploration of a range of downstream tasks across diverse fields including linguistics and artificial intelligence. This could include the automatic detection of student engagement, analysis of group interaction and collaboration in online conversation, and the analysis of conversational behaviours in an online setting.

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Quevedo: Annotation and Processing of Graphical Languages
Antonio F. G. Sevilla | Alberto Díaz Esteban | José María Lahoz-Bengoechea

In this article, we present Quevedo, a software tool we have developed for the task of automatic processing of graphical languages. These are languages which use images to convey meaning, relying not only on the shape of symbols but also on their spatial arrangement in the page, and relative to each other. When presented in image form, these languages require specialized computational processing which is not the same as usually done either for natural language processing or for artificial vision. Quevedo enables this specialized processing, focusing on a data-based approach. As a command line application and library, it provides features for the collection and management of image datasets, and their machine learning recognition using neural networks and recognizer pipelines. This processing requires careful annotation of the source data, for which Quevedo offers an extensive and visual web-based annotation interface. In this article, we also briefly present a case study centered on the task of SignWriting recognition, the original motivation for writing the software. Quevedo is written in Python, and distributed freely under the Open Software License version 3.0.

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Merkel Podcast Corpus: A Multimodal Dataset Compiled from 16 Years of Angela Merkel’s Weekly Video Podcasts
Debjoy Saha | Shravan Nayak | Timo Baumann

We introduce the Merkel Podcast Corpus, an audio-visual-text corpus in German collected from 16 years of (almost) weekly Internet podcasts of former German chancellor Angela Merkel. To the best of our knowledge, this is the first single speaker corpus in the German language consisting of audio, visual and text modalities of comparable size and temporal extent. We describe the methods used with which we have collected and edited the data which involves downloading the videos, transcripts and other metadata, forced alignment, performing active speaker recognition and face detection to finally curate the single speaker dataset consisting of utterances spoken by Angela Merkel. The proposed pipeline is general and can be used to curate other datasets of similar nature, such as talk show contents. Through various statistical analyses and applications of the dataset in talking face generation and TTS, we show the utility of the dataset. We argue that it is a valuable contribution to the research community, in particular, due to its realistic and challenging material at the boundary between prepared and spontaneous speech.

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Crowdsourcing Kazakh-Russian Sign Language: FluentSigners-50
Medet Mukushev | Aigerim Kydyrbekova | Alfarabi Imashev | Vadim Kimmelman | Anara Sandygulova

This paper presents the methodology we used to crowdsource a data collection of a new large-scale signer independent dataset for Kazakh-Russian Sign Language (KRSL) created for Sign Language Processing. By involving the Deaf community throughout the research process, we firstly designed a research protocol and then performed an efficient crowdsourcing campaign that resulted in a new FluentSigners-50 dataset. The FluentSigners-50 dataset consists of 173 sentences performed by 50 KRSL signers for 43,250 video samples. Dataset contributors recorded videos in real-life settings on various backgrounds using various devices such as smartphones and web cameras. Therefore, each dataset contribution has a varying distance to the camera, camera angles and aspect ratio, video quality, and frame rates. Additionally, the proposed dataset contains a high degree of linguistic and inter-signer variability and thus is a better training set for recognizing a real-life signed speech. FluentSigners-50 is publicly available at https://krslproject.github.io/fluentsigners-50/

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Connecting a French Dictionary from the Beginning of the 20th Century to Wikidata
Pierre Nugues

The Petit Larousse illustré is a French dictionary first published in 1905. Its division in two main parts on language and on history and geography corresponds to a major milestone in French lexicography as well as a repository of general knowledge from this period. Although the value of many entries from 1905 remains intact, some descriptions now have a dimension that is more historical than contemporary. They are nonetheless significant to analyze and understand cultural representations from this time. A comparison with more recent information or a verification of these entries would require a tedious manual work. In this paper, we describe a new lexical resource, where we connected all the dictionary entries of the history and geography part to current data sources. For this, we linked each of these entries to a wikidata identifier. Using the wikidata links, we can automate more easily the identification, comparison, and verification of historically-situated representations. We give a few examples on how to process wikidata identifiers and we carried out a small analysis of the entities described in the dictionary to outline possible applications. The resource, i.e. the annotation of 20,245 dictionary entries with wikidata links, is available from GitHub (https://github.com/pnugues/petit_larousse_1905/)

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Metaphor annotation for German
Markus Egg | Valia Kordoni

The paper presents current work on a German corpus annotated for metaphor. Metaphors denote entities or situations that are in some sense similar to the literal referent, e.g., when “Handschrift” ‘signature’ is used in the sense of ‘distinguishing mark’ or the suppression of hopes is introduced by the verb “verschütten” ‘bury’. The corpus is part of a project on register, hence, includes material from different registers that represent register variation along a number of important dimensions, but we believe that it is of interest to research on metaphor in general. The corpus extends previous annotation initiatives in that it not only annotates the metaphoric expressions themselves but also their respective relevant contexts that trigger a metaphorical interpretation of the expressions. For the corpus, we developed extended annotation guidelines, which specifically focus not only on the identification of these metaphoric contexts but also analyse in detail specific linguistic challenges for metaphor annotation that emerge due to the grammar of German.

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NorDiaChange: Diachronic Semantic Change Dataset for Norwegian
Andrey Kutuzov | Samia Touileb | Petter Mæhlum | Tita Enstad | Alexandra Wittemann

We describe NorDiaChange: the first diachronic semantic change dataset for Norwegian. NorDiaChange comprises two novel subsets, covering about 80 Norwegian nouns manually annotated with graded semantic change over time. Both datasets follow the same annotation procedure and can be used interchangeably as train and test splits for each other. NorDiaChange covers the time periods related to pre- and post-war events, oil and gas discovery in Norway, and technological developments. The annotation was done using the DURel framework and two large historical Norwegian corpora. NorDiaChange is published in full under a permissive licence, complete with raw annotation data and inferred diachronic word usage graphs (DWUGs).

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Exploring Transformers for Ranking Portuguese Semantic Relations
Hugo Gonçalo Oliveira

We explored transformer-based language models for ranking instances of Portuguese lexico-semantic relations. Weights were based on the likelihood of natural language sequences that transmitted the relation instances, and expectations were that they would be useful for filtering out noisier instances. However, after analysing the weights, no strong conclusions were taken. They are not correlated with redundancy, but are lower for instances with longer and more specific arguments, which may nevertheless be a consequence of their sensitivity to the frequency of such arguments. They did also not reveal to be useful when computing word similarity with network embeddings. Despite the negative results, we see the reported experiments and insights as another contribution for better understanding transformer language models like BERT and GPT, and we make the weighted instances publicly available for further research.

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Building Static Embeddings from Contextual Ones: Is It Useful for Building Distributional Thesauri?
Olivier Ferret

While contextual language models are now dominant in the field of Natural Language Processing, the representations they build at the token level are not always suitable for all uses. In this article, we propose a new method for building word or type-level embeddings from contextual models. This method combines the generalization and the aggregation of token representations. We evaluate it for a large set of English nouns from the perspective of the building of distributional thesauri for extracting semantic similarity relations. Moreover, we analyze the differences between static embeddings and type-level embeddings according to features such as the frequency of words or the type of semantic relations these embeddings account for, showing that the properties of these two types of embeddings can be complementary and exploited for further improving distributional thesauri.

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Sentence Selection Strategies for Distilling Word Embeddings from BERT
Yixiao Wang | Zied Bouraoui | Luis Espinosa Anke | Steven Schockaert

Many applications crucially rely on the availability of high-quality word vectors. To learn such representations, several strategies based on language models have been proposed in recent years. While effective, these methods typically rely on a large number of contextualised vectors for each word, which makes them impractical. In this paper, we investigate whether similar results can be obtained when only a few contextualised representations of each word can be used. To this end, we analyse a range of strategies for selecting the most informative sentences. Our results show that with a careful selection strategy, high-quality word vectors can be learned from as few as 5 to 10 sentences.

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DiaWUG: A Dataset for Diatopic Lexical Semantic Variation in Spanish
Gioia Baldissin | Dominik Schlechtweg | Sabine Schulte im Walde

We provide a novel dataset – DiaWUG – with judgements on diatopic lexical semantic variation for six Spanish variants in Europe and Latin America. In contrast to most previous meaning-based resources and studies on semantic diatopic variation, we collect annotations on semantic relatedness for Spanish target words in their contexts from both a semasiological perspective (i.e., exploring the meanings of a word given its form, thus including polysemy) and an onomasiological perspective (i.e., exploring identical meanings of words with different forms, thus including synonymy). In addition, our novel dataset exploits and extends the existing framework DURel for annotating word senses in context (Erk et al., 2013; Schlechtweg et al., 2018) and the framework-embedded Word Usage Graphs (WUGs) – which up to now have mainly be used for semasiological tasks and resources – in order to distinguish, visualize and interpret lexical semantic variation of contextualized words in Spanish from these two perspectives, i.e., semasiological and onomasiological language variation.

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My Case, For an Adposition: Lexical Polysemy of Adpositions and Case Markers in Finnish and Latin
Daniel Chen | Mans Hulden

Adpositions and case markers contain a high degree of polysemy and participate in unique semantic role configurations. We present a novel application of the SNACS supersense hierarchy to Finnish and Latin data by manually annotating adposition and case marker tokens in Finnish and Latin translations of Chapters IV-V of Le Petit Prince (The Little Prince). We evaluate the computational validity of the semantic role annotation categories by grouping raw, contextualized Multilingual BERT embeddings using k-means clustering.

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WiC-TSV-de: German Word-in-Context Target-Sense-Verification Dataset and Cross-Lingual Transfer Analysis
Anna Breit | Artem Revenko | Narayani Blaschke

Target Sense Verification (TSV) describes the binary disambiguation task of deciding whether the intended sense of a target word in a context corresponds to a given target sense. In this paper, we introduce WiC-TSV-de, a multi-domain dataset for German Target Sense Verification. While the training and development sets consist of domain-independent instances only, the test set contains domain-bound subsets, originating from four different domains, being Gastronomy, Medicine, Hunting, and Zoology. The domain-bound subsets incorporate adversarial examples such as in-domain ambiguous target senses and context-mixing (i.e., using the target sense in an out-of-domain context) which contribute to the challenging nature of the presented dataset. WiC-TSV-de allows for the development of sense-inventory-independent disambiguation models that can generalise their knowledge for different domain settings. By combining it with the original English WiC-TSV benchmark, we performed monolingual and cross-lingual analysis, where the evaluated baseline models were not able to solve the dataset to a satisfying degree, leaving a big gap to human performance.

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Re-train or Train from Scratch? Comparing Pre-training Strategies of BERT in the Medical Domain
Hicham El Boukkouri | Olivier Ferret | Thomas Lavergne | Pierre Zweigenbaum

BERT models used in specialized domains all seem to be the result of a simple strategy: initializing with the original BERT and then resuming pre-training on a specialized corpus. This method yields rather good performance (e.g. BioBERT (Lee et al., 2020), SciBERT (Beltagy et al., 2019), BlueBERT (Peng et al., 2019)). However, it seems reasonable to think that training directly on a specialized corpus, using a specialized vocabulary, could result in more tailored embeddings and thus help performance. To test this hypothesis, we train BERT models from scratch using many configurations involving general and medical corpora. Based on evaluations using four different tasks, we find that the initial corpus only has a weak influence on the performance of BERT models when these are further pre-trained on a medical corpus.

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Universal Semantic Annotator: the First Unified API for WSD, SRL and Semantic Parsing
Riccardo Orlando | Simone Conia | Stefano Faralli | Roberto Navigli

In this paper, we present the Universal Semantic Annotator (USeA), which offers the first unified API for high-quality automatic annotations of texts in 100 languages through state-of-the-art systems for Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing. Together, such annotations can be used to provide users with rich and diverse semantic information, help second-language learners, and allow researchers to integrate explicit semantic knowledge into downstream tasks and real-world applications.

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D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research
Jan Philip Wahle | Terry Ruas | Saif Mohammad | Bela Gipp

DBLP is the largest open-access repository of scientific articles on computer science and provides metadata associated with publications, authors, and venues. We retrieved more than 6 million publications from DBLP and extracted pertinent metadata (e.g., abstracts, author affiliations, citations) from the publication texts to create the DBLP Discovery Dataset (D3). D3 can be used to identify trends in research activity, productivity, focus, bias, accessibility, and impact of computer science research. We present an initial analysis focused on the volume of computer science research (e.g., number of papers, authors, research activity), trends in topics of interest, and citation patterns. Our findings show that computer science is a growing research field (15% annually), with an active and collaborative researcher community. While papers in recent years present more bibliographical entries in comparison to previous decades, the average number of citations has been declining. Investigating papers’ abstracts reveals that recent topic trends are clearly reflected in D3. Finally, we list further applications of D3 and pose supplemental research questions. The D3 dataset, our findings, and source code are publicly available for research purposes.

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SciPar: A Collection of Parallel Corpora from Scientific Abstracts
Dimitrios Roussis | Vassilis Papavassiliou | Prokopis Prokopidis | Stelios Piperidis | Vassilis Katsouros

This paper presents SciPar, a new collection of parallel corpora created from openly available metadata of bachelor theses, master theses and doctoral dissertations hosted in institutional repositories, digital libraries of universities and national archives. We describe first how we harvested and processed metadata from 86, mainly European, repositories to extract bilingual titles and abstracts, and then how we mined high quality sentence pairs in a wide range of scientific areas and sub-disciplines. In total, the resource includes 9.17 million segment alignments in 31 language pairs and is publicly available via the ELRC-SHARE repository. The bilingual corpora in this collection could prove valuable in various applications, such as cross-lingual plagiarism detection or adapting Machine Translation systems for the translation of scientific texts and academic writing in general, especially for language pairs which include English.

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CATs are Fuzzy PETs: A Corpus and Analysis of Potentially Euphemistic Terms
Martha Gavidia | Patrick Lee | Anna Feldman | JIng Peng

Euphemisms have not received much attention in natural language processing, despite being an important element of polite and figurative language. Euphemisms prove to be a difficult topic, not only because they are subject to language change, but also because humans may not agree on what is a euphemism and what is not. Nonetheless, the first step to tackling the issue is to collect and analyze examples of euphemisms. We present a corpus of potentially euphemistic terms (PETs) along with example texts from the GloWbE corpus. Additionally, we present a subcorpus of texts where these PETs are not being used euphemistically, which may be useful for future applications. We also discuss the results of multiple analyses run on the corpus. Firstly, we find that sentiment analysis on the euphemistic texts supports that PETs generally decrease negative and offensive sentiment. Secondly, we observe cases of disagreement in an annotation task, where humans are asked to label PETs as euphemistic or not in a subset of our corpus text examples. We attribute the disagreement to a variety of potential reasons, including if the PET was a commonly accepted term (CAT).

We present the Camel Treebank (CAMELTB), a 188K word open-source dependency treebank of Modern Standard and Classical Arabic. CAMELTB 1.0 includes 13 sub-corpora comprising selections of texts from pre-Islamic poetry to social media online commentaries, and covering a range of genres from religious and philosophical texts to news, novels, and student essays. The texts are all publicly available (out of copyright, creative commons, or under open licenses). The texts were morphologically tokenized and syntactically parsed automatically, and then manually corrected by a team of trained annotators. The annotations follow the guidelines of the Columbia Arabic Treebank (CATiB) dependency representation. We discuss our annotation process and guideline extensions, and we present some initial observations on lexical and syntactic differences among the annotated sub-corpora. This corpus will be publicly available to support and encourage research on Arabic NLP in general and on new, previously unexplored genres that are of interest to a wider spectrum of researchers, from historical linguistics and digital humanities to computer-assisted language pedagogy.

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MentSum: A Resource for Exploring Summarization of Mental Health Online Posts
Sajad Sotudeh | Nazli Goharian | Zachary Young

Mental health remains a significant challenge of public health worldwide. With increasing popularity of online platforms, many use the platforms to share their mental health conditions, express their feelings, and seek help from the community and counselors. Some of these platforms, such as Reachout, are dedicated forums where the users register to seek help. Others such as Reddit provide subreddits where the users publicly but anonymously post their mental health distress. Although posts are of varying length, it is beneficial to provide a short, but informative summary for fast processing by the counselors. To facilitate research in summarization of mental health online posts, we introduce Mental Health Summarization dataset, MentSum, containing over 24k carefully selected user posts from Reddit, along with their short user-written summary (called TLDR) in English from 43 mental health subreddits. This domain-specific dataset could be of interest not only for generating short summaries on Reddit, but also for generating summaries of posts on the dedicated mental health forums such as Reachout. We further evaluate both extractive and abstractive state-of-the-art summarization baselines in terms of Rouge scores, and finally conduct an in-depth human evaluation study of both user-written and system-generated summaries, highlighting challenges in this research.

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Klexikon: A German Dataset for Joint Summarization and Simplification
Dennis Aumiller | Michael Gertz

Traditionally, Text Simplification is treated as a monolingual translation task where sentences between source texts and their simplified counterparts are aligned for training. However, especially for longer input documents, summarizing the text (or dropping less relevant content altogether) plays an important role in the simplification process, which is currently not reflected in existing datasets. Simultaneously, resources for non-English languages are scarce in general and prohibitive for training new solutions. To tackle this problem, we pose core requirements for a system that can jointly summarize and simplify long source documents. We further describe the creation of a new dataset for joint Text Simplification and Summarization based on German Wikipedia and the German children’s encyclopedia “Klexikon”, consisting of almost 2,900 documents. We release a document-aligned version that particularly highlights the summarization aspect, and provide statistical evidence that this resource is well suited to simplification as well. Code and data are available on Github: https://github.com/dennlinger/klexikon

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Applying Automatic Text Summarization for Fake News Detection
Philipp Hartl | Udo Kruschwitz

The distribution of fake news is not a new but a rapidly growing problem. The shift to news consumption via social media has been one of the drivers for the spread of misleading and deliberately wrong information, as in addition to its ease of use there is rarely any veracity monitoring. Due to the harmful effects of such fake news on society, the detection of these has become increasingly important. We present an approach to the problem that combines the power of transformer-based language models while simultaneously addressing one of their inherent problems. Our framework, CMTR-BERT, combines multiple text representations, with the goal of circumventing sequential limits and related loss of information the underlying transformer architecture typically suffers from. Additionally, it enables the incorporation of contextual information. Extensive experiments on two very different, publicly available datasets demonstrates that our approach is able to set new state-of-the-art performance benchmarks. Apart from the benefit of using automatic text summarization techniques we also find that the incorporation of contextual information contributes to performance gains.

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Increasing CMDI’s Semantic Interoperability with schema.org
Nino Meisinger | Thorsten Trippel | Claus Zinn

The CLARIN Concept Registry (CCR) is the common semantic ground for most CMDI-based profiles to describe language-related resources in the CLARIN universe. While the CCR supports semantic interoperability within this universe, it does not extend beyond it. The flexibility of CMDI, however, allows users to use other term or concept registries when defining their metadata components. In this paper, we describe our use of schema.org, a light ontology used by many parties across disciplines.

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RefCo and its Checker: Improving Language Documentation Corpora’s Reusability Through a Semi-Automatic Review Process
Herbert Lange | Jocelyn Aznar

The QUEST (QUality ESTablished) project aims at ensuring the reusability of audio-visual datasets (Wamprechtshammer et al., 2022) by devising quality criteria and curating processes. RefCo (Reference Corpora) is an initiative within QUEST in collaboration with DoReCo (Documentation Reference Corpus, Paschen et al. (2020)) focusing on language documentation projects. Previously, Aznar and Seifart (2020) introduced a set of quality criteria dedicated to documenting fieldwork corpora. Based on these criteria, we establish a semi-automatic review process for existing and work-in-progress corpora, in particular for language documentation. The goal is to improve the quality of a corpus by increasing its reusability. A central part of this process is a template for machine-readable corpus documentation and automatic data verification based on this documentation. In addition to the documentation and automatic verification, the process involves a human review and potentially results in a RefCo certification of the corpus. For each of these steps, we provide guidelines and manuals. We describe the evaluation process in detail, highlight the current limits for automatic evaluation and how the manual review is organized accordingly.

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Identification and Analysis of Personification in Hungarian: The PerSECorp project
Gábor Simon

Despite the recent findings on the conceptual and linguistic organization of personification, we have relatively little knowledge about its lexical patterns and grammatical templates. It is especially true in the case of Hungarian which has remained an understudied language regarding the constructions of figurative meaning generation. The present paper aims to provide a corpus-driven approach to personification analysis in the framework of cognitive linguistics. This approach is based on the building of a semi-automatically processed research corpus (the PerSE corpus) in which personifying linguistic structures are annotated manually. The present test version of the corpus consists of online car reviews written in Hungarian (10468 words altogether): the texts were tokenized, lemmatized, morphologically analyzed, syntactically parsed, and PoS-tagged with the e-magyar NLP tool. For the identification of personifications, the adaptation of the MIPVU protocol was used and combined with additional analysis of semantic relations within personifying multi-word expressions. The paper demonstrates the structure of the corpus as well as the levels of the annotation. Furthermore, it gives an overview of possible data types emerging from the analysis: lexical pattern, grammatical characteristics, and the construction-like behavior of personifications in Hungarian.

Discourse markers carry information about the discourse structure and organization, and also signal local dependencies or epistemological stance of speaker. They provide instructions on how to interpret the discourse, and their study is paramount to understand the mechanism underlying discourse organization. This paper presents a new language resource, an ISO-based annotated multilingual parallel corpus for discourse markers. The corpus comprises nine languages, Bulgarian, Lithuanian, German, European Portuguese, Hebrew, Romanian, Polish, and Macedonian, with English as a pivot language. In order to represent the meaning of the discourse markers, we propose an annotation scheme of discourse relations from ISO 24617-8 with a plug-in to ISO 24617-2 for communicative functions. We describe an experiment in which we applied the annotation scheme to assess its validity. The results reveal that, although some extensions are required to cover all the multilingual data, it provides a proper representation of discourse markers value. Additionally, we report some relevant contrastive phenomena concerning discourse markers interpretation and role in discourse. This first step will allow us to develop deep learning methods to identify and extract discourse relations and communicative functions, and to represent that information as Linguistic Linked Open Data (LLOD).

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LIP-RTVE: An Audiovisual Database for Continuous Spanish in the Wild
David Gimeno-Gómez | Carlos-D. Martínez-Hinarejos

Speech is considered as a multi-modal process where hearing and vision are two fundamentals pillars. In fact, several studies have demonstrated that the robustness of Automatic Speech Recognition systems can be improved when audio and visual cues are combined to represent the nature of speech. In addition, Visual Speech Recognition, an open research problem whose purpose is to interpret speech by reading the lips of the speaker, has been a focus of interest in the last decades. Nevertheless, in order to estimate these systems in the currently Deep Learning era, large-scale databases are required. On the other hand, while most of these databases are dedicated to English, other languages lack sufficient resources. Thus, this paper presents a semi-automatically annotated audiovisual database to deal with unconstrained natural Spanish, providing 13 hours of data extracted from Spanish television. Furthermore, baseline results for both speaker-dependent and speaker-independent scenarios are reported using Hidden Markov Models, a traditional paradigm that has been widely used in the field of Speech Technologies.

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Modality Alignment between Deep Representations for Effective Video-and-Language Learning
Hyeongu Yun | Yongil Kim | Kyomin Jung

Video-and-Language learning, such as video question answering or video captioning, is the next challenge in the deep learning society, as it pursues the way how human intelligence perceives everyday life. These tasks require the ability of multi-modal reasoning which is to handle both visual information and text information simultaneously across time. In this point of view, a cross-modality attention module that fuses video representation and text representation takes a critical role in most recent approaches. However, existing Video-and-Language models merely compute the attention weights without considering the different characteristics of video modality and text modality. Such na ̈ıve attention module hinders the current models to fully enjoy the strength of cross-modality. In this paper, we propose a novel Modality Alignment method that benefits the cross-modality attention module by guiding it to easily amalgamate multiple modalities. Specifically, we exploit Centered Kernel Alignment (CKA) which was originally proposed to measure the similarity between two deep representations. Our method directly optimizes CKA to make an alignment between video and text embedding representations, hence it aids the cross-modality attention module to combine information over different modalities. Experiments on real-world Video QA tasks demonstrate that our method outperforms conventional multi-modal methods significantly with +3.57% accuracy increment compared to the baseline in a popular benchmark dataset. Additionally, in a synthetic data environment, we show that learning the alignment with our method boosts the performance of the cross-modality attention.

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Mutual Gaze and Linguistic Repetition in a Multimodal Corpus
Anais Murat | Maria Koutsombogera | Carl Vogel

This paper investigates the correlation between mutual gaze and linguistic repetition, a form of alignment, which we take as evidence of mutual understanding. We focus on a multimodal corpus made of three-party conversations and explore the question of whether mutual gaze events correspond to moments of repetition or non-repetition. Our results, although mainly significant on word unigrams and bigrams, suggest positive correlations between the presence of mutual gaze and the repetitions of tokens, lemmas, or parts-of-speech, but negative correlations when it comes to paired levels of representation (tokens or lemmas associated with their part-of-speech). No compelling correlation is found with duration of mutual gaze. Results are strongest when ignoring punctuation as representations of pauses, intonation, etc. in counting aligned tokens.

In natural language settings, many interactions include more than two speakers, and real-life interpretation is based on all types of information available in all modalities. This constitutes a challenge for corpus-based analyses because the information in the audio and visual channels must be included in the coding. The goal of the DINLANG project is to tackle that challenge and analyze spontaneous interactions in family dinner settings (two adults and two to three children). The families use either French, or LSF (French sign language). Our aim is to compare how participants share language across the range of modalities found in vocal and visual languaging in coordination with dining. In order to pinpoint similarities and differences, we had to find a common coding tool for all situations (variations from one family to another) and modalities. Our coding procedure incorporates the use of the ELAN software. We created a template organized around participants, situations, and modalities, rather than around language forms. Spoken language transcription can be integrated, when it exists, but it is not mandatory. Data that has been created with another software can be injected in ELAN files if it is linked using time stamps. Analyses performed with the coded files rely on ELAN’s structured search functionalities, which allow to achieve fine-grained temporal analyses and which can be completed by using spreadsheets or R language.

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Constructing a Lexical Resource of Russian Derivational Morphology
Lukáš Kyjánek | Olga Lyashevskaya | Anna Nedoluzhko | Daniil Vodolazsky | Zdeněk Žabokrtský

Words of any language are to some extent related thought the ways they are formed. For instance, the verb ‘exempl-ify’ and the noun ‘example-s’ are both based on the word ‘example’, but the verb is derived from it, while the noun is inflected. In Natural Language Processing of Russian, the inflection is satisfactorily processed; however, there are only a few machine-trackable resources that capture derivations even though Russian has both of these morphological processes very rich. Therefore, we devote this paper to improving one of the methods of constructing such resources and to the application of the method to a Russian lexicon, which results in the creation of the largest lexical resource of Russian derivational relations. The resulting database dubbed DeriNet.RU includes more than 300 thousand lexemes connected with more than 164 thousand binary derivational relations. To create such data, we combined the existing machine-learning methods that we improved to manage this goal. The whole approach is evaluated on our newly created data set of manual, parallel annotation. The resulting DeriNet.RU is freely available under an open license agreement.

This paper describes a method to enrich lexical resources with content relating to linguistic diversity, based on knowledge from the field of lexical typology. We capture the phenomenon of diversity through the notion of lexical gap and use a systematic method to infer gaps semi-automatically on a large scale, which we demonstrate on the kinship domain. The resulting free diversity-aware terminological resource consists of 198 concepts, 1,911 words, and 37,370 gaps in 699 languages. We see great potential in the use of resources such as ours for the improvement of a variety of cross-lingual NLP tasks, which we illustrate through an application in the evaluation of machine translation systems.

In this paper we describe our current work on creating a WordNet for Latvian based on the principles of the Princeton WordNet. The chosen methodology for word sense definition and sense linking is based on corpus evidence and the existing Tezaurs.lv online dictionary, ensuring a foundation that fits the Latvian language usage and existing linguistic tradition. We cover a wide set of semantic relations, including gradation sets. Currently the dataset consists of 6432 words linked in 5528 synsets, out of which 2717 synsets are considered fully completed as they have all the outgoing semantic links annotated, annotated with corpus examples for each sense and links to the English Princeton WordNet.

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Building Sentiment Lexicons for Mainland Scandinavian Languages Using Machine Translation and Sentence Embeddings
Peng Liu | Cristina Marco | Jon Atle Gulla

This paper presents a simple but effective method to build sentiment lexicons for the three Mainland Scandinavian languages: Danish, Norwegian and Swedish. This method benefits from the English Sentiwordnet and a thesaurus in one of the target languages. Sentiment information from the English resource is mapped to the target languages by using machine translation and similarity measures based on sentence embeddings. A number of experiments with Scandinavian languages are performed in order to determine the best working sentence embedding algorithm for this task. A careful extrinsic evaluation on several datasets yields state-of-the-art results using a simple rule-based sentiment analysis algorithm. The resources are made freely available under an MIT License.

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A Thesaurus-based Sentiment Lexicon for Danish: The Danish Sentiment Lexicon
Sanni Nimb | Sussi Olsen | Bolette Pedersen | Thomas Troelsgård

This paper describes how a newly published Danish sentiment lexicon with a high lexical coverage was compiled by use of lexicographic methods and based on the links between groups of words listed in semantic order in a thesaurus and the corresponding word sense descriptions in a comprehensive monolingual dictionary. The overall idea was to identify negative and positive sections in a thesaurus, extract the words from these sections and combine them with the dictionary information via the links. The annotation task of the dataset included several steps, and was based on the comparison of synonyms and near synonyms within a semantic field. In the cases where one of the words were included in the smaller Danish sentiment lexicon AFINN, its value there was used as inspiration and expanded to the synonyms when appropriate. In order to obtain a more practical lexicon with overall polarity values at lemma level, all the senses of the lemma were afterwards compared, taking into consideration dictionary information such as usage, style and frequency. The final lexicon contains 13,859 Danish polarity lemmas and includes morphological information. It is freely available at https://github.com/dsldk/danish-sentiment-lexicon (licence CC-BY-SA 4.0 International).

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IndoUKC: A Concept-Centered Indian Multilingual Lexical Resource
Nandu Chandran Nair | Rajendran S. Velayuthan | Yamini Chandrashekar | Gábor Bella | Fausto Giunchiglia

We introduce the IndoUKC, a new multilingual lexical database comprised of eighteen Indian languages, with a focus on formally capturing words and word meanings specific to Indian languages and cultures. The IndoUKC reuses content from the existing IndoWordNet resource while providing a new model for the cross-lingual mapping of lexical meanings that allows for a richer, diversity-aware representation. Accordingly, beyond a thorough syntactic and semantic cleaning, the IndoWordNet lexical content has been thoroughly remodeled in order to allow a more precise expression of language-specific meaning. The resulting database is made available both for browsing through a graphical web interface and for download through the LiveLanguage data catalogue.

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Korean Language Modeling via Syntactic Guide
Hyeondey Kim | Seonhoon Kim | Inho Kang | Nojun Kwak | Pascale Fung

While pre-trained language models play a vital role in modern language processing tasks, but not every language can benefit from them. Most existing research on pre-trained language models focuses primarily on widely-used languages such as English, Chinese, and Indo-European languages. Additionally, such schemes usually require extensive computational resources alongside a large amount of data, which is infeasible for less-widely used languages. We aim to address this research niche by building a language model that understands the linguistic phenomena in the target language which can be trained with low-resources. In this paper, we discuss Korean language modeling, specifically methods for language representation and pre-training methods. With our Korean-specific language representation, we are able to build more powerful language models for Korean understanding, even with fewer resources. The paper proposes chunk-wise reconstruction of the Korean language based on a widely used transformer architecture and bidirectional language representation. We also introduce morphological features such as Part-of-Speech (PoS) into the language understanding by leveraging such information during the pre-training. Our experiment results prove that the proposed methods improve the model performance of the investigated Korean language understanding tasks.

Whole-person functional limitations in the areas of mobility, self-care and domestic life affect a majority of individuals with disabilities. Detecting, recording and monitoring such limitations would benefit those individuals, as well as research on whole-person functioning and general public health. Dictionaries of terms related to whole-person function would enable automated identification and extraction of relevant information. However, no such terminologies currently exist, due in part to a lack of standardized coding and their availability mainly in free text clinical notes. In this paper, we introduce terminologies of whole-person function in the domains of mobility, self-care and domestic life, built and evaluated using a small set of manually annotated clinical notes, which provided a seedset that was expanded using a mix of lexical and deep learning approaches.

The paper gives an account of an infrastructure that will be integrated into a platform aimed at providing a multi-faceted experience to visitors of Northern Greece using mythology as a starting point. This infrastructure comprises a multi-lingual and multi-modal corpus (i.e., a corpus of textual data supplemented with images, and video) that belongs to the humanities domain along with a dedicated database (content management system) with advanced indexing, linking and search functionalities. We will present the corpus itself focusing on the content, the methodology adopted for its development, and the steps taken towards rendering it accessible via the database in a way that also facilitates useful visualizations. In this context, we tried to address three main challenges: (a) to add a novel annotation layer, namely geotagging, (b) to ensure the long-term maintenance of and accessibility to the highly heterogeneous primary data – even after the life cycle of the current project – by adopting a metadata schema that is compatible to existing standards; and (c) to render the corpus a useful resource to scholarly research in the digital humanities by adding a minimum set of linguistic annotations.

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An Architecture of resolving a multiple link path in a standoff-style data format to enhance the mobility of language resources
Kazushi Ohya

The present data formats proposed by authentic organizations are based on a so-called standoff-style data format in XML, which represents a semantic data model through an instance structure and a link structure. However, this type of data formats intended to enhance the power of representation of an XML format injures the mobility of data because an abstract data structure denoted by multiple link paths is hard to be converted into other data structures. This difficulty causes a problem in the reuse of data to convert into other data formats especially in a personal data management environment. In this paper, in order to compensate for the drawback, we propose a new concept of transforming a link structure to an instance structure on a new marked-up scheme. This approach to language data brings a new architecture of language data management to realize a personal data management environment in daily and long-life use.

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A Corpus of German Citizen Contributions in Mobility Planning: Supporting Evaluation Through Multidimensional Classification
Julia Romberg | Laura Mark | Tobias Escher

Political authorities in democratic countries regularly consult the public in order to allow citizens to voice their ideas and concerns on specific issues. When trying to evaluate the (often large number of) contributions by the public in order to inform decision-making, authorities regularly face challenges due to restricted resources. We identify several tasks whose automated support can help in the evaluation of public participation. These are i) the recognition of arguments, more precisely premises and their conclusions, ii) the assessment of the concreteness of arguments, iii) the detection of textual descriptions of locations in order to assign citizens’ ideas to a spatial location, and iv) the thematic categorization of contributions. To enable future research efforts to develop techniques addressing these four tasks, we introduce the CIMT PartEval Corpus, a new publicly-available German-language corpus that includes several thousand citizen contributions from six mobility-related planning processes in five German municipalities. The corpus provides annotations for each of these tasks which have not been available in German for the domain of public participation before either at all or in this scope and variety.

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Overlooked Data in Typological Databases: What Grambank Teaches Us About Gaps in Grammars
Jakob Lesage | Hannah J. Haynie | Hedvig Skirgård | Tobias Weber | Alena Witzlack-Makarevich

Typological databases can contain a wealth of information beyond the collection of linguistic properties across languages. This paper shows how information often overlooked in typological databases can inform the research community about the state of description of the world’s languages. We illustrate this using Grambank, a morphosyntactic typological database covering 2,467 language varieties and based on 3,951 grammatical descriptions. We classify and quantify the comments that accompany coded values in Grambank. We then aggregate these comments and the coded values to derive a level of description for 17 grammatical domains that Grambank covers (negation, adnominal modification, participant marking, tense, aspect, etc.). We show that the description level of grammatical domains varies across space and time. Information about gaps and uncertainties in the descriptive knowledge of grammatical domains within and across languages is essential for a correct analysis of data in typological databases and for the study of grammatical diversity more generally. When collected in a database, such information feeds into disciplines that focus on primary data collection, such as grammaticography and language documentation.

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Hong Kong: Longitudinal and Synchronic Characterisations of Protest News between 1998 and 2020
Arya D. McCarthy | Giovanna Maria Dora Dore

This paper showcases the utility and timeliness of the Hong Kong Protest News Dataset, a highly curated collection of news articles from diverse news sources, to investigate longitudinal and synchronic news characterisations of protests in Hong Kong between 1998 and 2020. The properties of the dataset enable us to apply natural language processing to its 4522 articles and thereby study patterns of journalistic practice across newspapers. This paper sheds light on whether depth and/or manner of reporting changed over time, and if so, in what ways, or in response to what. In its focus and methodology, this paper helps bridge the gap between “validity-focused methodological debates” and the use of computational methods of analysis in the social sciences.

This paper is based on a collection of 16th century letters from and to the Zurich reformer Heinrich Bullinger. Around 12,000 letters of this exchange have been preserved, out of which 3100 have been professionally edited, and another 5500 are available as provisional transcriptions. We have investigated code-switching in these 8600 letters, first on the sentence-level and then on the word-level. In this paper we give an overview of the corpus and its language mix (mostly Early New High German and Latin, but also French, Greek, Italian and Hebrew). We report on our experiences with a popular language identifier and present our results when training an alternative identifier on a very small training corpus of only 150 sentences per language. We use the automatically labeled sentences in order to bootstrap a word-based language classifier which works with high accuracy. Our research around the corpus building and annotation involves automatic handwritten text recognition, text normalisation for ENH German, and machine translation from medieval Latin into modern German.

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Quality and Efficiency of Manual Annotation: Pre-annotation Bias
Marie Mikulová | Milan Straka | Jan Štěpánek | Barbora Štěpánková | Jan Hajic

This paper presents an analysis of annotation using an automatic pre-annotation for a mid-level annotation complexity task - dependency syntax annotation. It compares the annotation efforts made by annotators using a pre-annotated version (with a high-accuracy parser) and those made by fully manual annotation. The aim of the experiment is to judge the final annotation quality when pre-annotation is used. In addition, it evaluates the effect of automatic linguistically-based (rule-formulated) checks and another annotation on the same data available to the annotators, and their influence on annotation quality and efficiency. The experiment confirmed that the pre-annotation is an efficient tool for faster manual syntactic annotation which increases the consistency of the resulting annotation without reducing its quality.

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A Comprehensive Evaluation and Correction of the TimeBank Corpus
Mustafa Ocal | Antonela Radas | Jared Hummer | Karine Megerdoomian | Mark Finlayson

TimeML is an annotation scheme for capturing temporal information in text. The developers of TimeML built the TimeBank corpus to both validate the scheme and provide a rich dataset of events, temporal expressions, and temporal relationships for training and testing temporal analysis systems. In our own work we have been developing methods aimed at TimeML graphs for detecting (and eventually automatically correcting) temporal inconsistencies, extracting timelines, and assessing temporal indeterminacy. In the course of this investigation we identified numerous previously unrecognized issues in the TimeBank corpus, including multiple violations of TimeML annotation guide rules, incorrectly disconnected temporal graphs, as well as inconsistent, redundant, missing, or otherwise incorrect annotations. We describe our methods for detecting and correcting these problems, which include: (a) automatic guideline checking (109 violations); (b) automatic inconsistency checking (65 inconsistent files); (c) automatic disconnectivity checking (625 incorrect breakpoints); and (d) manual comparison with the output of state-of-the-art automatic annotators to identify missing annotations (317 events, 52 temporal expressions). We provide our code as well as a set of patch files that can be applied to the TimeBank corpus to produce a corrected version for use by other researchers in the field.

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Evaluating Multilingual Sentence Representation Models in a Real Case Scenario
Rocco Tripodi | Rexhina Blloshmi | Simon Levis Sullam

In this paper, we present an evaluation of sentence representation models on the paraphrase detection task. The evaluation is designed to simulate a real-world problem of plagiarism and is based on one of the most important cases of forgery in modern history: the so-called “Protocols of the Elders of Zion”. The sentence pairs for the evaluation are taken from the infamous forged text “Protocols of the Elders of Zion” (Protocols) by unknown authors; and by “Dialogue in Hell between Machiavelli and Montesquieu” by Maurice Joly. Scholars have demonstrated that the first text plagiarizes from the second, indicating all the forged parts on qualitative grounds. Following this evidence, we organized the rephrased texts and asked native speakers to quantify the level of similarity between each pair. We used this material to evaluate sentence representation models in two languages: English and French, and on three tasks: similarity correlation, paraphrase identification, and paraphrase retrieval. Our evaluation aims at encouraging the development of benchmarks based on real-world problems, as a means to prevent problems connected to AI hypes, and to use NLP technologies for social good. Through our evaluation, we are able to confirm that the infamous Protocols are actually a plagiarized text but, as we will show, we encounter several problems connected with the convoluted nature of the task, that is very different from the one reported in standard benchmarks of paraphrase detection and sentence similarity. Code and data available at https://github.com/roccotrip/protocols.

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Validity, Agreement, Consensuality and Annotated Data Quality
Anaëlle Baledent | Yann Mathet | Antoine Widlöcher | Christophe Couronne | Jean-Luc Manguin

Reference annotated (or gold-standard) datasets are required for various common tasks such as training for machine learning systems or system validation. They are necessary to analyse or compare occurrences or items annotated by experts, or to compare objects resulting from any computational process to objects annotated by experts. But, even if reference annotated gold-standard corpora are required, their production is known as a difficult problem, from both a theoretical and practical point of view. Many studies devoted to theses issues conclude that multi-annotation is most of the time a necessity. That inter-annotator agreement measure, which is required to check the reliability of data and the reproducibility of an annotation task, and thus to establish a gold standard, is another thorny problem. Fine analysis of available metrics for this specific task then becomes essential. Our work is part of this effort and more precisely focuses on several problems, which are rarely discussed, although they are intrinsically linked with the interpretation of metrics. In particular, we focus here on the complex relations between agreement and reference (of which agreement among annotators is supposed to be an indicator), and the emergence of consensus. We also introduce the notion of consensuality as another relevant indicator.

Pretrained models through self-supervised learning have been recently introduced for both acoustic and language modeling. Applied to spoken language understanding tasks, these models have shown their great potential by improving the state-of-the-art performances on challenging benchmark datasets. In this paper, we present an error analysis reached by the use of such models on the French MEDIA benchmark dataset, known as being one of the most challenging benchmarks for the slot filling task among all the benchmarks accessible to the entire research community. One year ago, the state-of-art system reached a Concept Error Rate (CER) of 13.6% through the use of a end-to-end neural architecture. Some months later, a cascade approach based on the sequential use of a fine-tuned wav2vec2.0 model and a fine-tuned BERT model reaches a CER of 11.2%. This significant improvement raises questions about the type of errors that remain difficult to treat, but also about those that have been corrected using these models pre-trained through self-supervision learning on a large amount of data. This study brings some answers in order to better understand the limits of such models and open new perspectives to continue improving the performance.

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JGLUE: Japanese General Language Understanding Evaluation
Kentaro Kurihara | Daisuke Kawahara | Tomohide Shibata

To develop high-performance natural language understanding (NLU) models, it is necessary to have a benchmark to evaluate and analyze NLU ability from various perspectives. While the English NLU benchmark, GLUE, has been the forerunner, benchmarks are now being released for languages other than English, such as CLUE for Chinese and FLUE for French; but there is no such benchmark for Japanese. We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.

A popular idea in Computer Assisted Language Learning (CALL) is to use multimodal annotated texts, with annotations typically including embedded audio and translations, to support L2 learning through reading. An important question is how to create good quality audio, which can be done either through human recording or by a Text-To-Speech (TTS) engine. We may reasonably expect TTS to be quicker and easier, but human to be of higher quality. Here, we report a study using the open source LARA platform and ten languages. Samples of audio totalling about five minutes, representing the same four passages taken from LARA versions of Saint-Exupèry’s “Le petit prince”, were provided for each language in both human and TTS form; the passages were chosen to instantiate the 2x2 cross product of the conditions dialogue, not-dialogue and humour, not-humour. 251 subjects used a web form to compare human and TTS versions of each item and rate the voices as a whole. For the three languages where TTS did best, English, French and Irish, the evidence from this study and the previous one it extended suggest that TTS audio is now pedagogically adequate and roughly comparable with a non-professional human voice in terms of exemplifying correct pronunciation and prosody. It was however still judged substantially less natural and less pleasant to listen to. No clear evidence was found to support the hypothesis that dialogue and humour pose special problems for TTS. All data and software will be made freely available.

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Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations
Chris Emmery | Ákos Kádár | Grzegorz Chrupała | Walter Daelemans

A limited amount of studies investigates the role of model-agnostic adversarial behavior in toxic content classification. As toxicity classifiers predominantly rely on lexical cues, (deliberately) creative and evolving language-use can be detrimental to the utility of current corpora and state-of-the-art models when they are deployed for content moderation. The less training data is available, the more vulnerable models might become. This study is, to our knowledge, the first to investigate the effect of adversarial behavior and augmentation for cyberbullying detection. We demonstrate that model-agnostic lexical substitutions significantly hurt classifier performance. Moreover, when these perturbed samples are used for augmentation, we show models become robust against word-level perturbations at a slight trade-off in overall task performance. Augmentations proposed in prior work on toxicity prove to be less effective. Our results underline the need for such evaluations in online harm areas with small corpora.

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Constructing Distributions of Variation in Referring Expression Type from Corpora for Model Evaluation
T. Mark Ellison | Fahime Same

The generation of referring expressions (REs) is a non-deterministic task. However, the algorithms for the generation of REs are standardly evaluated against corpora of written texts which include only one RE per each reference. Our goal in this work is firstly to reproduce one of the few studies taking the distributional nature of the RE generation into account. We add to this work, by introducing a method for exploring variation in human RE choice on the basis of longitudinal corpora - substantial corpora with a single human judgement (in the process of composition) per RE. We focus on the prediction of RE types, proper name, description and pronoun. We compare evaluations made against distributions over these types with evaluations made against parallel human judgements. Our results show agreement in the evaluation of learning algorithms against distributions constructed from parallel human evaluations and from longitudinal data.

Data-driven systems need to be evaluated to establish trust in the scientific approach and its applicability. In particular, this is true for Knowledge Graph (KG) Question Answering (QA), where complex data structures are made accessible via natural-language interfaces. Evaluating the capabilities of these systems has been a driver for the community for more than ten years while establishing different KGQA benchmark datasets. However, comparing different approaches is cumbersome. The lack of existing and curated leaderboards leads to a missing global view over the research field and could inject mistrust into the results. In particular, the latest and most-used datasets in the KGQA community, LC-QuAD and QALD, miss providing central and up-to-date points of trust. In this paper, we survey and analyze a wide range of evaluation results with significant coverage of 100 publications and 98 systems from the last decade. We provide a new central and open leaderboard for any KGQA benchmark dataset as a focal point for the community - https://kgqa.github.io/leaderboard/. Our analysis highlights existing problems during the evaluation of KGQA systems. Thus, we will point to possible improvements for future evaluations.

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Multi-Task Learning for Cross-Lingual Abstractive Summarization
Sho Takase | Naoaki Okazaki

We present a multi-task learning framework for cross-lingual abstractive summarization to augment training data. Recent studies constructed pseudo cross-lingual abstractive summarization data to train their neural encoder-decoders. Meanwhile, we introduce existing genuine data such as translation pairs and monolingual abstractive summarization data into training. Our proposed method, Transum, attaches a special token to the beginning of the input sentence to indicate the target task. The special token enables us to incorporate the genuine data into the training data easily. The experimental results show that Transum achieves better performance than the model trained with only pseudo cross-lingual summarization data. In addition, we achieve the top ROUGE score on Chinese-English and Arabic-English abstractive summarization. Moreover, Transum also has a positive effect on machine translation. Experimental results indicate that Transum improves the performance from the strong baseline, Transformer, in Chinese-English, Arabic-English, and English-Japanese translation datasets.

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How Much Context Span is Enough? Examining Context-Related Issues for Document-level MT
Sheila Castilho

This paper analyses how much context span is necessary to solve different context-related issues, namely, reference, ellipsis, gender, number, lexical ambiguity, and terminology when translating from English into Portuguese. We use the DELA corpus, which consists of 60 documents and six different domains (subtitles, literary, news, reviews, medical, and legislation). We find that the shortest context span to disambiguate issues can appear in different positions in the document including preceding, following, global, world knowledge. Moreover, the average length depends on the issue types as well as the domain. Moreover, we show that the standard approach of relying on only two preceding sentences as context might not be enough depending on the domain and issue types.

Document-level Neural Machine Translation aims to increase the quality of neural translation models by taking into account contextual information. Properly modelling information beyond the sentence level can result in improved machine translation output in terms of coherence, cohesion and consistency. Suitable corpora for context-level modelling are necessary to both train and evaluate context-aware systems, but are still relatively scarce. In this work we describe TANDO, a document-level corpus for the under-resourced Basque-Spanish language pair, which we share with the scientific community. The corpus is composed of parallel data from three different domains and has been prepared with context-level information. Additionally, the corpus includes contrastive test sets for fine-grained evaluations of gender and register contextual phenomena on both source and target language sides. To establish the usefulness of the corpus, we trained and evaluated baseline Transformer models and context-aware variants based on context concatenation. Our results indicate that the corpus is suitable for fine-grained evaluation of document-level machine translation systems.

In this work, we present the work that has been carried on in the MT4All CEF project and the resources that it has generated by leveraging recent research carried out in the field of unsupervised learning. In the course of the project 18 monolingual corpora for specific domains and languages have been collected, and 12 bilingual dictionaries and translation models have been generated. As part of the research, the unsupervised MT methodology based only on monolingual corpora (Artetxe et al., 2017) has been tested on a variety of languages and domains. Results show that in specialised domains, when there is enough monolingual in-domain data, unsupervised results are comparable to those of general domain supervised translation, and that, at any rate, unsupervised techniques can be used to boost results whenever very little data is available.

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COVID-19 Mythbusters in World Languages
Mana Ashida | Jin-Dong Kim | Seunghun Lee

This paper introduces a multi-lingual database containing translated texts of COVID-19 mythbusters. The database has translations into 115 languages as well as the original English texts, of which the original texts are published by World Health Organization (WHO). This paper then presents preliminary analyses on latin-alphabet-based texts to see the potential of the database as a resource for multilingual linguistic analyses. The analyses on latin-alphabet-based texts gave interesting insights into the resource. While the amount of translated texts in each language was small, character bi-grams with normalization (lowercasing and removal of diacritics) was turned out to be an effective proxy for measuring the similarity of the languages, and the affinity ranking of language pairs could be obtained. Additionally, the hierarchical clustering analysis is performed using the character bigram overlap ratio of every possible pair of languages. The result shows the cluster of Germanic languages, Romance languages, and Southern Bantu languages. In sum, the multilingual database not only offers fixed set of materials in numerous languages, but also serves as a preliminary tool to identify the language family using text-based similarity measure of bigram overlap ratio.

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On the Multilingual Capabilities of Very Large-Scale English Language Models
Jordi Armengol-Estapé | Ona de Gibert Bonet | Maite Melero

Generative Pre-trained Transformers (GPTs) have recently been scaled to unprecedented sizes in the history of machine learning. These models, solely trained on the language modeling objective, have been shown to exhibit outstanding zero, one, and few-shot learning capabilities in a number of different tasks. Nevertheless, aside from anecdotal experiences, little is known regarding their multilingual capabilities, given the fact that the pre-training corpus is almost entirely composed of English text. In this work, we investigate its potential and limits in three tasks: extractive question-answering, text summarization and natural language generation for five different languages, as well as the effect of scale in terms of model size. Our results show that GPT-3 can be almost as useful for many languages as it is for English, with room for improvement if optimization of the tokenization is addressed.

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Evaluating Subtitle Segmentation for End-to-end Generation Systems
Alina Karakanta | François Buet | Mauro Cettolo | François Yvon

Subtitles appear on screen as short pieces of text, segmented based on formal constraints (length) and syntactic/semantic criteria. Subtitle segmentation can be evaluated with sequence segmentation metrics against a human reference. However, standard segmentation metrics cannot be applied when systems generate outputs different than the reference, e.g. with end-to-end subtitling systems. In this paper, we study ways to conduct reference-based evaluations of segmentation accuracy irrespective of the textual content. We first conduct a systematic analysis of existing metrics for evaluating subtitle segmentation. We then introduce Sigma, a Subtitle Segmentation Score derived from an approximate upper-bound of BLEU on segmentation boundaries, which allows us to disentangle the effect of good segmentation from text quality. To compare Sigma with existing metrics, we further propose a boundary projection method from imperfect hypotheses to the true reference. Results show that all metrics are able to reward high quality output but for similar outputs system ranking depends on each metric’s sensitivity to error type. Our thorough analyses suggest Sigma is a promising segmentation candidate but its reliability over other segmentation metrics remains to be validated through correlations with human judgements.

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Using Semantic Role Labeling to Improve Neural Machine Translation
Reinhard Rapp

Despite impressive progress in machine translation in recent years, it has occasionally been argued that current systems are still mainly based on pattern recognition and that further progress may be possible by using text understanding techniques, thereby e.g. looking at semantics of the type “Who is doing what to whom?”. In the current research we aim to take a small step into this direction. Assuming that semantic role labeling (SRL) grasps some of the relevant semantics, we automatically annotate the source language side of a standard parallel corpus, namely Europarl, with semantic roles. We then train a neural machine translation (NMT) system using the annotated corpus on the source language side, and the original unannotated corpus on the target language side. New text to be translated is first annotated by the same SRL system and then fed into the translation system. We compare the results to those of a baseline NMT system trained with unannotated text on both sides and find that the SRL-based system yields small improvements in terms of BLEU scores for each of the four language pairs under investigation, involving English, French, German, Greek and Spanish.

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A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference
Dibyanayan Bandyopadhyay | Arkadipta De | Baban Gain | Tanik Saikh | Asif Ekbal

Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), has been one of the central tasks in Artificial Intelligence (AI) and Natural Language Processing (NLP). RTE between the two pieces of texts is a crucial problem, and it adds further challenges when involving two different languages, i.e., in the cross-lingual scenario. This paper proposes an effective transfer learning approach for cross-lingual NLI. We perform experiments on English-Hindi language pairs in the cross-lingual setting to find out that our novel loss formulation could enhance the performance of the baseline model by up to 2%. To assess the effectiveness of our method further, we perform additional experiments on every possible language pair using four European languages, namely French, German, Bulgarian, and Turkish, on top of XNLI dataset. Evaluation results yield up to 10% performance improvement over the respective baseline models, in some cases surpassing the state-of-the-art (SOTA). It is also to be noted that our proposed model has 110M parameters which is much lesser than the SOTA model having 220M parameters. Finally, we argue that our transfer learning-based loss objective is model agnostic and thus can be used with other deep learning-based architectures for cross-lingual NLI.

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Simple TICO-19: A Dataset for Joint Translation and Simplification of COVID-19 Texts
Matthew Shardlow | Fernando Alva-Manchego

Specialist high-quality information is typically first available in English, and it is written in a language that may be difficult to understand by most readers. While Machine Translation technologies contribute to mitigate the first issue, the translated content will most likely still contain complex language. In order to investigate and address both problems simultaneously, we introduce Simple TICO-19, a new language resource containing manual simplifications of the English and Spanish portions of the TICO-19 corpus for Machine Translation of COVID-19 literature. We provide an in-depth description of the annotation process, which entailed designing an annotation manual and employing four annotators (two native English speakers and two native Spanish speakers) who simplified over 6,000 sentences from the English and Spanish portions of the TICO-19 corpus. We report several statistics on the new dataset, focusing on analysing the improvements in readability from the original texts to their simplified versions. In addition, we propose baseline methodologies for automatically generating the simplifications, translations and joint translation and simplifications contained in our dataset.

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Building Comparable Corpora for Assessing Multi-Word Term Alignment
Omar Adjali | Emmanuel Morin | Pierre Zweigenbaum

Recent work has demonstrated the importance of dealing with Multi-Word Terms (MWTs) in several Natural Language Processing applications. In particular, MWTs pose serious challenges for alignment and machine translation systems because of their syntactic and semantic properties. Thus, developing algorithms that handle MWTs is becoming essential for many NLP tasks. However, the availability of bilingual and more generally multi-lingual resources is limited, especially for low-resourced languages and in specialized domains. In this paper, we propose an approach for building comparable corpora and bilingual term dictionaries that help evaluate bilingual term alignment in comparable corpora. To that aim, we exploit parallel corpora to perform automatic bilingual MWT extraction and comparable corpus construction. Parallel information helps to align bilingual MWTs and makes it easier to build comparable specialized sub-corpora. Experimental validation on an existing dataset and on manually annotated data shows the interest of the proposed methodology.

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Mean Machine Translations: On Gender Bias in Icelandic Machine Translations
Agnes Sólmundsdóttir | Dagbjört Guðmundsdóttir | Lilja Björk Stefánsdóttir | Anton Ingason

This paper examines machine bias in language technology. Machine bias can affect machine learning algorithms when language models trained on large corpora include biased human decisions or reflect historical or social inequities, e.g. regarding gender and race. The focus of the paper is on gender bias in machine translation and we discuss a study conducted on Icelandic translations in the translation systems Google Translate and Vélþýðing.is. The results show a pattern which corresponds to certain societal ideas about gender. For example it seems to depend on the meaning of adjectives referring to people whether they appear in the masculine or feminine form. Adjectives describing positive personality traits were more likely to appear in masculine gender whereas the negative ones frequently appear in feminine gender. However, the opposite applied to appearance related adjectives. These findings unequivocally demonstrate the importance of being vigilant towards technology so as not to maintain societal inequalities and outdated views — especially in today’s digital world.

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An Analysis of Dialogue Act Sequence Similarity Across Multiple Domains
Ayesha Enayet | Gita Sukthankar

This paper presents an analysis of how dialogue act sequences vary across different datasets in order to anticipate the potential degradation in the performance of learned models during domain adaptation. We hypothesize the following: 1) dialogue sequences from related domains will exhibit similar n-gram frequency distributions 2) this similarity can be expressed by measuring the average Hamming distance between subsequences drawn from different datasets. Our experiments confirm that when dialogue acts sequences from two datasets are dissimilar they lie further away in embedding space, making it possible to train a classifier to discriminate between them even when the datasets are corrupted with noise. We present results from eight different datasets: SwDA, AMI (DialSum), GitHub, Hate Speech, Teams, Diplomacy Betrayal, SAMsum, and Military (Army). Our datasets were collected from many types of human communication including strategic planning, informal discussion, and social media exchanges. Our methodology provides intuition on the generalizability of dialogue models trained on different datasets. Based on our analysis, it is problematic to assume that machine learning models trained on one type of discourse will generalize well to other settings, due to contextual differences.

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Constructing a Culinary Interview Dialogue Corpus with Video Conferencing Tool
Taro Okahisa | Ribeka Tanaka | Takashi Kodama | Yin Jou Huang | Sadao Kurohashi

Interview is an efficient way to elicit knowledge from experts of different domains. In this paper, we introduce CIDC, an interview dialogue corpus in the culinary domain in which interviewers play an active role to elicit culinary knowledge from the cooking expert. The corpus consists of 308 interview dialogues (each about 13 minutes in length), which add up to a total of 69,000 utterances. We use a video conferencing tool for data collection, which allows us to obtain the facial expressions of the interlocutors as well as the screen-sharing contents. To understand the impact of the interlocutors’ skill level, we divide the experts into “semi-professionals’” and “enthusiasts” and the interviewers into “skilled interviewers” and “unskilled interviewers.” For quantitative analysis, we report the statistics and the results of the post-interview questionnaire. We also conduct qualitative analysis on the collected interview dialogues and summarize the salient patterns of how interviewers elicit knowledge from the experts. The corpus serves the purpose to facilitate future research on the knowledge elicitation mechanism in interview dialogues.

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UgChDial: A Uyghur Chat-based Dialogue Corpus for Response Space Classification
Zulipiye Yusupujiang | Jonathan Ginzburg

In this paper, we introduce a carefully designed and collected language resource: UgChDial – a Uyghur dialogue corpus based on a chatroom environment. The Uyghur Chat-based Dialogue Corpus (UgChDial) is divided into two parts: (1). Two-party dialogues and (2). Multi-party dialogues. We ran a series of 25, 120-minutes each, two-party chat sessions, totaling 7323 turns and 1581 question-response pairs. We created 16 different scenarios and topics to gather these two-party conversations. The multi-party conversations were compiled from chitchats in general channels as well as free chats in topic-oriented public channels, yielding 5588 unique turns and 838 question-response pairs. The initial purpose of this corpus is to study query-response pairs in Uyghur, building on an existing fine-grained response space taxonomy for English. We provide here initial annotation results on the Uyghur response space classification task using UgChDial.

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A Speculative and Tentative Common Ground Handling for Efficient Composition of Uncertain Dialogue
Saki Sudo | Kyoshiro Asano | Koh Mitsuda | Ryuichiro Higashinaka | Yugo Takeuchi

This study investigates how the grounding process is composed and explores new interaction approaches that adapt to human cognitive processes that have not yet been significantly studied. The results of an experiment indicate that grounding through dialogue is mutually accepted among participants through holistic expressions and suggest that common ground among participants may not necessarily be formed in a bottom-up way through analytic expressions. These findings raise the possibility of a promising new approach to creating a human-like dialogue system that may be more suitable for natural human communication.

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BaSCo: An Annotated Basque-Spanish Code-Switching Corpus for Natural Language Understanding
Maia Aguirre | Laura García-Sardiña | Manex Serras | Ariane Méndez | Jacobo López

The main objective of this work is the elaboration and public release of BaSCo, the first corpus with annotated linguistic resources encompassing Basque-Spanish code-switching. The mixture of Basque and Spanish languages within the same utterance is popularly referred to as Euskañol, a widespread phenomenon among bilingual speakers in the Basque Country. Thus, this corpus has been created to meet the demand of annotated linguistic resources in Euskañol in research areas such as multilingual dialogue systems. The presented resource is the result of translating to Euskañol a compilation of texts in Basque and Spanish that were used for training the Natural Language Understanding (NLU) models of several task-oriented bilingual chatbots. Those chatbots were meant to answer specific questions associated with the administration, fiscal, and transport domains. In addition, they had the transverse potential to answer to greetings, requests for help, and chit-chat questions asked to chatbots. BaSCo is a compendium of 1377 tagged utterances with every sample annotated at three levels: (i) NLU semantic labels, considering intents and entities, (ii) code-switching proportion, and (iii) domain of origin.

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ProDial – An Annotated Proactive Dialogue Act Corpus for Conversational Assistants using Crowdsourcing
Matthias Kraus | Nicolas Wagner | Wolfgang Minker

Robots will eventually enter our daily lives and assist with a variety of tasks. Especially in the household domain, robots may become indispensable helpers by overtaking tedious tasks, e.g. keeping the place tidy. Their effectiveness and efficiency, however, depend on their ability to adapt to our needs, routines, and personal characteristics. Otherwise, they may not be accepted and trusted in our private domain. For enabling adaptation, the interaction between a human and a robot needs to be personalized. Therefore, the robot needs to collect personal information from the user. However, it is unclear how such sensitive data can be collected in an understandable way without losing a user’s trust in the system. In this paper, we present a conversational approach for explicitly collecting personal user information using natural dialogue. For creating a sound interactive personalization, we have developed an empathy-augmented dialogue strategy. In an online study, the empathy-augmented strategy was compared to a baseline dialogue strategy for interactive personalization. We have found the empathy-augmented strategy to perform notably friendlier. Overall, using dialogue for interactive personalization has generally shown positive user reception.

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ELITR Minuting Corpus: A Novel Dataset for Automatic Minuting from Multi-Party Meetings in English and Czech
Anna Nedoluzhko | Muskaan Singh | Marie Hledíková | Tirthankar Ghosal | Ondřej Bojar

Taking minutes is an essential component of every meeting, although the goals, style, and procedure of this activity (“minuting” for short) can vary. Minuting is a rather unstructured writing activity and is affected by who is taking the minutes and for whom the intended minutes are. With the rise of online meetings, automatic minuting would be an important benefit for the meeting participants as well as for those who might have missed the meeting. However, automatically generating meeting minutes is a challenging problem due to a variety of factors including the quality of automatic speech recorders (ASRs), availability of public meeting data, subjective knowledge of the minuter, etc. In this work, we present the first of its kind dataset on Automatic Minuting. We develop a dataset of English and Czech technical project meetings which consists of transcripts generated from ASRs, manually corrected, and minuted by several annotators. Our dataset, AutoMin, consists of 113 (English) and 53 (Czech) meetings, covering more than 160 hours of meeting content. Upon acceptance, we will publicly release (aaa.bbb.ccc) the dataset as a set of meeting transcripts and minutes, excluding the recordings for privacy reasons. A unique feature of our dataset is that most meetings are equipped with more than one minute, each created independently. Our corpus thus allows studying differences in what people find important while taking the minutes. We also provide baseline experiments for the community to explore this novel problem further. To the best of our knowledge AutoMin is probably the first resource on minuting in English and also in a language other than English (Czech).

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Extracting Age-Related Stereotypes from Social Media Texts
Kathleen C. Fraser | Svetlana Kiritchenko | Isar Nejadgholi

Age-related stereotypes are pervasive in our society, and yet have been under-studied in the NLP community. Here, we present a method for extracting age-related stereotypes from Twitter data, generating a corpus of 300,000 over-generalizations about four contemporary generations (baby boomers, generation X, millennials, and generation Z), as well as “old” and “young” people more generally. By employing word-association metrics, semi-supervised topic modelling, and density-based clustering, we uncover many common stereotypes as reported in the media and in the psychological literature, as well as some more novel findings. We also observe trends consistent with the existing literature, namely that definitions of “young” and “old” age appear to be context-dependent, stereotypes for different generations vary across different topics (e.g., work versus family life), and some age-based stereotypes are distinct from generational stereotypes. The method easily extends to other social group labels, and therefore can be used in future work to study stereotypes of different social categories. By better understanding how stereotypes are formed and spread, and by tracking emerging stereotypes, we hope to eventually develop mitigating measures against such biased statements.

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Borrowing or Codeswitching? Annotating for Finer-Grained Distinctions in Language Mixing
Elena Alvarez-Mellado | Constantine Lignos

We present a new corpus of Twitter data annotated for codeswitching and borrowing between Spanish and English. The corpus contains 9,500 tweets annotated at the token level with codeswitches, borrowings, and named entities. This corpus differs from prior corpora of codeswitching in that we attempt to clearly define and annotate the boundary between codeswitching and borrowing and do not treat common “internet-speak” (lol, etc.) as codeswitching when used in an otherwise monolingual context. The result is a corpus that enables the study and modeling of Spanish-English borrowing and codeswitching on Twitter in one dataset. We present baseline scores for modeling the labels of this corpus using Transformer-based language models. The annotation itself is released with a CC BY 4.0 license, while the text it applies to is distributed in compliance with the Twitter terms of service.

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Multi-Aspect Transfer Learning for Detecting Low Resource Mental Disorders on Social Media
Ana Sabina Uban | Berta Chulvi | Paolo Rosso

Mental disorders are a serious and increasingly relevant public health issue. NLP methods have the potential to assist with automatic mental health disorder detection, but building annotated datasets for this task can be challenging; moreover, annotated data is very scarce for disorders other than depression. Understanding the commonalities between certain disorders is also important for clinicians who face the problem of shifting standards of diagnosis. We propose that transfer learning with linguistic features can be useful for approaching both the technical problem of improving mental disorder detection in the context of data scarcity, and the clinical problem of understanding the overlapping symptoms between certain disorders. In this paper, we target four disorders: depression, PTSD, anorexia and self-harm. We explore multi-aspect transfer learning for detecting mental disorders from social media texts, using deep learning models with multi-aspect representations of language (including multiple types of interpretable linguistic features). We explore different transfer learning strategies for cross-disorder and cross-platform transfer, and show that transfer learning can be effective for improving prediction performance for disorders where little annotated data is available. We offer insights into which linguistic features are the most useful vehicles for transferring knowledge, through ablation experiments, as well as error analysis.

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ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination
Hamdy Mubarak | Sabit Hassan | Shammur Absar Chowdhury | Firoj Alam

The emergence of the COVID-19 pandemic and the first global infodemic have changed our lives in many different ways. We relied on social media to get the latest information about COVID-19 pandemic and at the same time to disseminate information. The content in social media consisted not only health related advice, plans, and informative news from policymakers, but also contains conspiracies and rumors. It became important to identify such information as soon as they are posted to make an actionable decision (e.g., debunking rumors, or taking certain measures for traveling). To address this challenge, we develop and publicly release the first largest manually annotated Arabic tweet dataset, ArCovidVac, for COVID-19 vaccination campaign, covering many countries in the Arab region. The dataset is enriched with different layers of annotation, including, (i) Informativeness more vs. less importance of the tweets); (ii) fine-grained tweet content types (e.g., advice, rumors, restriction, authenticate news/information); and (iii) stance towards vaccination (pro-vaccination, neutral, anti-vaccination). Further, we performed in-depth analysis of the data, exploring the popularity of different vaccines, trending hashtags, topics, and presence of offensiveness in the tweets. We studied the data for individual types of tweets and temporal changes in stance towards vaccine. We benchmarked the ArCovidVac dataset using transformer architectures for informativeness, content types, and stance detection.

Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. In this paper, we introduce a new contemporary Reddit dataset for fake news spreader analysis, called FACTOID, monitoring political discussions on Reddit since the beginning of 2020. The dataset contains over 4K users with 3.4M Reddit posts, and includes, beyond the users’ binary labels, also their fine-grained credibility level (very low to very high) and their political bias strength (extreme right to extreme left). As far as we are aware, this is the first fake news spreader dataset that simultaneously captures both the long-term context of users’ historical posts and the interactions between them. To create the first benchmark on our data, we provide methods for identifying misinformation spreaders by utilizing the social connections between the users along with their psycho-linguistic features. We show that the users’ social interactions can, on their own, indicate misinformation spreading, while the psycho-linguistic features are mostly informative in non-neural classification settings. In a qualitative analysis we observe that detecting affective mental processes correlates negatively with right-biased users, and that the openness to experience factor is lower for those who spread fake news.

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Multitask Learning for Grapheme-to-Phoneme Conversion of Anglicisms in German Speech Recognition
Julia Pritzen | Michael Gref | Dietlind Zühlke | Christoph Andreas Schmidt

Anglicisms are a challenge in German speech recognition. Due to their irregular pronunciation compared to native German words, automatically generated pronunciation dictionaries often contain incorrect phoneme sequences for Anglicisms. In this work, we propose a multitask sequence-to-sequence approach for grapheme-to-phoneme conversion to improve the phonetization of Anglicisms. We extended a grapheme-to-phoneme model with a classification task to distinguish Anglicisms from native German words. With this approach, the model learns to generate different pronunciations depending on the classification result. We used our model to create supplementary Anglicism pronunciation dictionaries to be added to an existing German speech recognition model. Tested on a special Anglicism evaluation set, we improved the recognition of Anglicisms compared to a baseline model, reducing the word error rate by a relative 1 % and the Anglicism error rate by a relative 3 %. With our experiment, we show that multitask learning can help solving the challenge of Anglicisms in German speech recognition.

We present SDS-200, a corpus of Swiss German dialectal speech with Standard German text translations, annotated with dialect, age, and gender information of the speakers. The dataset allows for training speech translation, dialect recognition, and speech synthesis systems, among others. The data was collected using a web recording tool that is open to the public. Each participant was given a text in Standard German and asked to translate it to their Swiss German dialect before recording it. To increase the corpus quality, recordings were validated by other participants. The data consists of 200 hours of speech by around 4000 different speakers and covers a large part of the Swiss German dialect landscape. We release SDS-200 alongside a baseline speech translation model, which achieves a word error rate (WER) of 30.3 and a BLEU score of 53.1 on the SDS-200 test set. Furthermore, we use SDS-200 to fine-tune a pre-trained XLS-R model, achieving 21.6 WER and 64.0 BLEU.

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Extracting Linguistic Knowledge from Speech: A Study of Stop Realization in 5 Romance Languages
Yaru Wu | Mathilde Hutin | Ioana Vasilescu | Lori Lamel | Martine Adda-Decker

This paper builds upon recent work in leveraging the corpora and tools originally used to develop speech technologies for corpus-based linguistic studies. We address the non-canonical realization of consonants in connected speech and we focus on voicing alternation phenomena of stops in 5 standard varieties of Romance languages (French, Italian, Spanish, Portuguese, Romanian). For these languages, both large scale corpora and speech recognition systems were available for the study. We use forced alignment with pronunciation variants and machine learning techniques to examine to what extent such frequent phenomena characterize languages and what are the most triggering factors. The results confirm that voicing alternations occur in all Romance languages. Automatic classification underlines that surrounding contexts and segment duration are recurring contributing factors for modeling voicing alternation. The results of this study also demonstrate the new role that machine learning techniques such as classification algorithms can play in helping to extract linguistic knowledge from speech and to suggest interesting research directions.

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Overlaps and Gender Analysis in the Context of Broadcast Media
Martin Lebourdais | Marie Tahon | Antoine Laurent | Sylvain Meignier | Anthony Larcher

Our main goal is to study the interactions between speakers according to their gender and role in broadcast media. In this paper, we propose an extensive study of gender and overlap annotations in various speech corpora mainly dedicated to diarisation or transcription tasks. We point out the issue of the heterogeneity of the annotation guidelines for both overlapping speech and gender categories. On top of that, we analyse how the speech content (casual speech, meetings, debate, interviews, etc.) impacts the distribution of overlapping speech segments. On a small dataset of 93 recordings from LCP French channel, we intend to characterise the interactions between speakers according to their gender. Finally, we propose a method which aims to highlight active speech areas in terms of interactions between speakers. Such a visualisation tool could improve the efficiency of qualitative studies conducted by researchers in human sciences.

This paper presents a semi-automatic approach to create a diachronic corpus of voices balanced for speaker’s age, gender, and recording period, according to 32 categories (2 genders, 4 age ranges and 4 recording periods). Corpora were selected at French National Institute of Audiovisual (INA) to obtain at least 30 speakers per category (a total of 960 speakers; only 874 have be found yet). For each speaker, speech excerpts were extracted from audiovisual documents using an automatic pipeline consisting of speech detection, background music and overlapped speech removal and speaker diarization, used to present clean speaker segments to human annotators identifying target speakers. This pipeline proved highly effective, cutting down manual processing by a factor of ten. Evaluation of the quality of the automatic processing and of the final output is provided. It shows the automatic processing compare to up-to-date process, and that the output provides high quality speech for most of the selected excerpts. This method is thus recommendable for creating large corpora of known target speakers.

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DiscoGeM: A Crowdsourced Corpus of Genre-Mixed Implicit Discourse Relations
Merel Scholman | Tianai Dong | Frances Yung | Vera Demberg

We present DiscoGeM, a crowdsourced corpus of 6,505 implicit discourse relations from three genres: political speech, literature, and encyclopedic texts. Each instance was annotated by 10 crowd workers. Various label aggregation methods were explored to evaluate how to obtain a label that best captures the meaning inferred by the crowd annotators. The results show that a significant proportion of discourse relations in DiscoGeM are ambiguous and can express multiple relation senses. Probability distribution labels better capture these interpretations than single labels. Further, the results emphasize that text genre crucially affects the distribution of discourse relations, suggesting that genre should be included as a factor in automatic relation classification. We make available the newly created DiscoGeM corpus, as well as the dataset with all annotator-level labels. Both the corpus and the dataset can facilitate a multitude of applications and research purposes, for example to function as training data to improve the performance of automatic discourse relation parsers, as well as facilitate research into non-connective signals of discourse relations.

Broadcast political debate is a core pillar of democracy: it is the public’s easiest access to opinions that shape policies and enables the general public to make informed choices. With QT30, we present the largest corpus of analysed dialogical argumentation ever created (19,842 utterances, 280,000 words) and also the largest corpus of analysed broadcast political debate to date, using 30 episodes of BBC’s ‘Question Time’ from 2020 and 2021. Question Time is the prime institution in UK broadcast political debate and features questions from the public on current political issues, which are responded to by a weekly panel of five figures of UK politics and society. QT30 is highly argumentative and combines language of well-versed political rhetoric with direct, often combative, justification-seeking of the general public. QT30 is annotated with Inference Anchoring Theory, a framework well-known in argument mining, which encodes the way arguments and conflicts are created and reacted to in dialogical settings. The resource is freely available at http://corpora.aifdb.org/qt30.

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Scaling up Discourse Quality Annotation for Political Science
Neele Falk | Gabriella Lapesa

The empirical quantification of the quality of a contribution to a political discussion is at the heart of deliberative theory, the subdiscipline of political science which investigates decision-making in deliberative democracy. Existing annotation on deliberative quality is time-consuming and carried out by experts, typically resulting in small datasets which also suffer from strong class imbalance. Scaling up such annotations with automatic tools is desirable, but very challenging. We take up this challenge and explore different strategies to improve the prediction of deliberative quality dimensions (justification, common good, interactivity, respect) in a standard dataset. Our results show that simple data augmentation techniques successfully alleviate data imbalance. Classifiers based on linguistic features (textual complexity and sentiment/polarity) and classifiers integrating argument quality annotations (from the argument mining community in NLP) were consistently outperformed by transformer-based models, with or without data augmentation.

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Clarifying Implicit and Underspecified Phrases in Instructional Text
Talita Anthonio | Anna Sauer | Michael Roth

Natural language inherently consists of implicit and underspecified phrases, which represent potential sources of misunderstanding. In this paper, we present a data set of such phrases in English from instructional texts together with multiple possible clarifications. Our data set, henceforth called CLAIRE, is based on a corpus of revision histories from wikiHow, from which we extract human clarifications that resolve an implicit or underspecified phrase. We show how language modeling can be used to generate alternate clarifications, which may or may not be compatible with the human clarification. Based on plausibility judgements for each clarification, we define the task of distinguishing between plausible and implausible clarifications. We provide several baseline models for this task and analyze to what extent different clarifications represent multiple readings as a first step to investigate misunderstandings caused by implicit/underspecified language in instructional texts.

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Multilingual Pragmaticon: Database of Discourse Formulae
Anton Buzanov | Polina Bychkova | Arina Molchanova | Anna Postnikova | Daria Ryzhova

The paper presents a multilingual database aimed to be used as a tool for typological analysis of response constructions called discourse formulae (DF), cf. English ‘No way¡ or French ‘Ça va¡ ( ‘all right’). The two primary qualities that make DF of theoretical interest for linguists are their idiomaticity and the special nature of their meanings (cf. consent, refusal, negation), determined by their dialogical function. The formal and semantic structures of these items are language-specific. Compiling a database with DF from various languages would help estimate the diversity of DF in both of these aspects, and, at the same time, establish some frequently occurring patterns. The DF in the database are accompanied with glosses and assigned with multiple tags, such as pragmatic function, additional semantics, the illocutionary type of the context, etc. As a starting point, Russian, Serbian and Slovene DF are included into the database. This data already shows substantial grammatical and lexical variability.

In this paper we present the Serbian part of the ELTeC multilingual corpus of novels written in the time period 1840-1920. The corpus is being built in order to test various distant reading methods and tools with the aim of re-thinking the European literary history. We present the various steps that led to the production of the Serbian sub-collection: the novel selection and retrieval, text preparation, structural annotation, POS-tagging, lemmatization and named entity recognition. The Serbian sub-collection was published on different platforms in order to make it freely available to various users. Several use examples show that this sub-collection is usefull for both close and distant reading approaches.

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Exploring Text Recombination for Automatic Narrative Level Detection
Nils Reiter | Judith Sieker | Svenja Guhr | Evelyn Gius | Sina Zarrieß

Automatizing the process of understanding the global narrative structure of long texts and stories is still a major challenge for state-of-the-art natural language understanding systems, particularly because annotated data is scarce and existing annotation workflows do not scale well to the annotation of complex narrative phenomena. In this work, we focus on the identification of narrative levels in texts corresponding to stories that are embedded in stories. Lacking sufficient pre-annotated training data, we explore a solution to deal with data scarcity that is common in machine learning: the automatic augmentation of an existing small data set of annotated samples with the help of data synthesis. We present a workflow for narrative level detection, that includes the operationalization of the task, a model, and a data augmentation protocol for automatically generating narrative texts annotated with breaks between narrative levels. Our experiments suggest that narrative levels in long text constitute a challenging phenomenon for state-of-the-art NLP models, but generating training data synthetically does improve the prediction results considerably.

Spelling normalisation is a useful step in the study and analysis of historical language texts, whether it is manual analysis by experts or automatic analysis using downstream natural language processing (NLP) tools. Not only does it help to homogenise the variable spelling that often exists in historical texts, but it also facilitates the use of off-the-shelf contemporary NLP tools, if contemporary spelling conventions are used for normalisation. We present FREEMnorm, a new benchmark for the normalisation of Early Modern French (from the 17th century) into contemporary French and provide a thorough comparison of three different normalisation methods: ABA, an alignment-based approach and MT-approaches, (both statistical and neural), including extensive parameter searching, which is often missing in the normalisation literature.

anguage models for historical states of language are becoming increasingly important to allow the optimal digitisation and analysis of old textual sources. Because these historical states are at the same time more complex to process and more scarce in the corpora available, this paper presents recent efforts to overcome this difficult situation. These efforts include producing a corpus, creating the model, and evaluating it with an NLP task currently used by scholars in other ongoing projects.

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Detecting Multiple Transitions in Literary Texts
Nuette Heyns | Menno van Zaanen

Identifying the high level structure of texts provides important information when performing distant reading analysis. The structure of texts is not necessarily linear, as transitions, such as changes in the scenery or flashbacks, can be present. As a first step in identifying this structure, we aim to identify transitions in texts. Previous work (Heyns and van Zaanen, 2021) proposed a system that can successfully identify one transition in literary texts. The text is split in snippets and LDA is applied, resulting in a sequence of topics. A transition is introduced at the point that separates the topics (before and after the point) best. In this article, we extend the existing system such that it can detect multiple transitions. Additionally, we introduce a new system that inherently handles multiple transitions in texts. The new system also relies on LDA information, but is more robust than the previous system. We apply these systems to texts with known transitions (as they are constructed by concatenating text snippets stemming from different source texts) and evaluation both systems on texts with one transition and texts with two transitions. As both systems rely on LDA to identify transitions between snippets, we also show the impact of varying the number of LDA topics on the results as well. The new system consistently outperforms the previous system, not only on texts with multiple transitions, but also on single boundary texts.

Parliamentary transcripts provide a valuable resource to understand the reality and know about the most important facts that occur over time in our societies. Furthermore, the political debates captured in these transcripts facilitate research on political discourse from a computational social science perspective. In this paper we release the first version of a newly compiled corpus from Basque parliamentary transcripts. The corpus is characterized by heavy Basque-Spanish code-switching, and represents an interesting resource to study political discourse in contrasting languages such as Basque and Spanish. We enrich the corpus with metadata related to relevant attributes of the speakers and speeches (language, gender, party...) and process the text to obtain named entities and lemmas. The obtained metadata is then used to perform a detailed corpus analysis which provides interesting insights about the language use of the Basque political representatives across time, parties and gender.

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GerEO: A Large-Scale Resource on the Syntactic Distribution of German Experiencer-Object Verbs
Johanna M. Poppek | Simon Masloch | Tibor Kiss

Although studied for several decades, the syntactic properties of experiencer-object (EO) verbs are still under discussion, while most analyses are not supported by substantial corpus data. With GerEO, we intend to fill this lacuna for German EO-verbs by presenting a large-scale database of more than 10,000 examples for 64 verbs (up to 200 per verb) from a newspaper corpus annotated for several syntactic and semantic features relevant for their analysis, including the overall syntactic construction, the semantic stimulus type, and the form of a possible stimulus preposition, i.e. a preposition heading a PP that indicates (a part/aspect of) the stimulus. Non-psych occurrences of the verbs are not excluded from the database but marked as such to make a comparison possible. Data of this kind can be used to develop and test theoretical hypotheses on the properties of EO-verbs, aid in the construction of experiments as well as provide training and test data for AI systems.

Classifying citations according to their purpose and importance is a challenging task that has gained considerable interest in recent years. This interest has been primarily driven by the need to create more transparent, efficient, merit-based reward systems in academia; a system that goes beyond simple bibliometric measures and considers the semantics of citations. Such systems that quantify and classify the influence of citations can act as edges that link knowledge nodes to a graph and enable efficient knowledge discovery. While a number of researchers have experimented with a variety of models, these experiments are typically limited to single-domain applications and the resulting models are hardly comparable. Recently, two Citation Context Classification (3C) shared tasks (at WOSP2020 and SDP2021) created the first benchmark enabling direct comparison of citation classification approaches, revealing the crucial impact of supplementary data on the performance of models. Reflecting from the findings of these shared tasks, we are releasing a new multi-disciplinary dataset, ACT2, an extended SDP 3C shared task dataset. This modified corpus has annotations for both citation function and importance classes newly enriched with supplementary contextual and non-contextual feature sets the selection of which follows from the lists of features used by the more successful teams in these shared tasks. Additionally, we include contextual features for cited papers (e.g. Abstract of the cited paper), which most existing datasets lack, but which have a lot of potential to improve results. We describe the methodology used for feature extraction and the challenges involved in the process. The feature enriched ACT2 dataset is available at https://github.com/oacore/ACT2.

This paper describes the continuation of a project that aims at establishing an interoperable annotation schema for quantification phenomena as part of the ISO suite of standards for semantic annotation, known as the Semantic Annotation Framework. After a break, caused by the Covid-19 pandemic, the project was relaunched in early 2022 with a second working draft of an annotation scheme, which is discussed in this paper. Keywords: semantic annotation, quantification, interoperability, annotation schema, ISO standard

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The LTRC Hindi-Telugu Parallel Corpus
Vandan Mujadia | Dipti Sharma

We present the Hindi-Telugu Parallel Corpus of different technical domains such as Natural Science, Computer Science, Law and Healthcare along with the General domain. The qualitative corpus consists of 700K parallel sentences of which 535K sentences were created using multiple methods such as extract, align and review of Hindi-Telugu corpora, end-to-end human translation, iterative back-translation driven post-editing and around 165K parallel sentences were collected from available sources in the public domain. We present the comparative assessment of created parallel corpora for representativeness and diversity. The corpus has been pre-processed for machine translation, and we trained a neural machine translation system using it and report state-of-the-art baseline results on the developed development set over multiple domains and on available benchmarks. With this, we define a new task on Domain Machine Translation for low resource language pairs such as Hindi and Telugu. The developed corpus (535K) is freely available for non-commercial research and to the best of our knowledge, this is the well curated, largest, publicly available domain parallel corpus for Hindi-Telugu.

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MHE: Code-Mixed Corpora for Similar Language Identification
Priya Rani | John P. McCrae | Theodorus Fransen

This paper introduces a new Magahi-Hindi-English (MHE) code-mixed data-set for similar language identification (SMLID), where Magahi is a less-resourced minority language. This corpus provides a language id at two levels: word and sentence. This data-set is the first Magahi-Hindi-English code-mixed data-set for similar language identification task. Furthermore, we will discuss the complexity of the data-set and provide a few baselines for the language identification task.

We introduce a dataset built around a large collection of TV (and movie) series. Those are filled with challenging multi-party dialogues. Moreover, TV series come with a very active fan base that allows the collection of metadata and accelerates annotation. With 16 TV and movie series, Bazinga! amounts to 400+ hours of speech and 8M+ tokens, including 500K+ tokens annotated with the speaker, addressee, and entity linking information. Along with the dataset, we also provide a baseline for speaker diarization, punctuation restoration, and person entity recognition. The results demonstrate the difficulty of the tasks and of transfer learning from models trained on mono-speaker audio or written text, which is more widely available. This work is a step towards better multi-party dialogue structuring and understanding. Bazinga! is available at hf.co/bazinga. Because (a large) part of Bazinga! is only partially annotated, we also expect this dataset to foster research towards self- or weakly-supervised learning methods.

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The Ellogon Web Annotation Tool: Annotating Moral Values and Arguments
Alexandros Fotios Ntogramatzis | Anna Gradou | Georgios Petasis | Marko Kokol

In this paper, we present the Ellogon Web Annotation Tool. It is a collaborative, web-based annotation tool built upon the Ellogon infrastructure offering an improved user experience and adaptability to various annotation scenarios by making good use of the latest design practices and web development frameworks. Being in development for many years, this paper describes its current architecture, along with the recent modifications that extend the existing functionalities and the new features that were added. The new version of the tool offers document analytics, annotation inspection and comparison features, a modern UI, and formatted text import (e.g. TEI XML documents, rendered with simple markup). We present two use cases that serve as two examples of different annotation scenarios to demonstrate the new functionalities. An appropriate (user-supplied, XML-based) annotation schema is used for each scenario. The first schema contains the relevant components for representing concepts, moral values, and ideas. The second includes all the necessary elements for annotating argumentative units in a document and their binary relations.

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WeCanTalk: A New Multi-language, Multi-modal Resource for Speaker Recognition
Karen Jones | Kevin Walker | Christopher Caruso | Jonathan Wright | Stephanie Strassel

The WeCanTalk (WCT) Corpus is a new multi-language, multi-modal resource for speaker recognition. The corpus contains Cantonese, Mandarin and English telephony and video speech data from over 200 multilingual speakers located in Hong Kong. Each speaker contributed at least 10 telephone conversations of 8-10 minutes’ duration collected via a custom telephone platform based in Hong Kong. Speakers also uploaded at least 3 videos in which they were both speaking and visible, along with one selfie image. At least half of the calls and videos for each speaker were in Cantonese, while their remaining recordings featured one or more different languages. Both calls and videos were made in a variety of noise conditions. All speech and video recordings were audited by experienced multilingual annotators for quality including presence of the expected language and for speaker identity. The WeCanTalk Corpus has been used to support the NIST 2021 Speaker Recognition Evaluation and will be published in the LDC catalog.

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Using Wiktionary to Create Specialized Lexical Resources and Datasets
Lenka Bajčetić | Thierry Declerck

This paper describes an approach aiming at utilizing Wiktionary data for creating specialized lexical datasets which can be used for enriching other lexical (semantic) resources or for generating datasets that can be used for evaluating or improving NLP tasks, like Word Sense Disambiguation, Word-in-Context challenges, or Sense Linking across lexicons and dictionaries. We have focused on Wiktionary data about pronunciation information in English, and grammatical number and grammatical gender in German.

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STAPI: An Automatic Scraper for Extracting Iterative Title-Text Structure from Web Documents
Nan Zhang | Shomir Wilson | Prasenjit Mitra

Formal documents often are organized into sections of text, each with a title, and extracting this structure remains an under-explored aspect of natural language processing. This iterative title-text structure is valuable data for building models for headline generation and section title generation, but there is no corpus that contains web documents annotated with titles and prose texts. Therefore, we propose the first title-text dataset on web documents that incorporates a wide variety of domains to facilitate downstream training. We also introduce STAPI (Section Title And Prose text Identifier), a two-step system for labeling section titles and prose text in HTML documents. To filter out unrelated content like document footers, its first step involves a filter that reads HTML documents and proposes a set of textual candidates. In the second step, a typographic classifier takes the candidates from the filter and categorizes each one into one of the three pre-defined classes (title, prose text, and miscellany). We show that STAPI significantly outperforms two baseline models in terms of title-text identification. We release our dataset along with a web application to facilitate supervised and semi-supervised training in this domain.

ELTE Poetry Corpus is a database that stores canonical Hungarian poetry with automatically generated annotations of the poems’ structural units, grammatical features and sound devices, i.e. rhyme patterns, rhyme pairs, rhythm, alliterations and the main phonological features of words. The corpus has an open access online query tool with several search functions. The paper presents the main stages of the annotation process and the tools used for each stage. The TEI XML format of the different versions of the corpus, each of which contains an increasing number of annotation layers, is presented as well. We have also specified our own XML format for the corpus, slightly different from TEI, in order to make it easier and faster to execute queries on the corpus. We discuss the results of a manual evaluation of the quality of automatic annotation of rhythm, as well as the results of an automatic evaluation of different rule sets used for the automatic annotation of rhyme patterns. Finally, the paper gives an overview of the main functions of the online query tool developed for the corpus.

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HAWP: a Dataset for Hindi Arithmetic Word Problem Solving
Harshita Sharma | Pruthwik Mishra | Dipti Sharma

Word Problem Solving remains a challenging and interesting task in NLP. A lot of research has been carried out to solve different genres of word problems with various complexity levels in recent years. However, most of the publicly available datasets and work has been carried out for English. Recently there has been a surge in this area of word problem solving in Chinese with the creation of large benchmark datastes. Apart from these two languages, labeled benchmark datasets for low resource languages are very scarce. This is the first attempt to address this issue for any Indian Language, especially Hindi. In this paper, we present HAWP (Hindi Arithmetic Word Problems), a dataset consisting of 2336 arithmetic word problems in Hindi. We also developed baseline systems for solving these word problems. We also propose a new evaluation technique for word problem solvers taking equation equivalence into account.

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The Bulgarian Event Corpus: Overview and Initial NER Experiments
Petya Osenova | Kiril Simov | Iva Marinova | Melania Berbatova

The paper describes the Bulgarian Event Corpus (BEC). The annotation scheme is based on CIDOC-CRM ontology and on the English Framenet, adjusted for our task. It includes two main layers: named entities and events with their roles. The corpus is multi-domain and mainly oriented towards Social Sciences and Humanities (SSH). It will be used for: extracting knowledge and making it available through the Bulgaria-centric Knowledge Graph; further developing an annotation scheme that handles multiple domains in SSH; training automatic modules for the most important knowledge-based tasks, such as domain-specific and nested NER, NEL, event detection and profiling. Initial experiments were conducted on standard NER task due to complexity of the dataset and the rich NE annotation scheme. The results are promising with respect to some labels and give insights on handling better other ones. These experiments serve also as error detection modules that would help us in scheme re-design. They are a basis for further and more complex tasks, such as nested NER, NEL and event detection.

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A Corpus for Commonsense Inference in Story Cloze Test
Bingsheng Yao | Ethan Joseph | Julian Lioanag | Mei Si

The Story Cloze Test (SCT) is designed for training and evaluating machine learning algorithms for narrative understanding and inferences. The SOTA models can achieve over 90% accuracy on predicting the last sentence. However, it has been shown that high accuracy can be achieved by merely using surface-level features. We suspect these models may not truly understand the story. Based on the SCT dataset, we constructed a human-labeled and human-verified commonsense knowledge inference dataset. Given the first four sentences of a story, we asked crowd-source workers to choose from four types of narrative inference for deciding the ending sentence and which sentence contributes most to the inference. We accumulated data on 1871 stories, and three human workers labeled each story. Analysis of the intra-category and inter-category agreements show a high level of consensus. We present two new tasks for predicting the narrative inference categories and contributing sentences. Our results show that transformer-based models can reach SOTA performance on the original SCT task using transfer learning but don’t perform well on these new and more challenging tasks.

We present GTP-SW3, a 3.5 billion parameter autoregressive language model, trained on a newly created 100 GB Swedish corpus. This paper provides insights with regards to data collection and training, while highlights the challenges of proper model evaluation. The results of quantitive evaluation through perplexity indicate that GPT-SW3 is a competent model in comparison with existing autoregressive models of similar size. Additionally, we perform an extensive prompting study which reveals the good text generation capabilities of GTP-SW3.

This paper describes a system for interactive poem generation, which combines neural language models (LMs) for poem generation with explicit constraints that can be set by users on form, topic, emotion, and rhyming scheme. LMs cannot learn such constraints from the data, which is scarce with respect to their needs even for a well-resourced language such as French. We propose a method to generate verses and stanzas by combining LMs with rule-based algorithms, and compare several approaches for adjusting the words of a poem to a desired combination of topics or emotions. An approach to automatic rhyme setting using a phonetic dictionary is proposed as well. Our system has been demonstrated at public events, and log analysis shows that users found it engaging.

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ELF22: A Context-based Counter Trolling Dataset to Combat Internet Trolls
Huije Lee | Young Ju Na | Hoyun Song | Jisu Shin | Jong Park

Online trolls increase social costs and cause psychological damage to individuals. With the proliferation of automated accounts making use of bots for trolling, it is difficult for targeted individual users to handle the situation both quantitatively and qualitatively. To address this issue, we focus on automating the method to counter trolls, as counter responses to combat trolls encourage community users to maintain ongoing discussion without compromising freedom of expression. For this purpose, we propose a novel dataset for automatic counter response generation. In particular, we constructed a pair-wise dataset that includes troll comments and counter responses with labeled response strategies, which enables models fine-tuned on our dataset to generate responses by varying counter responses according to the specified strategy. We conducted three tasks to assess the effectiveness of our dataset and evaluated the results through both automatic and human evaluation. In human evaluation, we demonstrate that the model fine-tuned with our dataset shows a significantly improved performance in strategy-controlled sentence generation.

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Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task
Isaac Ampomah | James Burton | Amir Enshaei | Noura Al Moubayed

Numerical tables are widely employed to communicate or report the classification performance of machine learning (ML) models with respect to a set of evaluation metrics. For non-experts, domain knowledge is required to fully understand and interpret the information presented by numerical tables. This paper proposes a new natural language generation (NLG) task where neural models are trained to generate textual explanations, analytically describing the classification performance of ML models based on the metrics’ scores reported in the tables. Presenting the generated texts along with the numerical tables will allow for a better understanding of the classification performance of ML models. We constructed a dataset comprising numerical tables paired with their corresponding textual explanations written by experts to facilitate this NLG task. Experiments on the dataset are conducted by fine-tuning pre-trained language models (T5 and BART) to generate analytical textual explanations conditioned on the information in the tables. Furthermore, we propose a neural module, Metrics Processing Unit (MPU), to improve the performance of the baselines in terms of correctly verbalising the information in the corresponding table. Evaluation and analysis conducted indicate, that exploring pre-trained models for data-to-text generation leads to better generalisation performance and can produce high-quality textual explanations.

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Barch: an English Dataset of Bar Chart Summaries
Iza Škrjanec | Muhammad Salman Edhi | Vera Demberg

We present Barch, a new English dataset of human-written summaries describing bar charts. This dataset contains 47 charts based on a selection of 18 topics. Each chart is associated with one of the four intended messages expressed in the chart title. Using crowdsourcing, we collected around 20 summaries per chart, or one thousand in total. The text of the summaries is aligned with the chart data as well as with analytical inferences about the data drawn by humans. Our datasets is one of the first to explore the effect of intended messages on the data descriptions in chart summaries. Additionally, it lends itself well to the task of training data-driven systems for chart-to-text generation. We provide results on the performance of state-of-the-art neural generation models trained on this dataset and discuss the strengths and shortcomings of different models.

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Effectiveness of Data Augmentation and Pretraining for Improving Neural Headline Generation in Low-Resource Settings
Matej Martinc | Syrielle Montariol | Lidia Pivovarova | Elaine Zosa

We tackle the problem of neural headline generation in a low-resource setting, where only limited amount of data is available to train a model. We compare the ideal high-resource scenario on English with results obtained on a smaller subset of the same data and also run experiments on two small news corpora covering low-resource languages, Croatian and Estonian. Two options for headline generation in a multilingual low-resource scenario are investigated: a pretrained multilingual encoder-decoder model and a combination of two pretrained language models, one used as an encoder and the other as a decoder, connected with a cross-attention layer that needs to be trained from scratch. The results show that the first approach outperforms the second one by a large margin. We explore several data augmentation and pretraining strategies in order to improve the performance of both models and show that while we can drastically improve the second approach using these strategies, they have little to no effect on the performance of the pretrained encoder-decoder model. Finally, we propose two new measures for evaluating the performance of the models besides the classic ROUGE scores.

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Effectiveness of French Language Models on Abstractive Dialogue Summarization Task
Yongxin Zhou | François Portet | Fabien Ringeval

Pre-trained language models have established the state-of-the-art on various natural language processing tasks, including dialogue summarization, which allows the reader to quickly access key information from long conversations in meetings, interviews or phone calls. However, such dialogues are still difficult to handle with current models because the spontaneity of the language involves expressions that are rarely present in the corpora used for pre-training the language models. Moreover, the vast majority of the work accomplished in this field has been focused on English. In this work, we present a study on the summarization of spontaneous oral dialogues in French using several language specific pre-trained models: BARThez, and BelGPT-2, as well as multilingual pre-trained models: mBART, mBARThez, and mT5. Experiments were performed on the DECODA (Call Center) dialogue corpus whose task is to generate abstractive synopses from call center conversations between a caller and one or several agents depending on the situation. Results show that the BARThez models offer the best performance far above the previous state-of-the-art on DECODA. We further discuss the limits of such pre-trained models and the challenges that must be addressed for summarizing spontaneous dialogues.

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ALEXSIS: A Dataset for Lexical Simplification in Spanish
Daniel Ferrés | Horacio Saggion

Lexical Simplification is the process of reducing the lexical complexity of a text by replacing difficult words with easier to read (or understand) expressions while preserving the original information and meaning. In this paper we introduce ALEXSIS, a new dataset for this task, and we use ALEXSIS to benchmark Lexical Simplification systems in Spanish. The paper describes the evaluation of three kind of approaches to Lexical Simplification, a thesaurus-based approach, a single transformers-based approach, and a combination of transformers. We also report state of the art results on a previous Lexical Simplification dataset for Spanish.

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The IARPA BETTER Program Abstract Task Four New Semantically Annotated Corpora from IARPA’s BETTER Program
Timothy Mckinnon | Carl Rubino

IARPA’s Better Extraction from Text Towards Enhanced Retrieval (BETTER) Program created multiple multilingual datasets to spawn and evaluate cross-language information extraction and information retrieval research and development in zero-shot conditions. The first set of these resources for information extraction, the “Abstract” data will be released to the public at LREC 2022 in four languages to champion further information extraction work in this area. This paper presents the event and argument annotation in the Abstract Evaluation phase of BETTER, as well as the data collection, preparation, partitioning and mark-up of the datasets.

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A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis Treatment
Uyen Phan | Phuong N.V Nguyen | Nhung Nguyen

Named Entity Recognition (NER) is an important task in information extraction. However, due to the lack of labelled corpora, biomedical NER has scarcely been studied in Vietnamese compared to English. To address this situation, we have constructed VietBioNER, a labelled NER corpus of Vietnamese academic biomedical text. The corpus focuses specifically on supporting tuberculosis surveillance, and was constructed by collecting scientific papers and grey literature related to tuberculosis symptoms and diagnostics. We manually annotated a small set of the collected documents with five categories of named entities: Organisation, Location, Date and Time, Symptom and Disease, and Diagnostic Procedure. Inter-annotator agreement ranges from 70.59% and 95.89% F-score according to entity category. In this paper, we make available two splits of the corpus, corresponding to traditional supervised learning and few-shot learning settings. We also provide baseline results for both of these settings, in addition to a dictionary-based approach, as a means to stimulate further research into Vietnamese biomedical NER. Although supervised methods produce results that are far superior to the other two approaches, the fact that even one-shot learning can outperform the dictionary-based method provides evidence that further research into few-shot learning on this text type would be worthwhile.

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RaFoLa: A Rationale-Annotated Corpus for Detecting Indicators of Forced Labour
Erick Mendez Guzman | Viktor Schlegel | Riza Batista-Navarro

Forced labour is the most common type of modern slavery, and it is increasingly gaining the attention of the research and social community. Recent studies suggest that artificial intelligence (AI) holds immense potential for augmenting anti-slavery action. However, AI tools need to be developed transparently in cooperation with different stakeholders. Such tools are contingent on the availability and access to domain-specific data, which are scarce due to the near-invisible nature of forced labour. To the best of our knowledge, this paper presents the first openly accessible English corpus annotated for multi-class and multi-label forced labour detection. The corpus consists of 989 news articles retrieved from specialised data sources and annotated according to risk indicators defined by the International Labour Organization (ILO). Each news article was annotated for two aspects: (1) indicators of forced labour as classification labels and (2) snippets of the text that justify labelling decisions. We hope that our data set can help promote research on explainability for multi-class and multi-label text classification. In this work, we explain our process for collecting the data underpinning the proposed corpus, describe our annotation guidelines and present some statistical analysis of its content. Finally, we summarise the results of baseline experiments based on different variants of the Bidirectional Encoder Representation from Transformer (BERT) model.

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Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT
Mustafa Jarrar | Mohammed Khalilia | Sana Ghanem

This paper presents Wojood, a corpus for Arabic nested Named Entity Recognition (NER). Nested entities occur when one entity mention is embedded inside another entity mention. Wojood consists of about 550K Modern Standard Arabic (MSA) and dialect tokens that are manually annotated with 21 entity types including person, organization, location, event and date. More importantly, the corpus is annotated with nested entities instead of the more common flat annotations. The data contains about 75K entities and 22.5% of which are nested. The inter-annotator evaluation of the corpus demonstrated a strong agreement with Cohen’s Kappa of 0.979 and an F1-score of 0.976. To validate our data, we used the corpus to train a nested NER model based on multi-task learning using the pre-trained AraBERT (Arabic BERT). The model achieved an overall micro F1-score of 0.884. Our corpus, the annotation guidelines, the source code and the pre-trained model are publicly available.

In this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get advice from medical doctors. As is common in social media data in this domain, the class labels of the corpus are very imbalanced. This and a high topic imbalance make it a very challenging dataset, since often, the same symptom can have several causes and is not always related to a medication intake. We aim to encourage further multi-lingual efforts in the domain of ADR detection and provide preliminary experiments for binary classification using different methods of zero- and few-shot learning based on a multi-lingual model. When fine-tuning XLM-RoBERTa first on English patient forum data and then on the new German data, we achieve an F1-score of 37.52 for the positive class. We make the dataset and models publicly available for the community.

Despite remarkable advances in the development of language resources over the recent years, there is still a shortage of annotated, publicly available corpora covering (German) medical language. With the initial release of the German Guideline Program in Oncology NLP Corpus (GGPONC), we have demonstrated how such corpora can be built upon clinical guidelines, a widely available resource in many natural languages with a reasonable coverage of medical terminology. In this work, we describe a major new release for GGPONC. The corpus has been substantially extended in size and re-annotated with a new annotation scheme based on SNOMED CT top level hierarchies, reaching high inter-annotator agreement (γ=.94). Moreover, we annotated elliptical coordinated noun phrases and their resolutions, a common language phenomenon in (not only German) scientific documents. We also trained BERT-based named entity recognition models on this new data set, which achieve high performance on short, coarse-grained entity spans (F1=.89), while the rate of boundary errors increases for long entity spans. GGPONC is freely available through a data use agreement. The trained named entity recognition models, as well as the detailed annotation guide, are also made publicly available.

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ClinIDMap: Towards a Clinical IDs Mapping for Data Interoperability
Elena Zotova | Montse Cuadros | German Rigau

This paper presents ClinIDMap, a tool for mapping identifiers between clinical ontologies and lexical resources. ClinIDMap interlinks identifiers from UMLS, SMOMED-CT, ICD-10 and the corresponding Wikipedia articles for concepts from the UMLS Metathesaurus. Our main goal is to provide semantic interoperability across the clinical concepts from various knowledge bases. As a side effect, the mapping enriches already annotated corpora in multiple languages with new labels. For instance, spans manually annotated with IDs from UMLS can be annotated with Semantic Types and Groups, and its corresponding SNOMED CT and ICD-10 IDs. We also experiment with sequence labelling models for detecting Diagnosis and Procedures concepts and for detecting UMLS Semantic Groups trained on Spanish, English, and bilingual corpora obtained with the new mapping procedure. The ClinIDMap tool is publicly available.

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Identifying Draft Bills Impacting Existing Legislation: a Case Study on Romanian
Corina Ceausu | Sergiu Nisioi

In our paper, we present a novel corpus of historical legal documents on the Romanian public procurement legislation and an annotated subset of draft bills that have been screened by legal experts and identified as impacting past public procurement legislation. Using the manual annotations provided by the experts, we attempt to automatically identify future draft bills that have the potential to impact existing policies on public procurement.

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MuLD: The Multitask Long Document Benchmark
George Hudson | Noura Al Moubayed

The impressive progress in NLP techniques has been driven by the development of multi-task benchmarks such as GLUE and SuperGLUE. While these benchmarks focus on tasks for one or two input sentences, there has been exciting work in designing efficient techniques for processing much longer inputs. In this paper, we present MuLD: a new long document benchmark consisting of only documents over 10,000 tokens. By modifying existing NLP tasks, we create a diverse benchmark which requires models to successfully model long-term dependencies in the text. We evaluate how existing models perform, and find that our benchmark is much more challenging than their ‘short document’ equivalents. Furthermore, by evaluating both regular and efficient transformers, we show that models with increased context length are better able to solve the tasks presented, suggesting that future improvements in these models are vital for solving similar long document problems. We release the data and code for baselines to encourage further research on efficient NLP models.

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A Cross-document Coreference Dataset for Longitudinal Tracking across Radiology Reports
Surabhi Datta | Hio Cheng Lam | Atieh Pajouhi | Sunitha Mogalla | Kirk Roberts

This paper proposes a new cross-document coreference resolution (CDCR) dataset for identifying co-referring radiological findings and medical devices across a patient’s radiology reports. Our annotated corpus contains 5872 mentions (findings and devices) spanning 638 MIMIC-III radiology reports across 60 patients, covering multiple imaging modalities and anatomies. There are a total of 2292 mention chains. We describe the annotation process in detail, highlighting the complexities involved in creating a sizable and realistic dataset for radiology CDCR. We apply two baseline methods–string matching and transformer language models (BERT)–to identify cross-report coreferences. Our results indicate the requirement of further model development targeting better understanding of domain language and context to address this challenging and unexplored task. This dataset can serve as a resource to develop more advanced natural language processing CDCR methods in the future. This is one of the first attempts focusing on CDCR in the clinical domain and holds potential in benefiting physicians and clinical research through long-term tracking of radiology findings.

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How’s Business Going Worldwide ? A Multilingual Annotated Corpus for Business Relation Extraction
Hadjer Khaldi | Farah Benamara | Camille Pradel | Grégoire Sigel | Nathalie Aussenac-Gilles

The business world has changed due to the 21st century economy, where borders have melted and trades became free. Nowadays,competition is no longer only at the local market level but also at the global level. In this context, the World Wide Web has become a major source of information for companies and professionals to keep track of their complex, rapidly changing, and competitive business environment. A lot of effort is nonetheless needed to collect and analyze this information due to information overload problem and the huge number of web pages to process and analyze. In this paper, we propose the BizRel resource, the first multilingual (French,English, Spanish, and Chinese) dataset for automatic extraction of binary business relations involving organizations from the web. This dataset is used to train several monolingual and cross-lingual deep learning models to detect these relations in texts. Our results are encouraging, demonstrating the effectiveness of such a resource for both research and business communities. In particular, we believe multilingual business relation extraction systems are crucial tools for decision makers to identify links between specific market stakeholders and build business networks which enable to anticipate changes and discover new threats or opportunities. Our work is therefore an important direction toward such tools.

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Do Transformer Networks Improve the Discovery of Rules from Text?
Mahdi Rahimi | Mihai Surdeanu

With their Discovery of Inference Rules from Text (DIRT) algorithm, Lin and Pantel (2001) made a seminal contribution to the field of rule acquisition from text, by adapting the distributional hypothesis of Harris (1954) to rules that model binary relations such as X treat Y. DIRT’s relevance is renewed in today’s neural era given the recent focus on interpretability in the field of natural language processing. We propose a novel take on the DIRT algorithm, where we implement the distributional hypothesis using the contextualized embeddings provided by BERT, a transformer-network-based language model (Vaswani et al. 2017; Devlin et al. 2018). In particular, we change the similarity measure between pairs of slots (i.e., the set of words matched by a rule) from the original formula that relies on lexical items to a formula computed using contextualized embeddings. We empirically demonstrate that this new similarity method yields a better implementation of the distributional hypothesis, and this, in turn, yields rules that outperform the original algorithm in the question answering-based evaluation proposed by Lin and Pantel (2001).

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Offensive language detection in Hebrew: can other languages help?
Marina Litvak | Natalia Vanetik | Chaya Liebeskind | Omar Hmdia | Rizek Abu Madeghem

Unfortunately, offensive language in social media is a common phenomenon nowadays. It harms many people and vulnerable groups. Therefore, automated detection of offensive language is in high demand and it is a serious challenge in multilingual domains. Various machine learning approaches combined with natural language techniques have been applied for this task lately. This paper contributes to this area from several aspects: (1) it introduces a new dataset of annotated Facebook comments in Hebrew; (2) it describes a case study with multiple supervised models and text representations for a task of offensive language detection in three languages, including two Semitic (Hebrew and Arabic) languages; (3) it reports evaluation results of cross-lingual and multilingual learning for detection of offensive content in Semitic languages; and (4) it discusses the limitations of these settings.

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JaMIE: A Pipeline Japanese Medical Information Extraction System with Novel Relation Annotation
Fei Cheng | Shuntaro Yada | Ribeka Tanaka | Eiji Aramaki | Sadao Kurohashi

In the field of Japanese medical information extraction, few analyzing tools are available and relation extraction is still an under-explored topic. In this paper, we first propose a novel relation annotation schema for investigating the medical and temporal relations between medical entities in Japanese medical reports. We experiment with the practical annotation scenarios by separately annotating two different types of reports. We design a pipeline system with three components for recognizing medical entities, classifying entity modalities, and extracting relations. The empirical results show accurate analyzing performance and suggest the satisfactory annotation quality, the superiority of the latest contextual embedding models. and the feasible annotation strategy for high-accuracy demand.

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Enhanced Entity Annotations for Multilingual Corpora
Michael Strobl | Amine Trabelsi | Osmar Zaïane

Modern approaches in Natural Language Processing (NLP) require, ideally, large amounts of labelled data for model training. However, new language resources, for example, for Named Entity Recognition (NER), Co-reference Resolution (CR), Entity Linking (EL) and Relation Extraction (RE), naming a few of the most popular tasks in NLP, have always been challenging to create since manual text annotations can be very time-consuming to acquire. While there may be an acceptable amount of labelled data available for some of these tasks in one language, there may be a lack of datasets in another. WEXEA is a tool to exhaustively annotate entities in the English Wikipedia. Guidelines for editors of Wikipedia articles result, on the one hand, in only a few annotations through hyperlinks, but on the other hand, make it easier to exhaustively annotate the rest of these articles with entities than starting from scratch. We propose the following main improvements to WEXEA: Creating multi-lingual corpora, improved entity annotations using a proven NER system, annotating dates and times. A brief evaluation of the annotation quality of WEXEA is added.

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Enriching Epidemiological Thematic Features For Disease Surveillance Corpora Classification
Edmond Menya | Mathieu Roche | Roberto Interdonato | Dickson Owuor

We present EpidBioBERT, a biosurveillance epidemiological document tagger for disease surveillance over PADI-Web system. Our model is trained on PADI-Web corpus which contains news articles on Animal Diseases Outbreak extracted from the web. We train a classifier to discriminate between relevant and irrelevant documents based on their epidemiological thematic feature content in preparation for further epidemiology information extraction. Our approach proposes a new way to perform epidemiological document classification by enriching epidemiological thematic features namely disease, host, location and date, which are used as inputs to our epidemiological document classifier. We adopt a pre-trained biomedical language model with a novel fine tuning approach that enriches these epidemiological thematic features. We find these thematic features rich enough to improve epidemiological document classification over a smaller data set than initially used in PADI-Web classifier. This improves the classifiers ability to avoid false positive alerts on disease surveillance systems. To further understand information encoded in EpidBioBERT, we experiment the impact of each epidemiology thematic feature on the classifier under ablation studies. We compare our biomedical pre-trained approach with a general language model based model finding that thematic feature embeddings pre-trained on general English documents are not rich enough for epidemiology classification task. Our model achieves an F1-score of 95.5% over an unseen test set, with an improvement of +5.5 points on F1-Score on the PADI-Web classifier with nearly half the training data set.

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Spanish Datasets for Sensitive Entity Detection in the Legal Domain
Ona de Gibert Bonet | Aitor García Pablos | Montse Cuadros | Maite Melero

The de-identification of sensible data, also known as automatic textual anonymisation, is essential for data sharing and reuse, both for research and commercial purposes. The first step for data anonymisation is the detection of sensible entities. In this work, we present four new datasets for named entity detection in Spanish in the legal domain. These datasets have been generated in the framework of the MAPA project, three smaller datasets have been manually annotated and one large dataset has been automatically annotated, with an estimated error rate of around 14%. In order to assess the quality of the generated datasets, we have used them to fine-tune a battery of entity-detection models, using as foundation different pre-trained language models: one multilingual, two general-domain monolingual and one in-domain monolingual. We compare the results obtained, which validate the datasets as a valuable resource to fine-tune models for the task of named entity detection. We further explore the proposed methodology by applying it to a real use case scenario.

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ConvTextTM: An Explainable Convolutional Tsetlin Machine Framework for Text Classification
Bimal Bhattarai | Ole-Christoffer Granmo | Lei Jiao

Recent advancements in natural language processing (NLP) have reshaped the industry, with powerful language models such as GPT-3 achieving superhuman performance on various tasks. However, the increasing complexity of such models turns them into “black boxes”, creating uncertainty about their internal operation and decision-making. Tsetlin Machine (TM) employs human-interpretable conjunctive clauses in propositional logic to solve complex pattern recognition problems and has demonstrated competitive performance in various NLP tasks. In this paper, we propose ConvTextTM, a novel convolutional TM architecture for text classification. While legacy TM solutions treat the whole text as a corpus-specific set-of-words (SOW), ConvTextTM breaks down the text into a sequence of text fragments. The convolution over the text fragments opens up for local position-aware analysis. Further, ConvTextTM eliminates the dependency on a corpus-specific vocabulary. Instead, it employs a generic SOW formed by the tokenization scheme of the Bidirectional Encoder Representations from Transformers (BERT). The convolution binds together the tokens, allowing ConvTextTM to address the out-of-vocabulary problem as well as spelling errors. We investigate the local explainability of our proposed method using clause-based features. Extensive experiments are conducted on seven datasets, to demonstrate that the accuracy of ConvTextTM is either superior or comparable to state-of-the-art baselines.

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Elvis vs. M. Jackson: Who has More Albums? Classification and Identification of Elements in Comparative Questions
Meriem Beloucif | Seid Muhie Yimam | Steffen Stahlhacke | Chris Biemann

Comparative Question Answering (cQA) is the task of providing concrete and accurate responses to queries such as: “Is Lyft cheaper than a regular taxi?” or “What makes a mortgage different from a regular loan?”. In this paper, we propose two new open-domain real-world datasets for identifying and labeling comparative questions. While the first dataset contains instances of English questions labeled as comparative vs. non-comparative, the second dataset provides additional labels including the objects and the aspects of comparison. We conduct several experiments that evaluate the soundness of our datasets. The evaluation of our datasets using various classifiers show promising results that reach close-to-human results on a binary classification task with a neural model using ALBERT embeddings. When approaching the unsupervised sequence labeling task, some headroom remains.

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Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction
Hui-Syuan Yeh | Thomas Lavergne | Pierre Zweigenbaum

Relation extraction is a core problem for natural language processing in the biomedical domain. Recent research on relation extraction showed that prompt-based learning improves the performance on both fine-tuning on full training set and few-shot training. However, less effort has been made on domain-specific tasks where good prompt design can be even harder. In this paper, we investigate prompting for biomedical relation extraction, with experiments on the ChemProt dataset. We present a simple yet effective method to systematically generate comprehensive prompts that reformulate the relation extraction task as a cloze-test task under a simple prompt formulation. In particular, we experiment with different ranking scores for prompt selection. With BioMed-RoBERTa-base, our results show that prompting-based fine-tuning obtains gains by 14.21 F1 over its regular fine-tuning baseline, and 1.14 F1 over SciFive-Large, the current state-of-the-art on ChemProt. Besides, we find prompt-based learning requires fewer training examples to make reasonable predictions. The results demonstrate the potential of our methods in such a domain-specific relation extraction task.

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Comparing Annotated Datasets for Named Entity Recognition in English Literature
Rositsa Ivanova | Marieke van Erp | Sabrina Kirrane

The growing interest in named entity recognition (NER) in various domains has led to the creation of different benchmark datasets, often with slightly different annotation guidelines. To better understand the different NER benchmark datasets for the domain of English literature and their impact on the evaluation of NER tools, we analyse two existing annotated datasets and create two additional gold standard datasets. Following on from this, we evaluate the performance of two NER tools, one domain-specific and one general-purpose NER tool, using the four gold standards, and analyse the sources for the differences in the measured performance. Our results show that the performance of the two tools varies significantly depending on the gold standard used for the individual evaluations.

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Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion
Flora Sakketou | Allison Lahnala | Liane Vogel | Lucie Flek

There is an increasing need for the ability to model fine-grained opinion shifts of social media users, as concerns about the potential polarizing social effects increase. However, the lack of publicly available datasets that are suitable for the task presents a major challenge. In this paper, we introduce an innovative annotated dataset for modeling subtle opinion fluctuations and detecting fine-grained stances. The dataset includes a sufficient amount of stance polarity and intensity labels per user over time and within entire conversational threads, thus making subtle opinion fluctuations detectable both in long term and in short term. All posts are annotated by non-experts and a significant portion of the data is also annotated by experts. We provide a strategy for recruiting suitable non-experts. Our analysis of the inter-annotator agreements shows that the resulting annotations obtained from the majority vote of the non-experts are of comparable quality to the annotations of the experts. We provide analyses of the stance evolution in short term and long term levels, a comparison of language usage between users with vacillating and resolute attitudes, and fine-grained stance detection baselines.

Despite the large number of computational resources for emotion recognition, there is a lack of data sets relying on appraisal models. According to Appraisal theories, emotions are the outcome of a multi-dimensional evaluation of events. In this paper, we present APPReddit, the first corpus of non-experimental data annotated according to this theory. After describing its development, we compare our resource with enISEAR, a corpus of events created in an experimental setting and annotated for appraisal. Results show that the two corpora can be mapped notwithstanding different typologies of data and annotations schemes. A SVM model trained on APPReddit predicts four appraisal dimensions without significant loss. Merging both corpora in a single training set increases the prediction of 3 out of 4 dimensions. Such findings pave the way to a better performing classification model for appraisal prediction.

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Evaluating Methods for Extraction of Aspect Terms in Opinion Texts in Portuguese - the Challenges of Implicit Aspects
Mateus Machado | Thiago Alexandre Salgueiro Pardo

One of the challenges of aspect-based sentiment analysis is the implicit mention of aspects. These are more difficult to identify and may require world knowledge to do so. In this work, we evaluate frequency-based, hybrid, and machine learning methods, including the use of the pre-trained BERT language model, in the task of extracting aspect terms in opinionated texts in Portuguese, emphasizing the analysis of implicit aspects. Besides the comparative evaluation of methods, the differential of this work lies in the analysis’s novelty using a typology of implicit aspects that shows the knowledge needed to identify each implicit aspect term, thus allowing a mapping of the strengths and weaknesses of each method.

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SenticNet 7: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment Analysis
Erik Cambria | Qian Liu | Sergio Decherchi | Frank Xing | Kenneth Kwok

In recent years, AI research has demonstrated enormous potential for the benefit of humanity and society. While often better than its human counterparts in classification and pattern recognition tasks, however, AI still struggles with complex tasks that require commonsense reasoning such as natural language understanding. In this context, the key limitations of current AI models are: dependency, reproducibility, trustworthiness, interpretability, and explainability. In this work, we propose a commonsense-based neurosymbolic framework that aims to overcome these issues in the context of sentiment analysis. In particular, we employ unsupervised and reproducible subsymbolic techniques such as auto-regressive language models and kernel methods to build trustworthy symbolic representations that convert natural language to a sort of protolanguage and, hence, extract polarity from text in a completely interpretable and explainable manner.

In this paper, we launch a new Universal Dependencies treebank for an endangered language from Amazonia: Kakataibo, a Panoan language spoken in Peru. We first discuss the collaborative methodology implemented, which proved effective to create a treebank in the context of a Computational Linguistic course for undergraduates. Then, we describe the general details of the treebank and the language-specific considerations implemented for the proposed annotation. We finally conduct some experiments on part-of-speech tagging and syntactic dependency parsing. We focus on monolingual and transfer learning settings, where we study the impact of a Shipibo-Konibo treebank, another Panoan language resource.

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The Norwegian Colossal Corpus: A Text Corpus for Training Large Norwegian Language Models
Per Kummervold | Freddy Wetjen | Javier de la Rosa

Norwegian has been one of many languages lacking sufficient available text to train quality language models. In an attempt to bridge this gap, we introduce the Norwegian Colossal Corpus (NCC), which comprises 49GB of clean Norwegian textual data containing over 7B words. The NCC is composed of different and varied sources, ranging from books and newspapers to government documents and public reports, showcasing the various uses of the Norwegian language in society. The corpus contains mainly Norwegian Bokmål and Norwegian Nynorsk. Each document in the corpus is tagged with metadata that enables the creation of sub-corpora for specific needs. Its structure makes it easy to combine with large web archives that for licensing reasons could not be distributed together with the NCC. By releasing this corpus openly to the public, we hope to foster the creation of both better Norwegian language models and multilingual language models with support for Norwegian.

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Embeddings models for Buddhist Sanskrit
Ligeia Lugli | Matej Martinc | Andraž Pelicon | Senja Pollak

The paper presents novel resources and experiments for Buddhist Sanskrit, broadly defined here including all the varieties of Sanskrit in which Buddhist texts have been transmitted. We release a novel corpus of Buddhist texts, a novel corpus of general Sanskrit and word similarity and word analogy datasets for intrinsic evaluation of Buddhist Sanskrit embeddings models. We compare the performance of word2vec and fastText static embeddings models, with default and optimized parameter settings, as well as contextual models BERT and GPT-2, with different training regimes (including a transfer learning approach using the general Sanskrit corpus) and different embeddings construction regimes (given the encoder layers). The results show that for semantic similarity the fastText embeddings yield the best results, while for word analogy tasks BERT embeddings work the best. We also show that for contextual models the optimal layer combination for embedding construction is task dependant, and that pretraining the contextual embeddings models on a reference corpus of general Sanskrit is beneficial, which is a promising finding for future development of embeddings for less-resourced languages and domains.

This paper describes the process of data processing and training of an automatic speech recognition (ASR) system for Cook Islands Māori (CIM), an Indigenous language spoken by approximately 22,000 people in the South Pacific. We transcribed four hours of speech from adults and elderly speakers of the language and prepared two experiments. First, we trained three ASR systems: one statistical, Kaldi; and two based on Deep Learning, DeepSpeech and XLSR-Wav2Vec2. Wav2Vec2 tied with Kaldi for lowest character error rate (CER=6±1) and was slightly behind in word error rate (WER=23±2 versus WER=18±2 for Kaldi). This provides evidence that Deep Learning ASR systems are reaching the performance of statistical methods on small datasets, and that they can work effectively with extremely low-resource Indigenous languages like CIM. In the second experiment we used Wav2Vec2 to train models with held-out speakers. While the performance decreased (CER=15±7, WER=46±16), the system still showed considerable learning. We intend to use ASR to accelerate the documentation of CIM, using newly transcribed texts to improve the ASR and also generate teaching and language revitalization materials. The trained model is available under a license based on the Kaitiakitanga License, which provides for non-commercial use while retaining control of the model by the Indigenous community.

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A Generalized Approach to Protest Event Detection in German Local News
Gregor Wiedemann | Jan Matti Dollbaum | Sebastian Haunss | Priska Daphi | Larissa Daria Meier

Protest events provide information about social and political conflicts, the state of social cohesion and democratic conflict management, as well as the state of civil society in general. Social scientists are therefore interested in the systematic observation of protest events. With this paper, we release the first German language resource of protest event related article excerpts published in local news outlets. We use this dataset to train and evaluate transformer-based text classifiers to automatically detect relevant newspaper articles. Our best approach reaches a binary F1-score of 93.3 %, which is a promising result for our goal to support political science research. However, in a second experiment, we show that our model does not generalize equally well when applied to data from time periods and localities other than our training sample. To make protest event detection more robust, we test two ways of alternative preprocessing. First, we find that letting the classifier concentrate on sentences around protest keywords improves the F1-score for out-of-sample data up to +4 percentage points. Second, against our initial intuition, masking of named entities during preprocessing does not improve the generalization in terms of F1-scores. However, it leads to a significantly improved recall of the models.

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Evaluation of Transfer Learning and Domain Adaptation for Analyzing German-Speaking Job Advertisements
Ann-Sophie Gnehm | Eva Bühlmann | Simon Clematide

This paper presents text mining approaches on German-speaking job advertisements to enable social science research on the development of the labour market over the last 30 years. In order to build text mining applications providing information about profession and main task of a job, as well as experience and ICT skills needed, we experiment with transfer learning and domain adaptation. Our main contribution consists in building language models which are adapted to the domain of job advertisements, and their assessment on a broad range of machine learning problems. Our findings show the large value of domain adaptation in several respects. First, it boosts the performance of fine-tuned task-specific models consistently over all evaluation experiments. Second, it helps to mitigate rapid data shift over time in our special domain, and enhances the ability to learn from small updates with new, labeled task data. Third, domain-adaptation of language models is efficient: With continued in-domain pre-training we are able to outperform general-domain language models pre-trained on ten times more data. We share our domain-adapted language models and data with the research community.

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Pre-Training Language Models for Identifying Patronizing and Condescending Language: An Analysis
Carla Perez Almendros | Luis Espinosa Anke | Steven Schockaert

Patronizing and Condescending Language (PCL) is a subtle but harmful type of discourse, yet the task of recognizing PCL remains under-studied by the NLP community. Recognizing PCL is challenging because of its subtle nature, because available datasets are limited in size, and because this task often relies on some form of commonsense knowledge. In this paper, we study to what extent PCL detection models can be improved by pre-training them on other, more established NLP tasks. We find that performance gains are indeed possible in this way, in particular when pre-training on tasks focusing on sentiment, harmful language and commonsense morality. In contrast, for tasks focusing on political speech and social justice, no or only very small improvements were witnessed. These findings improve our understanding of the nature of PCL.

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HeLI-OTS, Off-the-shelf Language Identifier for Text
Tommi Jauhiainen | Heidi Jauhiainen | Krister Lindén

This paper introduces HeLI-OTS, an off-the-shelf text language identification tool using the HeLI language identification method. The HeLI-OTS language identifier is equipped with language models for 200 languages and licensed for academic as well as commercial use. We present the HeLI method and its use in our previous research. Then we compare the performance of the HeLI-OTS language identifier with that of fastText on two different data sets, showing that fastText favors the recall of common languages, whereas HeLI-OTS reaches both high recall and high precision for all languages. While introducing existing off-the-shelf language identification tools, we also give a picture of digital humanities-related research that uses such tools. The validity of the results of such research depends on the results given by the language identifier used, and especially for research focusing on the less common languages, the tendency to favor widely used languages might be very detrimental, which Heli-OTS is now able to remedy.

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Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages
Silvia Severini | Ayyoob Imani | Philipp Dufter | Hinrich Schütze

Parallel corpora are ideal for extracting a multilingual named entity (MNE) resource, i.e., a dataset of names translated into multiple languages. Prior work on extracting MNE datasets from parallel corpora required resources such as large monolingual corpora or word aligners that are unavailable or perform poorly for underresourced languages. We present CLC-BN, a new method for creating an MNE resource, and apply it to the Parallel Bible Corpus, a corpus of more than 1000 languages. CLC-BN learns a neural transliteration model from parallel-corpus statistics, without requiring any other bilingual resources, word aligners, or seed data. Experimental results show that CLC-BN clearly outperforms prior work. We release an MNE resource for 1340 languages and demonstrate its effectiveness in two downstream tasks: knowledge graph augmentation and bilingual lexicon induction.

In this paper we will discuss our preliminary work towards the construction of a WordNet for Old English, taking our inspiration from other similar WN construction projects for ancient languages such as Ancient Greek, Latin and Sanskrit. The Old English WordNet (OldEWN) will build upon this innovative work in a number of different ways which we articulate in the article, most importantly by treateating figurative meaning as a ‘first-class citizen’ in the structuring of the semantic system. From a more practical perspective we will describe our plan to utilize a pre-existing lexicographic resource and the naisc system to automatically compile a provisional version of the WordNet which will then be checked and enriched by Old English experts.

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A Framenet and Frame Annotator for German Social Media
Eckhard Bick

This paper presents PFN-DE, a new, parsing- and annotation-oriented framenet for German, with almost 15,000 frames, covering 11,300 verb lemmas. The resource was developed in the context of a Danish/German social-media study on hate speech and has a strong focus on coverage, robustness and cross-language comparability. A simple annotation scheme for argument roles meshes directly with the output of a syntactic parser, facilitating frame disambiguation through slot-filler conditions based on valency, syntactic function and semantic noun class. We discuss design principles for the framenet and the frame tagger using it, and present statistics for frame and role distribution at both the lexicon (type) and corpus (token) levels. In an evaluation run on Twitter data, the parser-based frame annotator achieved an overall F-score for frame senses of 93.6%.

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The Robotic Surgery Procedural Framebank
Marco Bombieri | Marco Rospocher | Simone Paolo Ponzetto | Paolo Fiorini

Robot-Assisted minimally invasive robotic surgery is the gold standard for the surgical treatment of many pathological conditions, and several manuals and academic papers describe how to perform these interventions. These high-quality, often peer-reviewed texts are the main study resource for medical personnel and consequently contain essential procedural domain-specific knowledge. The procedural knowledge therein described could be extracted, e.g., on the basis of semantic parsing models, and used to develop clinical decision support systems or even automation methods for some procedure’s steps. However, natural language understanding algorithms such as, for instance, semantic role labelers have lower efficacy and coverage issues when applied to domain others than those they are typically trained on (i.e., newswire text). To overcome this problem, starting from PropBank frames, we propose a new linguistic resource specific to the robotic-surgery domain, named Robotic Surgery Procedural Framebank (RSPF). We extract from robotic-surgical texts verbs and nouns that describe surgical actions and extend PropBank frames by adding any of new lemmas, frames or role sets required to cover missing lemmas, specific frames describing the surgical significance, or new semantic roles used in procedural surgical language. Our resource is publicly available and can be used to annotate corpora in the surgical domain to train and evaluate Semantic Role Labeling (SRL) systems in a challenging fine-grained domain setting.

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Representing the Toddler Lexicon: Do the Corpus and Semantics Matter?
Jennifer Weber | Eliana Colunga

Understanding child language development requires accurately representing children’s lexicons. However, much of the past work modeling children’s vocabulary development has utilized adult-based measures. The present investigation asks whether using corpora that captures the language input of young children more accurately represents children’s vocabulary knowledge. We present a newly-created toddler corpus that incorporates transcripts of child-directed conversations, the text of picture books written for preschoolers, and dialog from G-rated movies to approximate the language input a North American preschooler might hear. We evaluate the utility of the new corpus for modeling children’s vocabulary development by building and analyzing different semantic network models and comparing them to norms based on vocabulary norms for toddlers in this age range. More specifically, the relations between words in our semantic networks were derived from skip-gram neural networks (Word2Vec) trained on our toddler corpus or on Google news. Results revealed that the models built from the toddler corpus were more accurate at predicting toddler vocabulary growth than the adult-based corpus. These results speak to the importance of selecting a corpus that matches the population of interest.

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Organizing and Improving a Database of French Word Formation Using Formal Concept Analysis
Nyoman Juniarta | Olivier Bonami | Nabil Hathout | Fiammetta Namer | Yannick Toussaint

We apply Formal Concept Analysis (FCA) to organize and to improve the quality of Démonette2, a French derivational database, through a detection of both missing and spurious derivations in the database. We represent each derivational family as a graph. Given that the subgraph relation exists among derivational families, FCA can group families and represent them in a partially ordered set (poset). This poset is also useful for improving the database. A family is regarded as a possible anomaly (meaning that it may have missing and/or spurious derivations) if its derivational graph is almost, but not completely identical to a large number of other families.

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Towards a new Ontology for Sign Languages
Thierry Declerck

We present the current status of a new ontology for representing constitutive elements of Sign Languages (SL). This development emerged from investigations on how to represent multimodal lexical data in the OntoLex-Lemon framework, with the goal to publish such data in the Linguistic Linked Open Data (LLOD) cloud. While studying the literature and various sites dealing with sign languages, we saw the need to harmonise all the data categories (or features) defined and used in those sources, and to organise them in an ontology to which lexical descriptions in OntoLex-Lemon could be linked. We make the code of the first version of this ontology available, so that it can be further developed collaboratively by both the Linked Data and the SL communities

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Towards the Detection of a Semantic Gap in the Chain of Commonsense Knowledge Triples
Yoshihiko Hayashi

A commonsense knowledge resource organizes common sense that is not necessarily correct all the time, but most people are expected to know or believe. Such knowledge resources have recently been actively built and utilized in artificial intelligence, particularly natural language processing. In this paper, we discuss an important but not significantly discussed the issue of semantic gaps potentially existing in a commonsense knowledge graph and propose a machine learning-based approach to detect a semantic gap that may inhibit the proper chaining of knowledge triples. In order to establish this line of research, we created a pilot dataset from ConceptNet, in which chains consisting of two adjacent triples are sampled, and the validity of each chain is human-annotated. We also devised a few baseline methods for detecting the semantic gaps and compared them in small-scale experiments. Although the experimental results suggest that the detection of semantic gaps may not be a trivial task, we achieved several insights to further push this research direction, including the potential efficacy of sense embeddings and contextualized word representations enabled by a pre-trained language model.

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COPA-SSE: Semi-structured Explanations for Commonsense Reasoning
Ana Brassard | Benjamin Heinzerling | Pride Kavumba | Kentaro Inui

We present Semi-Structured Explanations for COPA (COPA-SSE), a new crowdsourced dataset of 9,747 semi-structured, English common sense explanations for Choice of Plausible Alternatives (COPA) questions. The explanations are formatted as a set of triple-like common sense statements with ConceptNet relations but freely written concepts. This semi-structured format strikes a balance between the high quality but low coverage of structured data and the lower quality but high coverage of free-form crowdsourcing. Each explanation also includes a set of human-given quality ratings. With their familiar format, the explanations are geared towards commonsense reasoners operating on knowledge graphs and serve as a starting point for ongoing work on improving such systems. The dataset is available at https://github.com/a-brassard/copa-sse.

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GRhOOT: Ontology of Rhetorical Figures in German
Ramona Kühn | Jelena Mitrović | Michael Granitzer

GRhOOT, the German RhetOrical OnTology, is a domain ontology of 110 rhetorical figures in the German language. The overall goal of building an ontology of rhetorical figures in German is not only the formal representation of different rhetorical figures, but also allowing for their easier detection, thus improving sentiment analysis, argument mining, detection of hate speech and fake news, machine translation, and many other tasks in which recognition of non-literal language plays an important role. The challenge of building such ontologies lies in classifying the figures and assigning adequate characteristics to group them, while considering their distinctive features. The ontology of rhetorical figures in the Serbian language was used as a basis for our work. Besides transferring and extending the concepts of the Serbian ontology, we ensured completeness and consistency by using description logic and SPARQL queries. Furthermore, we show a decision tree to identify figures and suggest a usage scenario on how the ontology can be utilized to collect and annotate data.

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Querying a Dozen Corpora and a Thousand Years with Fintan
Christian Chiarcos | Christian Fäth | Maxim Ionov

Large-scale diachronic corpus studies covering longer time periods are difficult if more than one corpus are to be consulted and, as a result, different formats and annotation schemas need to be processed and queried in a uniform, comparable and replicable manner. We describes the application of the Flexible Integrated Transformation and Annotation eNgineering (Fintan) platform for studying word order in German using syntactically annotated corpora that represent its entire written history. Focusing on nominal dative and accusative arguments, this study hints at two major phases in the development of scrambling in modern German. Against more recent assumptions, it supports the traditional view that word order flexibility decreased over time, but it also indicates that this was a relatively sharp transition in Early New High German. The successful case study demonstrates the potential of Fintan and the underlying LLOD technology for historical linguistics, linguistic typology and corpus linguistics. The technological contribution of this paper is to demonstrate the applicability of Fintan for querying across heterogeneously annotated corpora, as previously, it had only been applied for transformation tasks. With its focus on quantitative analysis, Fintan is a natural complement for existing multi-layer technologies that focus on query and exploration.

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The Index Thomisticus Treebank as Linked Data in the LiLa Knowledge Base
Francesco Mambrini | Marco Passarotti | Giovanni Moretti | Matteo Pellegrini

Although the Universal Dependencies initiative today allows for cross-linguistically consistent annotation of morphology and syntax in treebanks for several languages, syntactically annotated corpora are not yet interoperable with many lexical resources that describe properties of the words that occur therein. In order to cope with such limitation, we propose to adopt the principles of the Linguistic Linked Open Data community, to describe and publish dependency treebanks as LLOD. In particular, this paper illustrates the approach pursued in the LiLa Knowledge Base, which enables interoperability between corpora and lexical resources for Latin, to publish as Linguistic Linked Open Data the annotation layers of two versions of a Medieval Latin treebank (the Index Thomisticus Treebank).

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Building a Multilingual Taxonomy of Olfactory Terms with Timestamps
Stefano Menini | Teresa Paccosi | Serra Sinem Tekiroğlu | Sara Tonelli

Olfactory references play a crucial role in our memory and, more generally, in our experiences, since researchers have shown that smell is the sense that is most directly connected with emotions. Nevertheless, only few works in NLP have tried to capture this sensory dimension from a computational perspective. One of the main challenges is the lack of a systematic and consistent taxonomy of olfactory information, where concepts are organised also in a multi-lingual perspective. WordNet represents a valuable starting point in this direction, which can be semi-automatically extended taking advantage of Google n-grams and of existing language models. In this work we describe the process that has led to the semi-automatic development of a taxonomy for olfactory information in four languages (English, French, German and Italian), detailing the different steps and the intermediate evaluations. Along with being multi-lingual, the taxonomy also encloses temporal marks for olfactory terms thus making it a valuable resource for historical content analysis. The resource has been released and is freely available.

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Attention Understands Semantic Relations
Anastasia Chizhikova | Sanzhar Murzakhmetov | Oleg Serikov | Tatiana Shavrina | Mikhail Burtsev

Today, natural language processing heavily relies on pre-trained large language models. Even though such models are criticized for the poor interpretability, they still yield state-of-the-art solutions for a wide set of very different tasks. While lots of probing studies have been conducted to measure the models’ awareness of grammatical knowledge, semantic probing is less popular. In this work, we introduce the probing pipeline to study the representedness of semantic relations in transformer language models. We show that in this task, attention scores are nearly as expressive as the layers’ output activations, despite their lesser ability to represent surface cues. This supports the hypothesis that attention mechanisms are focusing not only on the syntactic relational information but also on the semantic one.

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Analysis of Dialogue in Human-Human Collaboration in Minecraft
Takuma Ichikawa | Ryuichiro Higashinaka

Recently, many studies have focused on developing dialogue systems that enable collaborative work; however, they rarely focus on creative tasks. Collaboration for creative work, in which humans and systems collaborate to create new value, will be essential for future dialogue systems. In this study, we collected 500 dialogues of human-human collaboration in Minecraft as a basis for developing a dialogue system that enables creative collaborative work. We conceived the Collaborative Garden Task, where two workers interact and collaborate in Minecraft to create a garden, and we collected dialogue, action logs, and subjective evaluations. We also collected third-person evaluations of the gardens and analyzed the relationship between dialogue and collaborative work that received high scores on the subjective and third-person evaluations in order to identify dialogic factors for high-quality collaborative work. We found that two essential aspects in creative collaborative work are performing more processes to ask for and agree on suggestions between workers and agreeing on a particular image of the final product in the early phase of work and then discussing changes and details.

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Data Collection for Empirically Determining the Necessary Information for Smooth Handover in Dialogue
Sanae Yamashita | Ryuichiro Higashinaka

Despite recent advances, dialogue systems still struggle to achieve fully autonomous transactions. Therefore, when a system encounters a problem, human operators need to take over the dialogue to complete the transaction. However, it is unclear what information should be presented to the operator when this handover takes place. In this study, we conducted a data collection experiment in which one of two operators talked to a user and switched with the other operator periodically while exchanging notes when the handovers took place. By examining these notes, it is possible to identify the information necessary for handing over the dialogue. We collected 60 dialogues in which two operators switched periodically while performing chat, consultation, and sales tasks in dialogue. We found that adjacency pairs are a useful representation for recording conversation history. In addition, we found that key-value-pair representation is also useful when there are underlying tasks, such as consultation and sales.

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The slurk Interaction Server Framework: Better Data for Better Dialog Models
Jana Götze | Maike Paetzel-Prüsmann | Wencke Liermann | Tim Diekmann | David Schlangen

This paper presents the slurk software, a lightweight interaction server for setting up dialog data collections and running experiments. slurk enables a multitude of settings including text-based, speech and video interaction between two or more humans or humans and bots, and a multimodal display area for presenting shared or private interactive context. The software is implemented in Python with an HTML and JavaScript frontend that can easily be adapted to individual needs. It also provides a setup for pairing participants on common crowdworking platforms such as Amazon Mechanical Turk and some example bot scripts for common interaction scenarios.

In this paper, we present the methodology of corpus design that will be used to study the comparison of influence between linguistic nudges with positive or negative influences and three conversational agents: robot, smart speaker, and human. We recruited forty-nine participants to form six groups. The conversational agents first asked the participants about their willingness to adopt five ecological habits and invest time and money in ecological problems. The participants were then asked the same questions but preceded by one linguistic nudge with positive or negative influence. The comparison of standard deviation and mean metrics of differences between these two notes (before the nudge and after) showed that participants were mainly affected by nudges with positive influence, even though several nudges with negative influence decreased the average note. In addition, participants from all groups were willing to spend more money than time on ecological problems. In general, our experiment’s early results suggest that a machine agent can influence participants to the same degree as a human agent. A better understanding of the power of influence of different conversational machines and the potential of influence of nudges of different polarities will lead to the development of ethical norms of human-computer interactions.

Building common ground with users is essential for dialogue agent systems and robots to interact naturally with people. While a few previous studies have investigated the process of building common ground in human-human dialogue, most of them have been conducted on the basis of text chat. In this study, we constructed a dialogue corpus to investigate the process of building common ground with a particular focus on the modality of dialogue and the social relationship between the participants in the process of building common ground, which are important but have not been investigated in the previous work. The results of our analysis suggest that adding the modality or developing the relationship between workers speeds up the building of common ground. Specifically, regarding the modality, the presence of video rather than only audio may unconsciously facilitate work, and as for the relationship, it is easier to convey information about emotions and turn-taking among friends than in first meetings. These findings and the corpus should prove useful for developing a system to support remote communication.

The ability to recognise emotions lends a conversational artificial intelligence a human touch. While emotions in chit-chat dialogues have received substantial attention, emotions in task-oriented dialogues remain largely unaddressed. This is despite emotions and dialogue success having equally important roles in a natural system. Existing emotion-annotated task-oriented corpora are limited in size, label richness, and public availability, creating a bottleneck for downstream tasks. To lay a foundation for studies on emotions in task-oriented dialogues, we introduce EmoWOZ, a large-scale manually emotion-annotated corpus of task-oriented dialogues. EmoWOZ is based on MultiWOZ, a multi-domain task-oriented dialogue dataset. It contains more than 11K dialogues with more than 83K emotion annotations of user utterances. In addition to Wizard-of-Oz dialogues from MultiWOZ, we collect human-machine dialogues within the same set of domains to sufficiently cover the space of various emotions that can happen during the lifetime of a data-driven dialogue system. To the best of our knowledge, this is the first large-scale open-source corpus of its kind. We propose a novel emotion labelling scheme, which is tailored to task-oriented dialogues. We report a set of experimental results to show the usability of this corpus for emotion recognition and state tracking in task-oriented dialogues.

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Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System
Eda Okur | Saurav Sahay | Lama Nachman

Contextually aware intelligent agents are often required to understand the users and their surroundings in real-time. Our goal is to build Artificial Intelligence (AI) systems that can assist children in their learning process. Within such complex frameworks, Spoken Dialogue Systems (SDS) are crucial building blocks to handle efficient task-oriented communication with children in game-based learning settings. We are working towards a multimodal dialogue system for younger kids learning basic math concepts. Our focus is on improving the Natural Language Understanding (NLU) module of the task-oriented SDS pipeline with limited datasets. This work explores the potential benefits of data augmentation with paraphrase generation for the NLU models trained on small task-specific datasets. We also investigate the effects of extracting entities for conceivably further data expansion. We have shown that paraphrasing with model-in-the-loop (MITL) strategies using small seed data is a promising approach yielding improved performance results for the Intent Recognition task.

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Towards Modelling Self-imposed Filter Bubbles in Argumentative Dialogue Systems
Annalena Aicher | Wolfgang Minker | Stefan Ultes

To build a well-founded opinion it is natural for humans to gather and exchange new arguments. Especially when being confronted with an overwhelming amount of information, people tend to focus on only the part of the available information that fits into their current beliefs or convenient opinions. To overcome this “self-imposed filter bubble” (SFB) in the information seeking process, it is crucial to identify influential indicators for the former. Within this paper we propose and investigate indicators for the the user’s SFB, mainly their Reflective User Engagement (RUE), their Personal Relevance (PR) ranking of content-related subtopics as well as their False (FK) and True Knowledge (TK) on the topic. Therefore, we analysed the answers of 202 participants of an online conducted user study, who interacted with our argumentative dialogue system BEA (“Building Engaging Argumentation”). Moreover, also the influence of different input/output modalities (speech/speech and drop-down menu/text) on the interaction with regard to the suggested indicators was investigated.

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Telling a Lie: Analyzing the Language of Information and Misinformation during Global Health Events
Ankit Aich | Natalie Parde

The COVID-19 pandemic and other global health events are unfortunately excellent environments for the creation and spread of misinformation, and the language associated with health misinformation may be typified by unique patterns and linguistic markers. Allowing health misinformation to spread unchecked can have devastating ripple effects; however, detecting and stopping its spread requires careful analysis of these linguistic characteristics at scale. We analyze prior investigations focusing on health misinformation, associated datasets, and detection of misinformation during health crises. We also introduce a novel dataset designed for analyzing such phenomena, comprised of 2.8 million news articles and social media posts spanning the early 1900s to the present. Our annotation guidelines result in strong agreement between independent annotators. We describe our methods for collecting this data and follow this with a thorough analysis of the themes and linguistic features that appear in information versus misinformation. Finally, we demonstrate a proof-of-concept misinformation detection task to establish dataset validity, achieving a strong performance benchmark (accuracy = 75%; F1 = 0.7).

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Misogyny and Aggressiveness Tend to Come Together and Together We Address Them
Arianna Muti | Francesco Fernicola | Alberto Barrón-Cedeño

We target the complementary binary tasks of identifying whether a tweet is misogynous and, if that is the case, whether it is also aggressive. We compare two ways to address these problems: one multi-class model that discriminates between all the classes at once: not misogynous, non aggressive-misogynous and aggressive-misogynous; as well as a cascaded approach where the binary classification is carried out separately (misogynous vs non-misogynous and aggressive vs non-aggressive) and then joined together. For the latter, two training and three testing scenarios are considered. Our models are built on top of AlBERTo and are evaluated on the framework of Evalita’s 2020 shared task on automatic misogyny and aggressiveness identification in Italian tweets. Our cascaded models —including the strong naïve baseline— outperform significantly the top submissions to Evalita, reaching state-of-the-art performance without relying on any external information.

In this paper, we discuss the development of a multilingual dataset annotated with a hierarchical, fine-grained tagset marking different types of aggression and the “context” in which they occur. The context, here, is defined by the conversational thread in which a specific comment occurs and also the “type” of discursive role that the comment is performing with respect to the previous comment. The initial dataset, being discussed here consists of a total 59,152 annotated comments in four languages - Meitei, Bangla, Hindi, and Indian English - collected from various social media platforms such as YouTube, Facebook, Twitter and Telegram. As is usual on social media websites, a large number of these comments are multilingual, mostly code-mixed with English. The paper gives a detailed description of the tagset being used for annotation and also the process of developing a multi-label, fine-grained tagset that has been used for marking comments with aggression and bias of various kinds including sexism (called gender bias in the tagset), religious intolerance (called communal bias in the tagset), class/caste bias and ethnic/racial bias. We also define and discuss the tags that have been used for marking the different discursive role being performed through the comments, such as attack, defend, etc. Finally, we present a basic statistical analysis of the dataset. The dataset is being incrementally made publicly available on the project website.

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Tweet Emotion Dynamics: Emotion Word Usage in Tweets from US and Canada
Krishnapriya Vishnubhotla | Saif M. Mohammad

Over the last decade, Twitter has emerged as one of the most influential forums for social, political, and health discourse. In this paper, we introduce a massive dataset of more than 45 million geo-located tweets posted between 2015 and 2021 from US and Canada (TUSC), especially curated for natural language analysis. We also introduce Tweet Emotion Dynamics (TED) — metrics to capture patterns of emotions associated with tweets over time. We use TED and TUSC to explore the use of emotion-associated words across US and Canada; across 2019 (pre-pandemic), 2020 (the year the pandemic hit), and 2021 (the second year of the pandemic); and across individual tweeters. We show that Canadian tweets tend to have higher valence, lower arousal, and higher dominance than the US tweets. Further, we show that the COVID-19 pandemic had a marked impact on the emotional signature of tweets posted in 2020, when compared to the adjoining years. Finally, we determine metrics of TED for 170,000 tweeters to benchmark characteristics of TED metrics at an aggregate level. TUSC and the metrics for TED will enable a wide variety of research on studying how we use language to express ourselves, persuade, communicate, and influence, with particularly promising applications in public health, affective science, social science, and psychology.

Social media posts containing hate speech are reproduced and redistributed at an accelerated pace, reaching greater audiences at a higher speed. We present a machine learning system for automatic detection of hate speech in Turkish, along with a hate speech dataset consisting of tweets collected in two separate domains. We first adopted a definition for hate speech that is in line with our goals and amenable to easy annotation; then designed the annotation schema for annotating the collected tweets. The Istanbul Convention dataset consists of tweets posted following the withdrawal of Turkey from the Istanbul Convention. The Refugees dataset was created by collecting tweets about immigrants by filtering based on commonly used keywords related to immigrants. Finally, we have developed a hate speech detection system using the transformer architecture (BERTurk), to be used as a baseline for the collected dataset. The binary classification accuracy is 77% when the system is evaluated using 5-fold cross-validation on the Istanbul Convention dataset and 71% for the Refugee dataset. We also tested a regression model with 0.66 and 0.83 RMSE on a scale of [0-4], for the Istanbul Convention and Refugees datasets.

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Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression
Ana-Maria Bucur | Adrian Cosma | Liviu P. Dinu

In this work, we explore the relationship between depression and manifestations of happiness in social media. While the majority of works surrounding depression focus on symptoms, psychological research shows that there is a strong link between seeking happiness and being diagnosed with depression. We make use of Positive-Unlabeled learning paradigm to automatically extract happy moments from social media posts of both controls and users diagnosed with depression, and qualitatively analyze them with linguistic tools such as LIWC and keyness information. We show that the life of depressed individuals is not always bleak, with positive events related to friends and family being more noteworthy to their lives compared to the more mundane happy events reported by control users.

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Evaluating Tokenizers Impact on OOVs Representation with Transformers Models
Alexandra Benamar | Cyril Grouin | Meryl Bothua | Anne Vilnat

Transformer models have achieved significant improvements in multiple downstream tasks in recent years. One of the main contributions of Transformers is their ability to create new representations for out-of-vocabulary (OOV) words. In this paper, we have evaluated three categories of OOVs: (A) new domain-specific terms (e.g., “eucaryote’” in microbiology), (B) misspelled words containing typos, and (C) cross-domain homographs (e.g., “arm” has different meanings in a clinical trial and anatomy). We use three French domain-specific datasets on the legal, medical, and energetical domains to robustly analyze these categories. Our experiments have led to exciting findings that showed: (1) It is easier to improve the representation of new words (A and B) than it is for words that already exist in the vocabulary of the Transformer models (C), (2) To ameliorate the representation of OOVs, the most effective method relies on adding external morpho-syntactic context rather than improving the semantic understanding of the words directly (fine-tuning) and (3) We cannot foresee the impact of minor misspellings in words because similar misspellings have different impacts on their representation. We believe that tackling the challenges of processing OOVs regarding their specificities will significantly help the domain adaptation aspect of BERT.

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Assessing the Quality of an Italian Crowdsourced Idiom Corpus:the Dodiom Experiment
Giuseppina Morza | Raffaele Manna | Johanna Monti

This paper describes how idiom-related language resources, collected through a crowdsourcing experiment carried out by means of Dodiom, a Game-with-a-purpose, have been analysed by language experts. The paper focuses on the criteria adopted for the data annotation and evaluation process. The main scope of this project is, indeed, the evaluation of the quality of the linguistic data obtained through a crowdsourcing project, namely to assess if the data provided and evaluated by the players who joined the game are actually considered of good quality by the language experts. Finally, results of the annotation and evaluation processes as well as future work are presented.

Medical data annotation requires highly qualified expertise. Despite the efforts devoted to medical entity linking in different languages, available data is very sparse in terms of both data volume and languages. In this work, we establish benchmarks for cross-lingual medical entity linking using clinical reports, clinical guidelines, and medical research papers. We present a test set filtering procedure designed to analyze the “hard cases” of entity linking approaching zero-shot cross-lingual transfer learning, evaluate state-of-the-art models, and draw several interesting conclusions based on our evaluation results.

The performance of Machine Translation (MT) systems varies significantly with inputs of diverging features such as topics, genres, and surface properties. Though there are many MT evaluation metrics that generally correlate with human judgments, they are not directly useful in identifying specific shortcomings of MT systems. In this demo, we present a benchmarking interface that enables improved evaluation of specific MT systems in isolation or multiple MT systems collectively by quantitatively evaluating their performance on many tasks across multiple domains and evaluation metrics. Further, it facilitates effective debugging and error analysis of MT output via the use of dynamic filters that help users hone in on problem sentences with specific properties, such as genre, topic, sentence length, etc. The interface can be extended to include additional filters such as lexical, morphological, and syntactic features. Aside from helping debug MT output, it can also help in identifying problems in reference translations and evaluation metrics.

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IceBATS: An Icelandic Adaptation of the Bigger Analogy Test Set
Steinunn Rut Friðriksdóttir | Hjalti Daníelsson | Steinþór Steingrímsson | Einar Sigurdsson

Word embedding models have become commonplace in a wide range of NLP applications. In order to train and use the best possible models, accurate evaluation is needed. For extrinsic evaluation of word embedding models, analogy evaluation sets have been shown to be a good quality estimator. We introduce an Icelandic adaptation of a large analogy dataset, BATS, evaluate it on three different word embedding models and show that our evaluation set is apt at measuring the capabilities of such models.

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Transfer Learning Methods for Domain Adaptation in Technical Logbook Datasets
Farhad Akhbardeh | Marcos Zampieri | Cecilia Ovesdotter Alm | Travis Desell

Event identification in technical logbooks poses challenges given the limited logbook data available in specific technical domains, the large set of possible classes, and logbook entries typically being in short form and non-standard technical language. Technical logbook data typically has both a domain, the field it comes from (e.g., automotive), and an application, what it is used for (e.g., maintenance). In order to better handle the problem of data scarcity, using a variety of technical logbook datasets, this paper investigates the benefits of using transfer learning from sources within the same domain (but different applications), from within the same application (but different domains) and from all available data. Results show that performing transfer learning within a domain provides statistically significant improvements, and in all cases but one the best performance. Interestingly, transfer learning from within the application or across the global dataset degrades results in all cases but one, which benefited from adding as much data as possible. A further analysis of the dataset similarities shows that the datasets with higher similarity scores performed better in transfer learning tasks, suggesting that this can be utilized to determine the effectiveness of adding a dataset in a transfer learning task for technical logbooks.

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Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data
Thomas Vakili | Anastasios Lamproudis | Aron Henriksson | Hercules Dalianis

Automatic de-identification is a cost-effective and straightforward way of removing large amounts of personally identifiable information from large and sensitive corpora. However, these systems also introduce errors into datasets due to their imperfect precision. These corruptions of the data may negatively impact the utility of the de-identified dataset. This paper de-identifies a very large clinical corpus in Swedish either by removing entire sentences containing sensitive data or by replacing sensitive words with realistic surrogates. These two datasets are used to perform domain adaptation of a general Swedish BERT model. The impact of the de-identification techniques is assessed by training and evaluating the models using six clinical downstream tasks. The results are then compared to a similar BERT model domain-adapted using an unaltered version of the clinical corpus. The results show that using an automatically de-identified corpus for domain adaptation does not negatively impact downstream performance. We argue that automatic de-identification is an efficient way of reducing the privacy risks of domain-adapted models and that the models created in this paper should be safe to distribute to other academic researchers.

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Dilated Convolutional Neural Networks for Lightweight Diacritics Restoration
Bálint Csanády | András Lukács

Diacritics restoration has become a ubiquitous task in the Latin-alphabet-based English-dominated Internet language environment. In this paper, we describe a small footprint 1D dilated convolution-based approach which operates on a character-level. We find that neural networks based on 1D dilated convolutions are competitive alternatives to solutions based on recurrent neural networks or linguistic modeling for the task of diacritics restoration. Our approach surpasses the performance of similarly sized models and is also competitive with larger models. A special feature of our solution is that it even runs locally in a web browser. We also provide a working example of this browser-based implementation. Our model is evaluated on different corpora, with emphasis on the Hungarian language. We performed comparative measurements about the generalization power of the model in relation to three Hungarian corpora. We also analyzed the errors to understand the limitation of corpus-based self-supervised training.

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Generating Artificial Texts as Substitution or Complement of Training Data
Vincent Claveau | Antoine Chaffin | Ewa Kijak

The quality of artificially generated texts has considerably improved with the advent of transformers. The question of using these models to generate learning data for supervised learning tasks naturally arises, especially when the original language resource cannot be distributed, or when it is small. In this article, this question is explored under 3 aspects: (i) are artificial data an efficient complement? (ii) can they replace the original data when those are not available or cannot be distributed for confidentiality reasons? (iii) can they improve the explainability of classifiers? Different experiments are carried out on classification tasks - namely sentiment analysis on product reviews and Fake News detection - using artificially generated data by fine-tuned GPT-2 models. The results show that such artificial data can be used in a certain extend but require pre-processing to significantly improve performance. We also show that bag-of-words approaches benefit the most from such data augmentation.

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From Pattern to Interpretation. Using Colibri Core to Detect Translation Patterns in the Peshitta.
Mathias Coeckelbergs

This article presents the first results of the CLARIAH-funded project ‘Patterns in Translation: Using Colibri Core for the Syriac Bible’ (PaTraCoSy). This project seeks to use Colibri Core to detect translation patterns in the Peshitta, the Syriac translation of the Hebrew Bible. We first describe how we constructed word and phrase alignment between these two texts. This step is necessary to succesfully implement the functionalities of Colibri Core. After this, we further describe our first investigations with the software. We describe how we use the built-in pattern modeller to detect n-gram and skipgram patterns in both Hebrew and Syriac texts. Colibri Core does not allow the creation of a bilingual model, which is why we compare the separate models. After a presentation of a few general insights on the overall translation behaviour of the Peshitta, we delve deeper into the concrete patterns we can detect by the n-gram/skipgram analysis. We provide multiple examples from the book of Genesis, a book which has been treated broadly in scholarly research into the Syriac translation, but which also appears to have interesting features based on our Colibri Core research.

Access to large pre-trained models of varied architectures, in many different languages, is central to the democratization of NLP. We introduce PAGnol, a collection of French GPT models. Using scaling laws, we efficiently train PAGnol-XL (1.5B parameters) with the same computational budget as CamemBERT, a model 13 times smaller. PAGnol-XL is the largest model trained from scratch for the French language. We plan to train increasingly large and performing versions of PAGnol, exploring the capabilities of French extreme-scale models. For this first release, we focus on the pre-training and scaling calculations underlining PAGnol. We fit a scaling law for compute for the French language, and compare it with its English counterpart. We find the pre-training dataset significantly conditions the quality of the outputs, with common datasets such as OSCAR leading to low-quality offensive text. We evaluate our models on discriminative and generative tasks in French, comparing to other state-of-the-art French and multilingual models, and reaching the state of the art in the abstract summarization task. Our research was conducted on the public GENCI Jean Zay supercomputer, and our models up to the Large are made publicly available.

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CEPOC: The Cambridge Exams Publishing Open Cloze dataset
Mariano Felice | Shiva Taslimipoor | Øistein E. Andersen | Paula Buttery

Open cloze tests are a standard type of exercise where examinees must complete a text by filling in the gaps without any given options to choose from. This paper presents the Cambridge Exams Publishing Open Cloze (CEPOC) dataset, a collection of open cloze tests from world-renowned English language proficiency examinations. The tests in CEPOC have been expertly designed and validated using standard principles in language research and assessment. They are prepared for language learners at different proficiency levels and hence classified into different CEFR levels (A2, B1, B2, C1, C2). This resource can be a valuable testbed for various NLP tasks. We perform a complete set of experiments on three tasks: gap filling, gap prediction, and CEFR text classification. We implement transformer-based systems based on pre-trained language models to model each task and use our dataset as a test set, providing promising benchmark results.

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ALBETO and DistilBETO: Lightweight Spanish Language Models
José Cañete | Sebastian Donoso | Felipe Bravo-Marquez | Andrés Carvallo | Vladimir Araujo

In recent years there have been considerable advances in pre-trained language models, where non-English language versions have also been made available. Due to their increasing use, many lightweight versions of these models (with reduced parameters) have also been released to speed up training and inference times. However, versions of these lighter models (e.g., ALBERT, DistilBERT) for languages other than English are still scarce. In this paper we present ALBETO and DistilBETO, which are versions of ALBERT and DistilBERT pre-trained exclusively on Spanish corpora. We train several versions of ALBETO ranging from 5M to 223M parameters and one of DistilBETO with 67M parameters. We evaluate our models in the GLUES benchmark that includes various natural language understanding tasks in Spanish. The results show that our lightweight models achieve competitive results to those of BETO (Spanish-BERT) despite having fewer parameters. More specifically, our larger ALBETO model outperforms all other models on the MLDoc, PAWS-X, XNLI, MLQA, SQAC and XQuAD datasets. However, BETO remains unbeaten for POS and NER. As a further contribution, all models are publicly available to the community for future research.

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On the Robustness of Cognate Generation Models
Winston Wu | David Yarowsky

We evaluate two popular neural cognate generation models’ robustness to several types of human-plausible noise (deletion, duplication, swapping, and keyboard errors, as well as a new type of error, phonological errors). We find that duplication and phonological substitution is least harmful, while the other types of errors are harmful. We present an in-depth analysis of the models’ results with respect to each error type to explain how and why these models perform as they do.

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CLISTER : A Corpus for Semantic Textual Similarity in French Clinical Narratives
Nicolas Hiebel | Olivier Ferret | Karën Fort | Aurélie Névéol

Modern Natural Language Processing relies on the availability of annotated corpora for training and evaluating models. Such resources are scarce, especially for specialized domains in languages other than English. In particular, there are very few resources for semantic similarity in the clinical domain in French. This can be useful for many biomedical natural language processing applications, including text generation. We introduce a definition of similarity that is guided by clinical facts and apply it to the development of a new French corpus of 1,000 sentence pairs manually annotated according to similarity scores. This new sentence similarity corpus is made freely available to the community. We further evaluate the corpus through experiments of automatic similarity measurement. We show that a model of sentence embeddings can capture similarity with state-of-the-art performance on the DEFT STS shared task evaluation data set (Spearman=0.8343). We also show that the corpus is complementary to DEFT STS.

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The Chinese Causative-Passive Homonymy Disambiguation: an adversarial Dataset for NLI and a Probing Task
Shanshan Xu | Katja Markert

The disambiguation of causative-passive homonymy (CPH) is potentially tricky for machines, as the causative and the passive are not distinguished by the sentences’ syntactic structure. By transforming CPH disambiguation to a challenging natural language inference (NLI) task, we present the first Chinese Adversarial NLI challenge set (CANLI). We show that the pretrained transformer model RoBERTa, fine-tuned on an existing large-scale Chinese NLI benchmark dataset, performs poorly on CANLI. We also employ Word Sense Disambiguation as a probing task to investigate to what extent the CPH feature is captured in the model’s internal representation. We find that the model’s performance on CANLI does not correspond to its internal representation of CPH, which is the crucial linguistic ability central to the CANLI dataset. CANLI is available on Hugging Face Datasets (Lhoest et al., 2021) at https://huggingface.co/datasets/sxu/CANLI

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Modeling Noise in Paraphrase Detection
Teemu Vahtola | Eetu Sjöblom | Jörg Tiedemann | Mathias Creutz

Noisy labels in training data present a challenging issue in classification tasks, misleading a model towards incorrect decisions during training. In this paper, we propose the use of a linear noise model to augment pre-trained language models to account for label noise in fine-tuning. We test our approach in a paraphrase detection task with various levels of noise and five different languages. Our experiments demonstrate the effectiveness of the additional noise model in making the training procedures more robust and stable. Furthermore, we show that this model can be applied without further knowledge about annotation confidence and reliability of individual training examples and we analyse our results in light of data selection and sampling strategies.

Discovered by (Austin,1962) and extensively promoted by (Searle, 1975), speech acts (SA) have been the object of extensive discussion in the philosophical and the linguistic literature, as well as in computational linguistics where the detection of SA have shown to be an important step in many down stream NLP applications. In this paper, we attempt to measure for the first time the role of SA on urgency detection in tweets, focusing on natural disasters. Indeed, SA are particularly relevant to identify intentions, desires, plans and preferences towards action, providing therefore actionable information that will help to set priorities for the human teams and decide appropriate rescue actions. To this end, we come up here with four main contributions: (1) A two-layer annotation scheme of SA both at the tweet and subtweet levels, (2) A new French dataset of 6,669 tweets annotated for both urgency and SA, (3) An in-depth analysis of the annotation campaign, highlighting the correlation between SA and urgency categories, and (4) A set of deep learning experiments to detect SA in a crisis corpus. Our results show that SA are correlated with urgency which is a first important step towards SA-aware NLP-based crisis management on social media.

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Towards a Cleaner Document-Oriented Multilingual Crawled Corpus
Julien Abadji | Pedro Ortiz Suarez | Laurent Romary | Benoît Sagot

The need for large corpora raw corpora has dramatically increased in recent years with the introduction of transfer learning and semi-supervised learning methods to Natural Language Processing. And while there have been some recent attempts to manually curate the amount of data necessary to train large language models, the main way to obtain this data is still through automatic web crawling. In this paper we take the existing multilingual web corpus OSCAR and its pipeline Ungoliant that extracts and classifies data from Common Crawl at the line level, and propose a set of improvements and automatic annotations in order to produce a new document-oriented version of OSCAR that could prove more suitable to pre-train large generative language models as well as hopefully other applications in Natural Language Processing and Digital Humanities.

We train several language models for Icelandic, including IceBERT, that achieve state-of-the-art performance in a variety of downstream tasks, including part-of-speech tagging, named entity recognition, grammatical error detection and constituency parsing. To train the models we introduce a new corpus of Icelandic text, the Icelandic Common Crawl Corpus (IC3), a collection of high quality texts found online by targeting the Icelandic top-level-domain .is. Several other public data sources are also collected for a total of 16GB of Icelandic text. To enhance the evaluation of model performance and to raise the bar in baselines for Icelandic, we manually translate and adapt the WinoGrande commonsense reasoning dataset. Through these efforts we demonstrate that a properly cleaned crawled corpus is sufficient to achieve state-of-the-art results in NLP applications for low to medium resource languages, by comparison with models trained on a curated corpus. We further show that initializing models using existing multilingual models can lead to state-of-the-art results for some downstream tasks.

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Adapting Language Models When Training on Privacy-Transformed Data
Tugtekin Turan | Dietrich Klakow | Emmanuel Vincent | Denis Jouvet

In recent years, voice-controlled personal assistants have revolutionized the interaction with smart devices and mobile applications. The collected data are then used by system providers to train language models (LMs). Each spoken message reveals personal information, hence removing private information from the input sentences is necessary. Our data sanitization process relies on recognizing and replacing named entities by other words from the same class. However, this may harm LM training because privacy-transformed data is unlikely to match the test distribution. This paper aims to fill the gap by focusing on the adaptation of LMs initially trained on privacy-transformed sentences using a small amount of original untransformed data. To do so, we combine class-based LMs, which provide an effective approach to overcome data sparsity in the context of n-gram LMs, and neural LMs, which handle longer contexts and can yield better predictions. Our experiments show that training an LM on privacy-transformed data result in a relative 11% word error rate (WER) increase compared to training on the original untransformed data, and adapting that model on a limited amount of original untransformed data leads to a relative 8% WER improvement over the model trained solely on privacy-transformed data.

We introduce a new benchmark for assessing the quality of text-to-text models for Polish. The benchmark consists of diverse tasks and datasets: KLEJ benchmark adapted for text-to-text, en-pl translation, summarization, and question answering. In particular, since summarization and question answering lack benchmark datasets for the Polish language, we describe in detail their construction and make them publicly available. Additionally, we present plT5 - a general-purpose text-to-text model for Polish that can be fine-tuned on various Natural Language Processing (NLP) tasks with a single training objective. Unsupervised denoising pre-training is performed efficiently by initializing the model weights with a multi-lingual T5 (mT5) counterpart. We evaluate the performance of plT5, mT5, Polish BART (plBART), and Polish GPT-2 (papuGaPT2). The plT5 scores top on all of these tasks except summarization, where plBART is best. In general (except summarization), the larger the model, the better the results. The encoder-decoder architectures prove to be better than the decoder-only equivalent.

The evaluation of Handwritten Text Recognition (HTR) models during their development is straightforward: because HTR is a supervised problem, the usual data split into training, validation, and test data sets allows the evaluation of models in terms of accuracy or error rates. However, the evaluation process becomes tricky as soon as we switch from development to application. A compilation of a new (and forcibly smaller) ground truth (GT) from a sample of the data that we want to apply the model on and the subsequent evaluation of models thereon only provides hints about the quality of the recognised text, as do confidence scores (if available) the models return. Moreover, if we have several models at hand, we face a model selection problem since we want to obtain the best possible result during the application phase. This calls for GT-free metrics to select the best model, which is why we (re-)introduce and compare different metrics, from simple, lexicon-based to more elaborate ones using standard language models and masked language models (MLM). We show that MLM-based evaluation can compete with lexicon-based methods, with the advantage that large and multilingual transformers are readily available, thus making compiling lexical resources for other metrics superfluous.

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A Semi-Automated Live Interlingual Communication Workflow Featuring Intralingual Respeaking: Evaluation and Benchmarking
Tomasz Korybski | Elena Davitti | Constantin Orasan | Sabine Braun

In this paper, we present a semi-automated workflow for live interlingual speech-to-text communication which seeks to reduce the shortcomings of existing ASR systems: a human respeaker works with a speaker-dependent speech recognition software (e.g., Dragon Naturally Speaking) to deliver punctuated same-language output of superior quality than obtained using out-of-the-box automatic speech recognition of the original speech. This is fed into a machine translation engine (the EU’s eTranslation) to produce live-caption ready text. We benchmark the quality of the output against the output of best-in-class (human) simultaneous interpreters working with the same source speeches from plenary sessions of the European Parliament. To evaluate the accuracy and facilitate the comparison between the two types of output, we use a tailored annotation approach based on the NTR model (Romero-Fresco and Pöchhacker, 2017). We find that the semi-automated workflow combining intralingual respeaking and machine translation is capable of generating outputs that are similar in terms of accuracy and completeness to the outputs produced in the benchmarking workflow, although the small scale of our experiment requires caution in interpreting this result.

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Are Embedding Spaces Interpretable? Results of an Intrusion Detection Evaluation on a Large French Corpus
Thibault Prouteau | Nicolas Dugué | Nathalie Camelin | Sylvain Meignier

Word embedding methods allow to represent words as vectors in a space that is structured using word co-occurrences so that words with close meanings are close in this space. These vectors are then provided as input to automatic systems to solve natural language processing problems. Because interpretability is a necessary condition to trusting such systems, interpretability of embedding spaces, the first link in the chain is an important issue. In this paper, we thus evaluate the interpretability of vectors extracted with two approaches: SPINE a k-sparse auto-encoder, and SINr, a graph-based method. This evaluation is based on a Word Intrusion Task with human annotators. It is operated using a large French corpus, and is thus, as far as we know, the first large-scale experiment regarding word embedding interpretability on this language. Furthermore, contrary to the approaches adopted in the literature where the evaluation is done on a small sample of frequent words, we consider a more realistic use-case where most of the vocabulary is kept for the evaluation. This allows to show how difficult this task is, even though SPINE and SINr show some promising results. In particular, SINr results are obtained with a very low amount of computation compared to SPINE, while being similarly interpretable.

In populous countries, pending legal cases have been growing exponentially. There is a need for developing techniques for processing and organizing legal documents. In this paper, we introduce a new corpus for structuring legal documents. In particular, we introduce a corpus of legal judgment documents in English that are segmented into topical and coherent parts. Each of these parts is annotated with a label coming from a list of pre-defined Rhetorical Roles. We develop baseline models for automatically predicting rhetorical roles in a legal document based on the annotated corpus. Further, we show the application of rhetorical roles to improve performance on the tasks of summarization and legal judgment prediction. We release the corpus and baseline model code along with the paper.

We evaluate an annotation schema for labeling logical fallacy types, originally developed for a crowd-sourcing annotation paradigm, now using an annotation paradigm of two trained linguist annotators. We apply the schema to a variety of different genres of text relating to the COVID-19 pandemic. Our linguist (as opposed to crowd-sourced) annotation of logical fallacies allows us to evaluate whether the annotation schema category labels are sufficiently clear and non-overlapping for both manual and, later, system assignment. We report inter-annotator agreement results over two annotation phases as well as a preliminary assessment of the corpus for training and testing a machine learning algorithm (Pattern-Exploiting Training) for fallacy detection and recognition. The agreement results and system performance underscore the challenging nature of this annotation task and suggest that the annotation schema and paradigm must be iteratively evaluated and refined in order to arrive at a set of annotation labels that can be reproduced by human annotators and, in turn, provide reliable training data for automatic detection and recognition systems.

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Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)
Amelie Wührl | Roman Klinger

Text mining and information extraction for the medical domain has focused on scientific text generated by researchers. However, their access to individual patient experiences or patient-doctor interactions is limited. On social media, doctors, patients and their relatives also discuss medical information. Individual information provided by laypeople complements the knowledge available in scientific text. It reflects the patient’s journey making the value of this type of data twofold: It offers direct access to people’s perspectives, and it might cover information that is not available elsewhere, including self-treatment or self-diagnose. Named entity recognition and relation extraction are methods to structure information that is available in unstructured text. However, existing medical social media corpora focused on a comparably small set of entities and relations. In contrast, we provide rich annotation layers to model patients’ experiences in detail. The corpus consists of medical tweets annotated with a fine-grained set of medical entities and relations between them, namely 14 entity (incl. environmental factors, diagnostics, biochemical processes, patients’ quality-of-life descriptions, pathogens, medical conditions, and treatments) and 20 relation classes (incl. prevents, influences, interactions, causes). The dataset consists of 2,100 tweets with approx. 6,000 entities and 2,200 relations.

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Improving Event Duration Question Answering by Leveraging Existing Temporal Information Extraction Data
Felix Virgo | Fei Cheng | Sadao Kurohashi

Understanding event duration is essential for understanding natural language. However, the amount of training data for tasks like duration question answering, i.e., McTACO, is very limited, suggesting a need for external duration information to improve this task. The duration information can be obtained from existing temporal information extraction tasks, such as UDS-T and TimeBank, where more duration data is available. A straightforward two-stage fine-tuning approach might be less likely to succeed given the discrepancy between the target duration question answering task and the intermediary duration classification task. This paper resolves this discrepancy by automatically recasting an existing event duration classification task from UDS-T to a question answering task similar to the target McTACO. We investigate the transferability of duration information by comparing whether the original UDS-T duration classification or the recast UDS-T duration question answering can be transferred to the target task. Our proposed model achieves a 13% Exact Match score improvement over the baseline on the McTACO duration question answering task, showing that the two-stage fine-tuning approach succeeds when the discrepancy between the target and intermediary tasks are resolved.

In this paper, we describe entity linking annotation over nested named entities in the recently released Russian NEREL dataset for information extraction. The NEREL collection is currently the largest Russian dataset annotated with entities and relations. It includes 933 news texts with annotation of 29 entity types and 49 relation types. The paper describes the main design principles behind NEREL’s entity linking annotation, provides its statistics, and reports evaluation results for several entity linking baselines. To date, 38,152 entity mentions in 933 documents are linked to Wikidata. The NEREL dataset is publicly available.

Named Entity Recognition (NER) is a foundational NLP task that aims to provide class labels like Person, Location, Organisation, Time, and Number to words in free text. Named Entities can also be multi-word expressions where the additional I-O-B annotation information helps label them during the NER annotation process. While English and European languages have considerable annotated data for the NER task, Indian languages lack on that front- both in terms of quantity and following annotation standards. This paper releases a significantly sized standard-abiding Hindi NER dataset containing 109,146 sentences and 2,220,856 tokens, annotated with 11 tags. We discuss the dataset statistics in all their essential detail and provide an in-depth analysis of the NER tag-set used with our data. The statistics of tag-set in our dataset shows a healthy per-tag distribution especially for prominent classes like Person, Location and Organisation. Since the proof of resource-effectiveness is in building models with the resource and testing the model on benchmark data and against the leader-board entries in shared tasks, we do the same with the aforesaid data. We use different language models to perform the sequence labelling task for NER and show the efficacy of our data by performing a comparative evaluation with models trained on another dataset available for the Hindi NER task. Our dataset helps achieve a weighted F1 score of 88.78 with all the tags and 92.22 when we collapse the tag-set, as discussed in the paper. To the best of our knowledge, no available dataset meets the standards of volume (amount) and variability (diversity), as far as Hindi NER is concerned. We fill this gap through this work, which we hope will significantly help NLP for Hindi. We release this dataset with our code and models for further research at https://github.com/cfiltnlp/HiNER

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Bootstrapping Text Anonymization Models with Distant Supervision
Anthi Papadopoulou | Pierre Lison | Lilja Øvrelid | Ildikó Pilán

We propose a novel method to bootstrap text anonymization models based on distant supervision. Instead of requiring manually labeled training data, the approach relies on a knowledge graph expressing the background information assumed to be publicly available about various individuals. This knowledge graph is employed to automatically annotate text documents including personal data about a subset of those individuals. More precisely, the method determines which text spans ought to be masked in order to guarantee k-anonymity, assuming an adversary with access to both the text documents and the background information expressed in the knowledge graph. The resulting collection of labeled documents is then used as training data to fine-tune a pre-trained language model for text anonymization. We illustrate this approach using a knowledge graph extracted from Wikidata and short biographical texts from Wikipedia. Evaluation results with a RoBERTa-based model and a manually annotated collection of 553 summaries showcase the potential of the approach, but also unveil a number of issues that may arise if the knowledge graph is noisy or incomplete. The results also illustrate that, contrary to most sequence labeling problems, the text anonymization task may admit several alternative solutions.

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Natural Questions in Icelandic
Vésteinn Snæbjarnarson | Hafsteinn Einarsson

We present the first extractive question answering (QA) dataset for Icelandic, Natural Questions in Icelandic (NQiI). Developing such datasets is important for the development and evaluation of Icelandic QA systems. It also aids in the development of QA methods that need to work for a wide range of morphologically and grammatically different languages in a multilingual setting. The dataset was created by asking contributors to come up with questions they would like to know the answer to. Later, they were tasked with finding answers to each others questions following a previously published methodology. The questions are Natural in the sense that they are real questions posed out of interest in knowing the answer. The complete dataset contains 18 thousand labeled entries of which 5,568 are directly suitable for training an extractive QA system for Icelandic. The dataset is a valuable resource for Icelandic which we demonstrate by creating and evaluating a system capable of extractive QA in Icelandic.

Quality assurance (QA) is an essential though underdeveloped part of the data annotation process. Although QA is supported to some extent in existing annotation tools, comprehensive support for QA is not standardly provided. In this paper we contribute QA4IE, a comprehensive QA tool for information extraction, which can (1) detect potential problems in text annotations in a timely manner, (2) accurately assess the quality of annotations, (3) visually display and summarize annotation discrepancies among annotation team members, (4) provide a comprehensive statistics report, and (5) support viewing of annotated documents interactively. This paper offers a competitive analysis comparing QA4IE and other popular annotation tools and demonstrates its features, usage, and effectiveness through a case study. The Python code, documentation, and demonstration video are available publicly at https://github.com/CC-RMD-EpiBio/QA4IE.

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A New Dataset for Topic-Based Paragraph Classification in Genocide-Related Court Transcripts
Miriam Schirmer | Udo Kruschwitz | Gregor Donabauer

Recent progress in natural language processing has been impressive in many different areas with transformer-based approaches setting new benchmarks for a wide range of applications. This development has also lowered the barriers for people outside the NLP community to tap into the tools and resources applied to a variety of domain-specific applications. The bottleneck however still remains the lack of annotated gold-standard collections as soon as one’s research or professional interest falls outside the scope of what is readily available. One such area is genocide-related research (also including the work of experts who have a professional interest in accessing, exploring and searching large-scale document collections on the topic, such as lawyers). We present GTC (Genocide Transcript Corpus), the first annotated corpus of genocide-related court transcripts which serves three purposes: (1) to provide a first reference corpus for the community, (2) to establish benchmark performances (using state-of-the-art transformer-based approaches) for the new classification task of paragraph identification of violence-related witness statements, (3) to explore first steps towards transfer learning within the domain. We consider our contribution to be addressing in particular this year’s hot topic on Language Technology for All.

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DeepREF: A Framework for Optimized Deep Learning-based Relation Classification
Igor Nascimento | Rinaldo Lima | Adrian-Gabriel Chifu | Bernard Espinasse | Sébastien Fournier

The Relation Extraction (RE) is an important basic Natural Language Processing (NLP) for many applications, such as search engines, recommender systems, question-answering systems and others. There are many studies in this subarea of NLP that continue to be explored, such as SemEval campaigns (2010 to 2018), or DDI Extraction (2013).For more than ten years, different RE systems using mainly statistical models have been proposed as well as the frameworks to develop them. This paper focuses on frameworks allowing to develop such RE systems using deep learning models. Such frameworks should make it possible to reproduce experiments of various deep learning models and pre-processing techniques proposed in various publications. Currently, there are very few frameworks of this type, and we propose a new open and optimizable framework, called DeepREF, which is inspired by the OpenNRE and REflex existing frameworks. DeepREF allows the employment of various deep learning models, to optimize their use, to identify the best inputs and to get better results with each data set for RE and compare with other experiments, making ablation studies possible. The DeepREF Framework is evaluated on several reference corpora from various application domains.

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Exploring Data Augmentation Strategies for Hate Speech Detection in Roman Urdu
Ubaid Azam | Hammad Rizwan | Asim Karim

In an era where social media platform users are growing rapidly, there has been a marked increase in hateful content being generated; to combat this, automatic hate speech detection systems are a necessity. For this purpose, researchers have recently focused their efforts on developing datasets, however, the vast majority of them have been generated for the English language, with only a few available for low-resource languages such as Roman Urdu. Furthermore, what few are available have small number of samples that pertain to hateful classes and these lack variations in topics and content. Thus, deep learning models trained on such datasets perform poorly when deployed in the real world. To improve performance the option of collecting and annotating more data can be very costly and time consuming. Thus, data augmentation techniques need to be explored to exploit already available datasets to improve model generalizability. In this paper, we explore different data augmentation techniques for the improvement of hate speech detection in Roman Urdu. We evaluate these augmentation techniques on two datasets. We are able to improve performance in the primary metric of comparison (F1 and Macro F1) as well as in recall, which is impertinent for human-in-the-loop AI systems.

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Incorporating LIWC in Neural Networks to Improve Human Trait and Behavior Analysis in Low Resource Scenarios
Isil Yakut Kilic | Shimei Pan

Psycholinguistic knowledge resources have been widely used in constructing features for text-based human trait and behavior analysis. Recently, deep neural network (NN)-based text analysis methods have gained dominance due to their high prediction performance. However, NN-based methods may not perform well in low resource scenarios where the ground truth data is limited (e.g., only a few hundred labeled training instances are available). In this research, we investigate diverse methods to incorporate Linguistic Inquiry and Word Count (LIWC), a widely-used psycholinguistic lexicon, in NN models to improve human trait and behavior analysis in low resource scenarios. We evaluate the proposed methods in two tasks: predicting delay discounting and predicting drug use based on social media posts. The results demonstrate that our methods perform significantly better than baselines that use only LIWC or only NN-based feature learning methods. They also performed significantly better than published results on the same dataset.

In the last few years, several attempts have been made on extracting information from material science research domain. Material Science research articles are a rich source of information about various entities related to material science such as names of the materials used for experiments, the computational software used along with its parameters, the method used in the experiments, etc. But the distribution of these entities is not uniform across different sections of research articles. Most of the sentences in the research articles do not contain any entity. In this work, we first use a sentence-level classifier to identify sentences containing at least one entity mention. Next, we apply the information extraction models only on the filtered sentences, to extract various entities of interest. Our experiments for named entity recognition in the material science research articles show that this additional sentence-level classification step helps to improve the F1 score by more than 4%.

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A Twitter Corpus for Named Entity Recognition in Turkish
Buse Çarık | Reyyan Yeniterzi

This paper introduces a new Turkish Twitter Named Entity Recognition dataset. The dataset, which consists of 5000 tweets from a year-long period, was labeled by multiple annotators with a high agreement score. The dataset is also diverse in terms of the named entity types as it contains not only person, organization, and location but also time, money, product, and tv-show categories. Our initial experiments with pretrained language models (like BertTurk) over this dataset returned F1 scores of around 80%. We share this dataset publicly.

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A STEP towards Interpretable Multi-Hop Reasoning:Bridge Phrase Identification and Query Expansion
Fan Luo | Mihai Surdeanu

We propose an unsupervised method for the identification of bridge phrases in multi-hop question answering (QA). Our method constructs a graph of noun phrases from the question and the available context, and applies the Steiner tree algorithm to identify the minimal sub-graph that connects all question phrases. Nodes in the sub-graph that bridge loosely-connected or disjoint subsets of question phrases due to low-strength semantic relations are extracted as bridge phrases. The identified bridge phrases are then used to expand the query based on the initial question, helping in increasing the relevance of evidence that has little lexical overlap or semantic relation with the question. Through an evaluation on HotpotQA, a popular dataset for multi-hop QA, we show that our method yields: (a) improved evidence retrieval, (b) improved QA performance when using the retrieved sentences; and (c) effective and faithful explanations when answers are provided.

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Question Generation and Answering for exploring Digital Humanities collections
Frederic Bechet | Elie Antoine | Jérémy Auguste | Géraldine Damnati

This paper introduces the question answering paradigm as a way to explore digitized archive collections for Social Science studies. In particular, we are interested in evaluating largely studied question generation and question answering approaches on a new type of documents, as a step forward beyond traditional benchmark evaluations. Question generation can be used as a way to provide enhanced training material for Machine Reading Question Answering algorithms but also has its own purpose in this paradigm, where relevant questions can be used as a way to create explainable links between documents. To this end, generating large amounts of question is not the only motivation, but we need to include qualitative and semantic control to the generation process. We propose a new approach for question generation, relying on a BART Transformer based generative model, for which input data are enriched by semantic constraints. Question generation and answering are evaluated on several French corpora, and the whole approach is validated on a new corpus of digitized archive collection of a French Social Science journal.

This paper provides an overview of the xDD/LAPPS Grid framework and provides results of evaluating the AskMe retrievalengine using the BEIR benchmark datasets. Our primary goal is to determine a solid baseline of performance to guide furtherdevelopment of our retrieval capabilities. Beyond this, we aim to dig deeper to determine when and why certain approachesperform well (or badly) on both in-domain and out-of-domain data, an issue that has to date received relatively little attention.

Electronic Health Records contain a lot of information in natural language that is not expressed in the structured clinical data. Especially in the case of new diseases such as COVID-19, this information is crucial to get a better understanding of patient recovery patterns and factors that may play a role in it. However, the language in these records is very different from standard language and generic natural language processing tools cannot easily be applied out-of-the-box. In this paper, we present a fine-tuned Dutch language model specifically developed for the language in these health records that can determine the functional level of patients according to a standard coding framework from the World Health Organization. We provide evidence that our classification performs at a sufficient level to generate patient recovery patterns that can be used in the future to analyse factors that contribute to the rehabilitation of COVID-19 patients and to predict individual patient recovery of functioning.

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Hierarchical Aggregation of Dialectal Data for Arabic Dialect Identification
Nurpeiis Baimukan | Houda Bouamor | Nizar Habash

Arabic is a collection of dialectal variants that are historically related but significantly different. These differences can be seen across regions, countries, and even cities in the same countries. Previous work on Arabic Dialect identification has focused mainly on specific dialect levels (region, country, province, or city) using level-specific resources; and different efforts used different schemas and labels. In this paper, we present the first effort aiming at defining a standard unified three-level hierarchical schema (region-country-city) for dialectal Arabic classification. We map 29 different data sets to this unified schema, and use the common mapping to facilitate aggregating these data sets. We test the value of such aggregation by building language models and using them in dialect identification. We make our label mapping code and aggregated language models publicly available.

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Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification
Lukas Wertz | Katsiaryna Mirylenka | Jonas Kuhn | Jasmina Bogojeska

Large scale, multi-label text datasets with high numbers of different classes are expensive to annotate, even more so if they deal with domain specific language. In this work, we aim to build classifiers on these datasets using Active Learning in order to reduce the labeling effort. We outline the challenges when dealing with extreme multi-label settings and show the limitations of existing Active Learning strategies by focusing on their effectiveness as well as efficiency in terms of computational cost. In addition, we present five multi-label datasets which were compiled from hierarchical classification tasks to serve as benchmarks in the context of extreme multi-label classification for future experiments. Finally, we provide insight into multi-class, multi-label evaluation and present an improved classifier architecture on top of pre-trained transformer language models.

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German Light Verb Constructions in Business Process Models
Kristin Kutzner | Ralf Laue

We present a resource of German light verb constructions extracted from textual labels in graphical business process models. Those models depict the activities in processes in an organization in a semi-formal way. From a large range of sources, we compiled a repository of 2,301 business process models. Their textual labels (altogether 52,963 labels) were analyzed. This produced a list of 5,246 occurrences of 846 light verb constructions. We found that the light verb constructions that occur in business process models differ from light verb constructions that have been analyzed in other texts. Hence, we conclude that texts in graphical business process models represent a specific type of texts that is worth to be studied on its own. We think that our work is a step towards better automatic analysis of business process models because understanding the actual meaning of activity labels is a prerequisite for detecting certain types of modelling problems.

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PhysNLU: A Language Resource for Evaluating Natural Language Understanding and Explanation Coherence in Physics
Jordan Meadows | Zili Zhou | André Freitas

In order for language models to aid physics research, they must first encode representations of mathematical and natural language discourse which lead to coherent explanations, with correct ordering and relevance of statements. We present a collection of datasets developed to evaluate the performance of language models in this regard, which measure capabilities with respect to sentence ordering, position, section prediction, and discourse coherence. Analysis of the data reveals the classes of arguments and sub-disciplines which are most common in physics discourse, as well as the sentence-level frequency of equations and expressions. We present baselines that demonstrate how contemporary language models are challenged by coherence related tasks in physics, even when trained on mathematical natural language objectives.

Reducing the complexity of texts by applying an Automatic Text Simplification (ATS) system has been sparking interest inthe area of Natural Language Processing (NLP) for several years and a number of methods and evaluation campaigns haveemerged targeting lexical and syntactic transformations. In recent years, several studies exploit deep learning techniques basedon very large comparable corpora. Yet the lack of large amounts of corpora (original-simplified) for French has been hinderingthe development of an ATS tool for this language. In this paper, we present our system, which is based on a combination ofmethods relying on word embeddings for lexical simplification and rule-based strategies for syntax and discourse adaptations. We present an evaluation of the lexical, syntactic and discourse-level simplifications according to automatic and humanevaluations. We discuss the performances of our system at the lexical, syntactic, and discourse levels

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AiRO - an Interactive Learning Tool for Children at Risk of Dyslexia
Peter Juel Henrichsen | Stine Fuglsang Engmose

This paper presents the AiRO learning tool, which is designed for use in classrooms and homes by children at risk of developing dyslexia. The tool is based on the client-server architecture with a graphical and auditive front end (providing the interaction with the learner) and all NLP-related components located at the back end (analysing the pupil’s input, deciding on the system’s response, preparing speech synthesis and other feedback, logging the pupil’s performance etc). AiRO software consists of independent modules for easy maintenance, e.g., upgrading the didactics or preparing AiROs for other languages. This paper also reports on our first tests ‘in vivo’ (November 2021) with 49 pupils (aged 6). The subjects completed 16 AiRO sessions over a four-week period. The subjects were pre- and post-tested on spelling and reading. The experimental group significantly out-performed the control group, suggesting that a new IT-supported teaching strategy may be within reach. A collection of AiRO resources (language materials, software, synthetic voice) are available as open source. At LREC, we shall present a demo of the AiRO learning tool.

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Creating a Basic Language Resource Kit for Faroese
Annika Simonsen | Sandra Saxov Lamhauge | Iben Nyholm Debess | Peter Juel Henrichsen

The biggest challenges we face in developing LR and LT for Faroese is the lack of existing resources. A few resources already exist for Faroese, but many of them are either of insufficient size and quality or are not easily accessible. Therefore, the Faroese ASR project, Ravnur, set out to make a BLARK for Faroese. The BLARK is still in the making, but many of its resources have already been produced or collected. The LR status is framed by mentioning existing LR of relevant size and quality. The specific components of the BLARK are presented as well as the working principles behind the BLARK. The BLARK will be a pillar in Faroese LR, being relatively substantial in both size, quality, and diversity. It will be open-source, inviting other small languages to use it as an inspiration to create their own BLARK. We comment on the faulty yet sprouting LT situation in the Faroe Islands. The LR and LT challenges are not solved with just a BLARK. Some initiatives are therefore proposed to better the prospects of Faroese LT. The open-source principle of the project should facilitate further development.

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Developing a Spell and Grammar Checker for Icelandic using an Error Corpus
Hulda Óladóttir | Þórunn Arnardóttir | Anton Ingason | Vilhjálmur Þorsteinsson

A lack of datasets for spelling and grammatical error correction in Icelandic, along with language-specific issues, has caused a dearth of spell and grammar checking systems for the language. We present the first open-source spell and grammar checking tool for Icelandic, using an error corpus at all stages. This error corpus was in part created to aid in the development of the tool. The system is built with a rule-based tool stack comprising a tokenizer, a morphological tagger, and a parser. For token-level error annotation, tokenization rules, word lists, and a trigram model are used in error detection and correction. For sentence-level error annotation, we use specific error grammar rules in the parser as well as regex-like patterns to search syntax trees. The error corpus gives valuable insight into the errors typically made when Icelandic text is written, and guided each development phase in a test-driven manner. We assess the system’s performance with both automatic and human evaluation, using the test set in the error corpus as a reference in the automatic evaluation. The data in the error corpus development set proved useful in various ways for error detection and correction.

Transcripts of teaching episodes can be effective tools to understand discourse patterns in classroom instruction. According to most educational experts, sustained classroom discourse is a critical component of equitable, engaging, and rich learning environments for students. This paper describes the TalkMoves dataset, composed of 567 human-annotated K-12 mathematics lesson transcripts (including entire lessons or portions of lessons) derived from video recordings. The set of transcripts primarily includes in-person lessons with whole-class discussions and/or small group work, as well as some online lessons. All of the transcripts are human-transcribed, segmented by the speaker (teacher or student), and annotated at the sentence level for ten discursive moves based on accountable talk theory. In addition, the transcripts include utterance-level information in the form of dialogue act labels based on the Switchboard Dialog Act Corpus. The dataset can be used by educators, policymakers, and researchers to understand the nature of teacher and student discourse in K-12 math classrooms. Portions of this dataset have been used to develop the TalkMoves application, which provides teachers with automated, immediate, and actionable feedback about their mathematics instruction.

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Automating Idea Unit Segmentation and Alignment for Assessing Reading Comprehension via Summary Protocol Analysis
Marcello Gecchele | Hiroaki Yamada | Takenobu Tokunaga | Yasuyo Sawaki | Mika Ishizuka

In this paper, we approach summary evaluation from an applied linguistics (AL) point of view. We provide computational tools to AL researchers to simplify the process of Idea Unit (IU) segmentation. The IU is a segmentation unit that can identify chunks of information. These chunks can be compared across documents to measure the content overlap between a summary and its source text. We propose a full revision of the annotation guidelines to allow machine implementation. The new guideline also improves the inter-annotator agreement, rising from 0.547 to 0.785 (Cohen’s Kappa). We release L2WS 2021, a IU gold standard corpus composed of 40 manually annotated student summaries. We propose IUExtract; i.e. the first automatic segmentation algorithm based on the IU. The algorithm was tested over the L2WS 2021 corpus. Our results are promising, achieving a precision of 0.789 and a recall of 0.844. We tested an existing approach to IU alignment via word embeddings with the state of the art model SBERT. The recorded precision for the top 1 aligned pair of IUs was 0.375. We deemed this result insufficient for effective automatic alignment. We propose “SAT”, an online tool to facilitate the collection of alignment gold standards for future training.

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IRAC: A Domain-Specific Annotated Corpus of Implicit Reasoning in Arguments
Keshav Singh | Naoya Inoue | Farjana Sultana Mim | Shoichi Naito | Kentaro Inui

The task of implicit reasoning generation aims to help machines understand arguments by inferring plausible reasonings (usually implicit) between argumentative texts. While this task is easy for humans, machines still struggle to make such inferences and deduce the underlying reasoning. To solve this problem, we hypothesize that as human reasoning is guided by innate collection of domain-specific knowledge, it might be beneficial to create such a domain-specific corpus for machines. As a starting point, we create the first domain-specific resource of implicit reasonings annotated for a wide range of arguments, which can be leveraged to empower machines with better implicit reasoning generation ability. We carefully design an annotation framework to collect them on a large scale through crowdsourcing and show the feasibility of creating a such a corpus at a reasonable cost and high-quality. Our experiments indicate that models trained with domain-specific implicit reasonings significantly outperform domain-general models in both automatic and human evaluations. To facilitate further research towards implicit reasoning generation in arguments, we present an in-depth analysis of our corpus and crowdsourcing methodology, and release our materials (i.e., crowdsourcing guidelines and domain-specific resource of implicit reasonings).

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Conversational Speech Recognition Needs Data? Experiments with Austrian German
Julian Linke | Philip N. Garner | Gernot Kubin | Barbara Schuppler

Conversational speech represents one of the most complex of automatic speech recognition (ASR) tasks owing to the high inter-speaker variation in both pronunciation and conversational dynamics. Such complexity is particularly sensitive to low-resourced (LR) scenarios. Recent developments in self-supervision have allowed such scenarios to take advantage of large amounts of otherwise unrelated data. In this study, we characterise an (LR) Austrian German conversational task. We begin with a non-pre-trained baseline and show that fine-tuning of a model pre-trained using self-supervision leads to improvements consistent with those in the literature; this extends to cases where a lexicon and language model are included. We also show that the advantage of pre-training indeed arises from the larger database rather than the self-supervision. Further, by use of a leave-one-conversation out technique, we demonstrate that robustness problems remain with respect to inter-speaker and inter-conversation variation. This serves to guide where future research might best be focused in light of the current state-of-the-art.

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A Benchmark Corpus for the Detection of Automatically Generated Text in Academic Publications
Vijini Liyanage | Davide Buscaldi | Adeline Nazarenko

Automatic text generation based on neural language models has achieved performance levels that make the generated text almost indistinguishable from those written by humans. Despite the value that text generation can have in various applications, it can also be employed for malicious tasks. The diffusion of such practices represent a threat to the quality of academic publishing. To address these problems, we propose in this paper two datasets comprised of artificially generated research content: a completely synthetic dataset and a partial text substitution dataset. In the first case, the content is completely generated by the GPT-2 model after a short prompt extracted from original papers. The partial or hybrid dataset is created by replacing several sentences of abstracts with sentences that are generated by the Arxiv-NLP model. We evaluate the quality of the datasets comparing the generated texts to aligned original texts using fluency metrics such as BLEU and ROUGE. The more natural the artificial texts seem, the more difficult they are to detect and the better is the benchmark. We also evaluate the difficulty of the task of distinguishing original from generated text by using state-of-the-art classification models.

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Building a Dataset for Automatically Learning to Detect Questions Requiring Clarification
Ivano Lauriola | Kevin Small | Alessandro Moschitti

Question Answering (QA) systems aim to return correct and concise answers in response to user questions. QA research generally assumes all questions are intelligible and unambiguous, which is unrealistic in practice as questions frequently encountered by virtual assistants are ambiguous or noisy. In this work, we propose to make QA systems more robust via the following two-step process: (1) classify if the input question is intelligible and (2) for such questions with contextual ambiguity, return a clarification question. We describe a new open-domain clarification corpus containing user questions sampled from Quora, which is useful for building machine learning approaches to solving these tasks.

This paper introduces the Austrian German sentiment dictionary ALPIN to account for the lack of resources for dictionary-based sentiment analysis in this specific variety of German, which is characterized by lexical idiosyncrasies that also affect word sentiment. The proposed language resource is based on Austrian news media in the field of politics, an austriacism list based on different resources and a posting data set based on a popular Austrian news media. Different resources are used to increase the diversity of the resulting language resource. Extensive crowd-sourcing is performed followed by evaluation and automatic conversion into sentiment scores. We show that crowd-sourcing enables the creation of a sentiment dictionary for the Austrian German domain. Additionally, the different parts of the sentiment dictionary are evaluated to show their impact on the resulting resource. Furthermore, the proposed dictionary is utilized in a web application and available for future research and free to use for anyone.

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Text Classification and Prediction in the Legal Domain
Minh-Quoc Nghiem | Paul Baylis | André Freitas | Sophia Ananiadou

We present a case study on the application of text classification and legal judgment prediction for flight compensation. We combine transformer-based classification models to classify responses from airlines and incorporate text data with other data types to predict a legal claim being successful. Our experimental evaluations show that our models achieve consistent and significant improvements over baselines and even outperformed human prediction when predicting a claim being successful. These models were integrated into an existing claim management system, providing substantial productivity gains for handling the case lifecycle, currently supporting several thousands of monthly processes.

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I still have Time(s): Extending HeidelTime for German Texts
Andy Luecking | Manuel Stoeckel | Giuseppe Abrami | Alexander Mehler

HeidelTime is one of the most widespread and successful tools for detecting temporal expressions in texts. Since HeidelTime’s pattern matching system is based on regular expression, it can be extended in a convenient way. We present such an extension for the German resources of HeidelTime: HeidelTimeExt. The extension has been brought about by means of observing false negatives within real world texts and various time banks. The gain in coverage is 2.7 % or 8.5 %, depending on the admitted degree of potential overgeneralization. We describe the development of HeidelTimeExt, its evaluation on text samples from various genres, and share some linguistic observations. HeidelTimeExt can be obtained from https://github.com/texttechnologylab/heideltime.

The aim of this study was to compare the morphological complexity in a corpus representing the language production of younger and older children across different languages. The language samples were taken from the Frog Story subcorpus of the CHILDES corpora, which comprises oral narratives collected by various researchers between 1990 and 2005. We extracted narratives by typically developing, monolingual, middle-class children. Additionally, samples of Lithuanian language, collected according to the same principles, were added. The corpus comprises 249 narratives evenly distributed across eight languages: Croatian, English, French, German, Italian, Lithuanian, Russian and Spanish. Two subcorpora were formed for each language: a younger children corpus and an older children corpus. Four measures of morphological complexity were calculated for each subcorpus: Bane, Kolmogorov, Word entropy and Relative entropy of word structure. The results showed that younger children corpora had lower morphological complexity than older children corpora for all four measures for Spanish and Russian. Reversed results were obtained for English and French, and the results for the remaining four languages showed variation. Relative entropy of word structure proved to be indicative of age differences. Word entropy and relative entropy of word structure show potential to demonstrate typological differences.

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EXPRES Corpus for A Field-specific Automated Exploratory Study of L2 English Expert Scientific Writing
Ana-Maria Bucur | Madalina Chitez | Valentina Muresan | Andreea Dinca | Roxana Rogobete

Field Specific Expert Scientific Writing in English as a Lingua Franca is essential for the effective research networking and dissemination worldwide. Extracting the linguistic profile of the research articles written in L2 English can help young researchers and expert scholars in various disciplines adapt to the scientific writing norms of their communities of practice. In this exploratory study, we present and test an automated linguistic assessment model that includes features relevant for the cross-disciplinary second language framework: Text Complexity Analysis features, such as Syntactic and Lexical Complexity, and Field Specific Academic Word Lists. We analyse how these features vary across four disciplinary fields (Economics, IT, Linguistics and Political Science) in a corpus of L2-English Expert Scientific Writing, part of the EXPRES corpus (Corpus of Expert Writing in Romanian and English). The variation in field specific writing is also analysed in groups of linguistic features extracted from the higher visibility (Hv) versus lower visibility (Lv) journals. After applying lexical sophistication, lexical variation and syntactic complexity formulae, significant differences between disciplines were identified, mainly that research articles from Lv journals have higher lexical complexity, but lower syntactic complexity than articles from Hv journals; while academic vocabulary proved to have discipline specific variation.

A law practitioner has to go through numerous lengthy legal case proceedings for their practices of various categories, such as land dispute, corruption, etc. Hence, it is important to summarize these documents, and ensure that summaries contain phrases with intent matching the category of the case. To the best of our knowledge, there is no evaluation metric that evaluates a summary based on its intent. We propose an automated intent-based summarization metric, which shows a better agreement with human evaluation as compared to other automated metrics like BLEU, ROUGE-L etc. in terms of human satisfaction. We also curate a dataset by annotating intent phrases in legal documents, and show a proof of concept as to how this system can be automated.

Detecting divergences in the applications of the law (where the same legal text is applied differently by two rulings) is an important task. It is the mission of the French Cour de Cassation. The first step in the detection of divergences is to detect similar cases, which is currently done manually by experts. They rely on summarised versions of the rulings (syntheses and keyword sequences), which are currently produced manually and are not available for all rulings. There is also a high degree of variability in the keyword choices and the level of granularity used. In this article, we therefore aim to provide automatic tools to facilitate the search for similar rulings. We do this by (i) providing automatic keyword sequence generation models, which can be used to improve the coverage of the analysis, and (ii) providing measures of similarity based on the available texts and augmented with predicted keyword sequences. Our experiments show that the predictions improve correlations of automatically obtained similarities against our specially colelcted human judgments of similarity.

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Cyrillic-MNIST: a Cyrillic Version of the MNIST Dataset
Bolat Tleubayev | Zhanel Zhexenova | Kenessary Koishybay | Anara Sandygulova

This paper presents a new handwritten dataset, Cyrillic-MNIST, a Cyrillic version of the MNIST dataset, comprising of 121,234 samples of 42 Cyrillic letters. The performance of Cyrillic-MNIST is evaluated using standard deep learning approaches and is compared to the Extended MNIST (EMNIST) dataset. The dataset is available at https://github.com/bolattleubayev/cmnist

The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community.

We present a lemmatizer/PoS tagger/dependency parser for West Frisian using a corpus of 44,714 words in 3,126 sentences that were annotated according to the guidelines of Universal Dependencies version 2. PoS tags were assigned to words by using a Dutch PoS tagger that was applied to a Dutch word-by-word translation, or to sentences of a Dutch parallel text. Best results were obtained when using word-by-word translations that were created by using the previous version of the Frisian translation program Oersetter. Morphologic and syntactic annotations were generated on the basis of a Dutch word-by-word translation as well. The performance of the lemmatizer/tagger/annotator when it was trained using default parameters was compared to the performance that was obtained when using the parameter values that were used for training the LassySmall UD 2.5 corpus. We study the effects of different hyperparameter settings on the accuracy of the annotation pipeline. The Frisian lemmatizer/PoS tagger/dependency parser is released as a web app and as a web service.

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A Dataset of Offensive German Language Tweets Annotated for Speech Acts
Melina Plakidis | Georg Rehm

We present a dataset consisting of German offensive and non-offensive tweets, annotated for speech acts. These 600 tweets are a subset of the dataset by Struß et al. (2019) and comprises three levels of annotation, i.e., six coarse-grained speech acts, 23 fine-grained speech acts and 14 different sentence types. Furthermore, we provide an evaluation in both qualitative and quantitative terms. The dataset is made publicly available under a CC-BY-4.0 license.

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Tracing Syntactic Change in the Scientific Genre: Two Universal Dependency-parsed Diachronic Corpora of Scientific English and German
Marie-Pauline Krielke | Luigi Talamo | Mahmoud Fawzi | Jörg Knappen

We present two comparable diachronic corpora of scientific English and German from the Late Modern Period (17th c.–19th c.) annotated with Universal Dependencies. We describe several steps of data pre-processing and evaluate the resulting parsing accuracy showing how our pre-processing steps significantly improve output quality. As a sanity check for the representativity of our data, we conduct a case study comparing previously gained insights on grammatical change in the scientific genre with our data. Our results reflect the often reported trend of English scientific discourse towards heavy noun phrases and a simplification of the sentence structure (Halliday, 1988; Halliday and Martin, 1993; Biber and Gray, 2011; Biber and Gray, 2016). We also show that this trend applies to German scientific discourse as well. The presented corpora are valuable resources suitable for the contrastive analysis of syntactic diachronic change in the scientific genre between 1650 and 1900. The presented pre-processing procedures and their evaluations are applicable to other languages and can be useful for a variety of Natural Language Processing tasks such as syntactic parsing.

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The Tembusu Treebank: An English Learner Treebank
Luís Morgado da Costa | Francis Bond | Roger V. P. Winder

This paper reports on the creation and development of the Tembusu Learner Treebank — an open treebank created from the NTU Corpus of Learner English, unique for incorporating mal-rules in the annotation of ungrammatical sentences. It describes the motivation and development of the treebank, as well as its exploitation to build a new parse-ranking model for the English Resource Grammar, designed to help improve the parse selection of ungrammatical sentences and diagnose these sentences through mal-rules. The corpus contains 25,000 sentences, of which 4,900 are treebanked. The paper concludes with an evaluation experiment that shows the usefulness of this new treebank in the tasks of grammatical error detection and diagnosis.

This paper presents the NDC Treebank of spoken Norwegian dialects in the Bokmål variety of Norwegian. It consists of dialect recordings made between 2006 and 2012 which have been digitised, segmented, transcribed and subsequently annotated with morphological and syntactic analysis. The nature of the spoken data gives rise to various challenges both in segmentation and annotation. We follow earlier efforts for Norwegian, in particular the LIA Treebank of spoken dialects transcribed in the Nynorsk variety of Norwegian, in the annotation principles to ensure interusability of the resources. We have developed a spoken language parser on the basis of the annotated material and report on its accuracy both on a test set across the dialects and by holding out single dialects.

This paper describes the first release of RRGparbank, a multilingual parallel treebank for Role and Reference Grammar (RRG) containing annotations of George Orwell’s novel 1984 and its translations. The release comprises the entire novel for English and a constructionally diverse and highly parallel sample (“seed”) for German, French and Russian. The paper gives an overview of annotation decisions that have been taken and describes the adopted treebanking methodology. Finally, as a possible application, a multilingual parser is trained on the treebank data. RRGparbank is one of the first resources to apply RRG to large amounts of real-world data. Furthermore, it enables comparative and typological corpus studies in RRG. And, finally, it creates new possibilities of data-driven NLP applications based on RRG.

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Unifying Morphology Resources with OntoLex-Morph. A Case Study in German
Christian Chiarcos | Christian Fäth | Maxim Ionov

The OntoLex vocabulary has become a widely used community standard for machine-readable lexical resources on the web. The primary motivation to use OntoLex in favor of tool- or application-specific formalisms is to facilitate interoperability and information integration across different resources. One of its extension that is currently being developed is a module for representing morphology, OntoLex-Morph. In this paper, we show how OntoLex-Morph can be used for the encoding and integration of different types of morphological resources on a unified basis. With German as the example, we demonstrate it for (a) a full-form dictionary with inflection information (Unimorph), (b) a dictionary of base forms and their derivations (UDer), (c) a dictionary of compounds (from GermaNet), and (d) lexicon and inflection rules of a finite-state parser/generator (SMOR/Morphisto). These data are converted to OntoLex-Morph, their linguistic information is consolidated and corresponding lexical entries are linked with each other.

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Building Dataset for Grounding of Formulae — Annotating Coreference Relations Among Math Identifiers
Takuto Asakura | Yusuke Miyao | Akiko Aizawa

Grounding the meaning of each symbol in math formulae is important for automated understanding of scientific documents. Generally speaking, the meanings of math symbols are not necessarily constant, and the same symbol is used in multiple meanings. Therefore, coreference relations between symbols need to be identified for grounding, and the task has aspects of both description alignment and coreference analysis. In this study, we annotated 15 papers selected from arXiv.org with the grounding information. In total, 12,352 occurrences of math identifiers in these papers were annotated, and all coreference relations between them were made explicit in each paper. The constructed dataset shows that regardless of the ambiguity of symbols in math formulae, coreference relations can be labeled with a high inter-annotator agreement. The constructed dataset enables us to achieve automation of formula grounding, and in turn, make deeper use of the knowledge in scientific documents using techniques such as math information extraction. The built grounding dataset is available at https://sigmathling.kwarc.info/resources/grounding- dataset/.

Recent advances in standardization for annotated language resources have led to successful large scale efforts, such as the Universal Dependencies (UD) project for multilingual syntactically annotated data. By comparison, the important task of coreference resolution, which clusters multiple mentions of entities in a text, has yet to be standardized in terms of data formats or annotation guidelines. In this paper we present CorefUD, a multilingual collection of corpora and a standardized format for coreference resolution, compatible with morphosyntactic annotations in the UD framework and including facilities for related tasks such as named entity recognition, which forms a first step in the direction of convergence for coreference resolution across languages.

The aim of the Universal Anaphora initiative is to push forward the state of the art in anaphora and anaphora resolution by expanding the aspects of anaphoric interpretation which are or can be reliably annotated in anaphoric corpora, producing unified standards to annotate and encode these annotations, deliver datasets encoded according to these standards, and developing methods for evaluating models carrying out this type of interpretation. Such expansion of the scope of anaphora resolution requires a comparable expansion of the scope of the scorers used to evaluate this work. In this paper, we introduce an extended version of the Reference Coreference Scorer (Pradhan et al., 2014) that can be used to evaluate the extended range of anaphoric interpretation included in the current Universal Anaphora proposal. The UA scorer supports the evaluation of identity anaphora resolution and of bridging reference resolution, for which scorers already existed but not integrated in a single package. It also supports the evaluation of split antecedent anaphora and discourse deixis, for which no tools existed. The proposed approach to the evaluation of split antecedent anaphora is entirely novel; the proposed approach to the evaluation of discourse deixis leverages the encoding of discourse deixis proposed in Universal Anaphora to enable the use for discourse deixis of the same metrics already used for identity anaphora. The scorer was tested in the recent CODI-CRAC 2021 Shared Task on Anaphora Resolution in Dialogues.

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Towards Evaluation of Cross-document Coreference Resolution Models Using Datasets with Diverse Annotation Schemes
Anastasia Zhukova | Felix Hamborg | Bela Gipp

Established cross-document coreference resolution (CDCR) datasets contain event-centric coreference chains of events and entities with identity relations. These datasets establish strict definitions of the coreference relations across related tests but typically ignore anaphora with more vague context-dependent loose coreference relations. In this paper, we qualitatively and quantitatively compare the annotation schemes of ECB+, a CDCR dataset with identity coreference relations, and NewsWCL50, a CDCR dataset with a mix of loose context-dependent and strict coreference relations. We propose a phrasing diversity metric (PD) that encounters for the diversity of full phrases unlike the previously proposed metrics and allows to evaluate lexical diversity of the CDCR datasets in a higher precision. The analysis shows that coreference chains of NewsWCL50 are more lexically diverse than those of ECB+ but annotating of NewsWCL50 leads to the lower inter-coder reliability. We discuss the different tasks that both CDCR datasets create for the CDCR models, i.e., lexical disambiguation and lexical diversity. Finally, to ensure generalizability of the CDCR models, we propose a direction for CDCR evaluation that combines CDCR datasets with multiple annotation schemes that focus of various properties of the coreference chains.

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Explainable Tsetlin Machine Framework for Fake News Detection with Credibility Score Assessment
Bimal Bhattarai | Ole-Christoffer Granmo | Lei Jiao

The proliferation of fake news, i.e., news intentionally spread for misinformation, poses a threat to individuals and society. Despite various fact-checking websites such as PolitiFact, robust detection techniques are required to deal with the increase in fake news. Several deep learning models show promising results for fake news classification, however, their black-box nature makes it difficult to explain their classification decisions and quality-assure the models. We here address this problem by proposing a novel interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM). In brief, we utilize the conjunctive clauses of the TM to capture lexical and semantic properties of both true and fake news text. Further, we use clause ensembles to calculate the credibility of fake news. For evaluation, we conduct experiments on two publicly available datasets, PolitiFact and GossipCop, and demonstrate that the TM framework significantly outperforms previously published baselines by at least 5% in terms of accuracy, with the added benefit of an interpretable logic-based representation. In addition, our approach provides a higher F1-score than BERT and XLNet, however, we obtain slightly lower accuracy. We finally present a case study on our model’s explainability, demonstrating how it decomposes into meaningful words and their negations.

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Enhancing Deep Learning with Embedded Features for Arabic Named Entity Recognition
Ali L. Hatab | Caroline Sabty | Slim Abdennadher

The introduction of word embedding models has remarkably changed many Natural Language Processing tasks. Word embeddings can automatically capture the semantics of words and other hidden features. Nonetheless, the Arabic language is highly complex, which results in the loss of important information. This paper uses Madamira, an external knowledge source, to generate additional word features. We evaluate the utility of adding these features to conventional word and character embeddings to perform the Named Entity Recognition (NER) task on Modern Standard Arabic (MSA). Our NER model is implemented using Bidirectional Long Short Term Memory and Conditional Random Fields (BiLSTM-CRF). We add morphological and syntactical features to different word embeddings to train the model. The added features improve the performance by different values depending on the used embedding model. The best performance is achieved by using Bert embeddings. Moreover, our best model outperforms the previous systems to the best of our knowledge.

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SCAI-QReCC Shared Task on Conversational Question Answering
Svitlana Vakulenko | Johannes Kiesel | Maik Fröbe

Search-Oriented Conversational AI (SCAI) is an established venue that regularly puts a spotlight upon the recent work advancing the field of conversational search. SCAI’21 was organised as an independent online event and featured a shared task on conversational question answering, on which this paper reports. The shared task featured three subtasks that correspond to three steps in conversational question answering: question rewriting, passage retrieval, and answer generation. This report discusses each subtask, but emphasizes the answer generation subtask as it attracted the most attention from the participants and we identified evaluation of answer correctness in the conversational settings as a major challenge and acurrent research gap. Alongside the automatic evaluation, we conducted two crowdsourcing experiments to collect annotations for answer plausibility and faithfulness. As a result of this shared task, the original conversational QA dataset used for evaluation was further extended with alternative correct answers produced by the participant systems.

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Semantic Relations between Text Segments for Semantic Storytelling: Annotation Tool - Dataset - Evaluation
Michael Raring | Malte Ostendorff | Georg Rehm

Semantic Storytelling describes the goal to automatically and semi-automatically generate stories based on extracted, processed, classified and annotated information from large content resources. Essential is the automated processing of text segments extracted from different content resources by identifying the relevance of a text segment to a topic and its semantic relation to other text segments. In this paper we present an approach to create an automatic classifier for semantic relations between extracted text segments from different news articles. We devise custom annotation guidelines based on various discourse structure theories and annotate a dataset of 2,501 sentence pairs extracted from 2,638 Wikinews articles. For the annotation, we developed a dedicated annotation tool. Based on the constructed dataset, we perform initial experiments with Transformer language models that are trained for the automatic classification of semantic relations. Our results with promising high accuracy scores suggest the validity and applicability of our approach for future Semantic Storytelling solutions.

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Evaluating Pre-training Objectives for Low-Resource Translation into Morphologically Rich Languages
Prajit Dhar | Arianna Bisazza | Gertjan van Noord

The scarcity of parallel data is a major limitation for Neural Machine Translation (NMT) systems, in particular for translation into morphologically rich languages (MRLs). An important way to overcome the lack of parallel data is to leverage target monolingual data, which is typically more abundant and easier to collect. We evaluate a number of techniques to achieve this, ranging from back-translation to random token masking, on the challenging task of translating English into four typologically diverse MRLs, under low-resource settings. Additionally, we introduce Inflection Pre-Training (or PT-Inflect), a novel pre-training objective whereby the NMT system is pre-trained on the task of re-inflecting lemmatized target sentences before being trained on standard source-to-target language translation. We conduct our evaluation on four typologically diverse target MRLs, and find that PT-Inflect surpasses NMT systems trained only on parallel data. While PT-Inflect is outperformed by back-translation overall, combining the two techniques leads to gains in some of the evaluated language pairs.

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Aligning Images and Text with Semantic Role Labels for Fine-Grained Cross-Modal Understanding
Abhidip Bhattacharyya | Cecilia Mauceri | Martha Palmer | Christoffer Heckman

As vision processing and natural language processing continue to advance, there is increasing interest in multimodal applications, such as image retrieval, caption generation, and human-robot interaction. These tasks require close alignment between the information in the images and text. In this paper, we present a new multimodal dataset that combines state of the art semantic annotation for language with the bounding boxes of corresponding images. This richer multimodal labeling supports cross-modal inference for applications in which such alignment is useful. Our semantic representations, developed in the natural language processing community, abstract away from the surface structure of the sentence, focusing on specific actions and the roles of their participants, a level that is equally relevant to images. We then utilize these representations in the form of semantic role labels in the captions and the images and demonstrate improvements in standard tasks such as image retrieval. The potential contributions of these additional labels is evaluated using a role-aware retrieval system based on graph convolutional and recurrent neural networks. The addition of semantic roles into this system provides a significant increase in capability and greater flexibility for these tasks, and could be extended to state-of-the-art techniques relying on transformers with larger amounts of annotated data.

This article presents a new French Sign Language (LSF) corpus called “Rosetta-LSF”. It was created to support future studies on the automatic translation of written French into LSF, rendered through the animation of a virtual signer. An overview of the field highlights the importance of a quality representation of LSF. In order to obtain quality animations understandable by signers, it must surpass the simple “gloss transcription” of the LSF lexical units to use in the discourse. To achieve this, we designed a corpus composed of four types of aligned data, and evaluated its usability. These are: news headlines in French, translations of these headlines into LSF in the form of videos showing animations of a virtual signer, gloss annotations of the “traditional” type—although including additional information on the context in which each gestural unit is performed as well as their potential for adaptation to another context—and AZee representations of the videos, i.e. formal expressions capturing the necessary and sufficient linguistic information. This article describes this data, exhibiting an example from the corpus. It is available online for public research.

We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE). The dataset contains annotations for eleven language pairs, including both high- and low-resource languages. Specifically, it is annotated for translation quality with human labels for up to 10,000 translations per language pair in the following formats: sentence-level direct assessments and post-editing effort, and word-level binary good/bad labels. Apart from the quality-related scores, each source-translation sentence pair is accompanied by the corresponding post-edited sentence, as well as titles of the articles where the sentences were extracted from, and information on the neural MT models used to translate the text. We provide a thorough description of the data collection and annotation process as well as an analysis of the annotation distribution for each language pair. We also report the performance of baseline systems trained on the MLQE-PE dataset. The dataset is freely available and has already been used for several WMT shared tasks.

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OpenKorPOS: Democratizing Korean Tokenization with Voting-Based Open Corpus Annotation
Sangwhan Moon | Won Ik Cho | Hye Joo Han | Naoaki Okazaki | Nam Soo Kim

Korean is a language with complex morphology that uses spaces at larger-than-word boundaries, unlike other East-Asian languages. While morpheme-based text generation can provide significant semantic advantages compared to commonly used character-level approaches, Korean morphological analyzers only provide a sequence of morpheme-level tokens, losing information in the tokenization process. Two crucial issues are the loss of spacing information and subcharacter level morpheme normalization, both of which make the tokenization result challenging to reconstruct the original input string, deterring the application to generative tasks. As this problem originates from the conventional scheme used when creating a POS tagging corpus, we propose an improvement to the existing scheme, which makes it friendlier to generative tasks. On top of that, we suggest a fully-automatic annotation of a corpus by leveraging public analyzers. We vote the surface and POS from the outcome and fill the sequence with the selected morphemes, yielding tokenization with a decent quality that incorporates space information. Our scheme is verified via an evaluation done on an external corpus, and subsequently, it is adapted to Korean Wikipedia to construct an open, permissive resource. We compare morphological analyzer performance trained on our corpus with existing methods, then perform an extrinsic evaluation on a downstream task.

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Enriching Grammatical Error Correction Resources for Modern Greek
Katerina Korre | John Pavlopoulos

Grammatical Error Correction (GEC), a task of Natural Language Processing (NLP), is challenging for underepresented languages. This issue is most prominent in languages other than English. This paper addresses the issue of data and system sparsity for GEC purposes in the modern Greek Language. Following the most popular current approaches in GEC, we develop and test an MT5 multilingual text-to-text transformer for Greek. To our knowledge this the first attempt to create a fully-fledged GEC model for Greek. Our evaluation shows that our system reaches up to 52.63% F0.5 score on part of the Greek Native Corpus (GNC), which is 16% below the winning system of the BEA-19 shared task on English GEC. In addition, we provide an extended version of the Greek Learner Corpus (GLC), on which our model reaches up to 22.76% F0.5. Previous versions did not include corrections with the annotations which hindered the potential development of efficient GEC systems. For that reason we provide a new set of corrections. This new dataset facilitates an exploration of the generalisation abilities and robustness of our system, given that the assessment is conducted on learner data while the training on native data.

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A Hmong Corpus with Elaborate Expression Annotations
David R. Mortensen | Xinyu Zhang | Chenxuan Cui | Katherine J. Zhang

This paper describes the first publicly available corpus of Hmong, a minority language of China, Vietnam, Laos, Thailand, and various countries in Europe and the Americas. The corpus has been scraped from a long-running Usenet newsgroup called soc.culture.hmong and consists of approximately 12 million tokens. This corpus (called SCH) is also the first substantial corpus to be annotated for elaborate expressions, a kind of four-part coordinate construction that is common and important in the languages of mainland Southeast Asia. We show that word embeddings trained on SCH can benefit tasks in Hmong (solving analogies) and that a model trained on it can label previously unseen elaborate expressions, in context, with an F1 of 90.79 (precision: 87.36, recall: 94.52). [ISO 639-3: mww, hmj]

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ELAL: An Emotion Lexicon for the Analysis of Alsatian Theatre Plays
Delphine Bernhard | Pablo Ruiz Fabo

In this work, we present a novel and manually corrected emotion lexicon for the Alsatian dialects, including graphical variants of Alsatian lexical items. These High German dialects are spoken in the North-East of France. They are used mainly orally, and thus lack a stable and consensual spelling convention. There has nevertheless been a continuous literary production since the middle of the 17th century and, in particular, theatre plays. A large sample of Alsatian theatre plays is currently being encoded according to the Text Encoding Initiative (TEI) Guidelines. The emotion lexicon will be used to perform automatic emotion analysis in this corpus of theatre plays. We used a graph-based approach to deriving emotion scores and translations, relying only on bilingual lexicons, cognates and spelling variants. The source lexicons for emotion scores are the NRC Valence Arousal and Dominance and NRC Emotion Intensity lexicons.

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Universal Dependencies for Western Sierra Puebla Nahuatl
Robert Pugh | Marivel Huerta Mendez | Mitsuya Sasaki | Francis Tyers

We present a morpho-syntactically-annotated corpus of Western Sierra Puebla Nahuatl that conforms to the annotation guidelines of the Universal Dependencies project. We describe the sources of the texts that make up the corpus, the annotation process, and important annotation decisions made throughout the development of the corpus. As the first indigenous language of Mexico to be added to the Universal Dependencies project, this corpus offers a good opportunity to test and more clearly define annotation guidelines for the Meso-american linguistic area, spontaneous and elicited spoken data, and code-switching.

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The Construction and Evaluation of the LEAFTOP Dataset of Automatically Extracted Nouns in 1480 Languages
Gregory Baker | Diego Molla

The LEAFTOP (language extracted automatically from thousands of passages) dataset consists of nouns that appear in multiple places in the four gospels of the New Testament. We use a naive approach — probabilistic inference — to identify likely translations in 1480 other languages. We evaluate this process and find that it provides lexiconaries with accuracy from 42% (Korafe) to 99% (Runyankole), averaging 72% correct across evaluated languages. The process translates up to 161 distinct lemmas from Koine Greek (average 159). We identify nouns which appear to be easy and hard to translate, language families where this technique works, and future possible improvements and extensions. The claims to novelty are: the use of a Koine Greek New Testament as the source language; using a fully-annotated manually-created grammatically parse of the source text; a custom scraper for texts in the target languages; a new metric for language similarity; a novel strategy for evaluation on low-resource languages.

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Huqariq: A Multilingual Speech Corpus of Native Languages of Peru forSpeech Recognition
Rodolfo Zevallos | Luis Camacho | Nelsi Melgarejo

The Huqariq corpus is a multilingual collection of speech from native Peruvian languages. The transcribed corpus is intended for the research and development of speech technologies to preserve endangered languages in Peru. Huqariq is primarily designed for the development of automatic speech recognition, language identification and text-to-speech tools. In order to achieve corpus collection sustainably, we employs the crowdsourcing methodology. Huqariq includes four native languages of Peru, and it is expected that by the year 2022, it can reach up to 20 native languages out of the 48 native languages in Peru. The corpus has 220 hours of transcribed audio recorded by more than 500 volunteers, making it the largest speech corpus for native languages in Peru. In order to verify the quality of the corpus, we present speech recognition experiments using 220 hours of fully transcribed audio.

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Writing System and Speaker Metadata for 2,800+ Language Varieties
Daan van Esch | Tamar Lucassen | Sebastian Ruder | Isaac Caswell | Clara Rivera

We describe an open-source dataset providing metadata for about 2,800 language varieties used in the world today. Specifically, the dataset provides the attested writing system(s) for each of these 2,800+ varieties, as well as an estimated speaker count for each variety. This dataset was developed through internal research and has been used for analyses around language technologies. This is the largest publicly-available, machine-readable resource with writing system and speaker information for the world’s languages. We analyze the distribution of languages and writing systems in our data and compare it to their representation in current NLP. We hope the availability of this data will catalyze research in under-represented languages.

We present three new corpora of urban varieties of Portuguese spoken in Angola, Mozambique, and São Tomé and Príncipe, where Portuguese is increasingly being spoken as first and second language in different multilingual settings. Given the scarcity of linguistic resources available for the African varieties of Portuguese, these corpora provide new, contemporary data for the study of each variety and for comparative research on African, Brazilian and European varieties, hereby improving our understanding of processes of language variation and change in postcolonial societies. The corpora consist of transcribed spoken data, complemented by a rich set of metadata describing the setting of the audio recordings and sociolinguistic information about the speakers. They are annotated with POS and lemma information and made available on the CQPweb platform, which allows for sophisticated data searches. The corpora are already being used for comparative research on constructions in the domain of possession and location involving the argument structure of intransitive, monotransitive and ditransitive verbs that select Goals, Locatives, and Recipients.

This study aims to create the very first dependency-to-constituency conversion algorithm optimised for Turkish language. For this purpose, a state-of-the-art morphologic analyser and a feature-based machine learning model was used. In order to enhance the performance of the conversion algorithm, bootstrap aggregating meta-algorithm was integrated. While creating the conversation algorithm, typological properties of Turkish were carefully considered. A comprehensive and manually annotated UD-style dependency treebank was the input, and constituency trees were the output of the conversion algorithm. A team of linguists manually annotated a set of constituency trees. These manually annotated trees were used as the gold standard to assess the performance of the algorithm. The conversion process yielded more than 8000 constituency trees whose UD-style dependency trees are also available on GitHub. In addition to its contribution to Turkish treebank resources, this study also offers a viable and easy-to-implement conversion algorithm that can be used to generate new constituency treebanks and training data for NLP resources like constituency parsers.

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Standard German Subtitling of Swiss German TV content: the PASSAGE Project
Jonathan David Mutal | Pierrette Bouillon | Johanna Gerlach | Veronika Haberkorn

In Switzerland, two thirds of the population speak Swiss German, a primarily spoken language with no standardised written form. It is widely used on Swiss TV, for example in news reports, interviews or talk shows, and subtitles are required for people who cannot understand this spoken language. This paper focuses on the task of automatic Standard German subtitling of spoken Swiss German, and more specifically on the translation of a normalised Swiss German speech recognition result into Standard German suitable for subtitles. Our contribution consists of a comparison of different statistical and deep learning MT systems for this task and an aligned corpus of normalised Swiss German and Standard German subtitles. Results of two evaluations, automatic and human, show that the systems succeed in improving the content, but are currently not capable of producing entirely correct Standard German.

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A Survey of Multilingual Models for Automatic Speech Recognition
Hemant Yadav | Sunayana Sitaram

Although Automatic Speech Recognition (ASR) systems have achieved human-like performance for a few languages, the majority of the world’s languages do not have usable systems due to the lack of large speech datasets to train these models. Cross-lingual transfer is an attractive solution to this problem, because low-resource languages can potentially benefit from higher-resource languages either through transfer learning, or being jointly trained in the same multilingual model. The problem of cross-lingual transfer has been well studied in ASR, however, recent advances in Self Supervised Learning are opening up avenues for unlabeled speech data to be used in multilingual ASR models, which can pave the way for improved performance on low-resource languages. In this paper, we survey the state of the art in multilingual ASR models that are built with cross-lingual transfer in mind. We present best practices for building multilingual models from research across diverse languages and techniques, discuss open questions and provide recommendations for future work.

Pre-trained Language Models such as BERT have become ubiquitous in NLP where they have achieved state-of-the-art performance in most NLP tasks. While these models are readily available for English and other widely spoken languages, they remain scarce for low-resource languages such as Luxembourgish. In this paper, we present LuxemBERT, a BERT model for the Luxembourgish language that we create using the following approach: we augment the pre-training dataset by considering text data from a closely related language that we partially translate using a simple and straightforward method. We are then able to produce the LuxemBERT model, which we show to be effective for various NLP tasks: it outperforms a simple baseline built with the available Luxembourgish text data as well the multilingual mBERT model, which is currently the only option for transformer-based language models in Luxembourgish. Furthermore, we present datasets for various downstream NLP tasks that we created for this study and will make available to researchers on request.

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PerPaDa: A Persian Paraphrase Dataset based on Implicit Crowdsourcing Data Collection
Salar Mohtaj | Fatemeh Tavakkoli | Habibollah Asghari

In this paper we introduce PerPaDa, a Persian paraphrase dataset that is collected from users’ input in a plagiarism detection system. As an implicit crowdsourcing experience, we have gathered a large collection of original and paraphrased sentences from Hamtajoo; a Persian plagiarism detection system, in which users try to conceal cases of text re-use in their documents by paraphrasing and re-submitting manuscripts for analysis. The compiled dataset contains 2446 instances of paraphrasing. In order to improve the overall quality of the collected data, some heuristics have been used to exclude sentences that don’t meet the proposed criteria. The introduced corpus is much larger than the available datasets for the task of paraphrase identification in Persian. Moreover, there is less bias in the data compared to the similar datasets, since the users did not try some fixed predefined rules in order to generate similar texts to their original inputs.

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Introducing the Welsh Text Summarisation Dataset and Baseline Systems
Ignatius Ezeani | Mahmoud El-Haj | Jonathan Morris | Dawn Knight

Welsh is an official language in Wales and is spoken by an estimated 884,300 people (29.2% of the population of Wales). Despite this status and estimated increase in speaker numbers since the last (2011) census, Welsh remains a minority language undergoing revitalisation and promotion by Welsh Government and relevant stakeholders. As part of the effort to increase the availability of Welsh digital technology, this paper introduces the first Welsh summarisation dataset, which we provide freely for research purposes to help advance the work on Welsh summarisation. The dataset was created by Welsh speakers through manually summarising Welsh Wikipedia articles. In addition, the paper discusses the implementation and evaluation of different summarisation systems for Welsh. The summarisation systems and results will serve as benchmarks for the development of summarisers in other minority language contexts.

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A Systematic Approach to Derive a Refined Speech Corpus for Sinhala
Disura Warusawithana | Nilmani Kulaweera | Lakshan Weerasinghe | Buddhika Karunarathne

Speech Recognition is an active research area where advances of technology have continuously driven the development of research work. However, due to the lack of adequate resources, certain languages such as Sinhala, are left to underutilize the technology. With techniques such as crowdsourcing and web scraping, several Sinhala corpora have been created and made publicly available. Despite them being large and generic, the correctness and consistency in their text data remain questionable, especially due to the lack of uniformity in the language used in the different sources of web scraped text. Addressing that requires a thorough understanding of technical and linguistic particulars pertaining to the language, which often leaves the issue unattended. We have followed a systematic approach to derive a refined corpus using a publicly available corpus for Sinhala speech recognition. In particular, we standardized the transcriptions of the corpus by removing noise in the text. Further, we applied corrections based on Sinhala linguistics. A comparative experiment shows a promising effect of the linguistic corrections by having a relative reduction of the Word-Error-Rate by 15.9%.

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IgboBERT Models: Building and Training Transformer Models for the Igbo Language
Chiamaka Chukwuneke | Ignatius Ezeani | Paul Rayson | Mahmoud El-Haj

This work presents a standard Igbo named entity recognition (IgboNER) dataset as well as the results from training and fine-tuning state-of-the-art transformer IgboNER models. We discuss the process of our dataset creation - data collection and annotation and quality checking. We also present experimental processes involved in building an IgboBERT language model from scratch as well as fine-tuning it along with other non-Igbo pre-trained models for the downstream IgboNER task. Our results show that, although the IgboNER task benefited hugely from fine-tuning large transformer model, fine-tuning a transformer model built from scratch with comparatively little Igbo text data seems to yield quite decent results for the IgboNER task. This work will contribute immensely to IgboNLP in particular as well as the wider African and low-resource NLP efforts Keywords: Igbo, named entity recognition, BERT models, under-resourced, dataset

LNCC is a diverse collection of Latvian language corpora representing both written and spoken language and is useful for both linguistic research and language modelling. The collection is intended to cover diverse Latvian language use cases and all the important text types and genres (e.g. news, social media, blogs, books, scientific texts, debates, essays, etc.), taking into account both quality and size aspects. To reach this objective, LNCC is a continuous multi-institutional and multi-project effort, supported by the Digital Humanities and Language Technology communities in Latvia. LNCC includes a broad range of Latvian texts from the Latvian National Library, Culture Information Systems Centre, Latvian National News Agency, Latvian Parliament, Latvian web crawl, various Latvian publishers, and from the Latvian language corpora created by Institute of Mathematics and Computer Science and its partners, including spoken language corpora. All corpora of LNCC are re-annotated with a uniform morpho-syntactic annotation scheme which enables federated search and consistent linguistics analysis in all the LNCC corpora, as well as facilitates to select and mix various corpora for pre-training large Latvian language models like BERT and GPT.

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Investigating the Relationship Between Romanian Financial News and Closing Prices from the Bucharest Stock Exchange
Ioan-Bogdan Iordache | Ana Sabina Uban | Catalin Stoean | Liviu P. Dinu

A new data set is gathered from a Romanian financial news website for the duration of four years. It is further refined to extract only information related to one company by selecting only paragraphs and even sentences that referred to it. The relation between the extracted sentiment scores of the texts and the stock prices from the corresponding dates is investigated using various approaches like the lexicon-based Vader tool, Financial BERT, as well as Transformer-based models. Automated translation is used, since some models could be only applied for texts in English. It is encouraging that all models, be that they are applied to Romanian or English texts, indicate a correlation between the sentiment scores and the increase or decrease of the stock closing prices.

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A Free/Open-Source Morphological Analyser and Generator for Sakha
Sardana Ivanova | Jonathan Washington | Francis Tyers

We present, to our knowledge, the first ever published morphological analyser and generator for Sakha, a marginalised language of Siberia. The transducer, developed using HFST, has coverage of solidly above 90%, and high precision. In the development of the analyser, we have expanded linguistic knowledge about Sakha, and developed strategies for complex grammatical patterns. The transducer is already being used in downstream tasks, including computer assisted language learning applications for linguistic maintenance and computational linguistic shared tasks.

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An Expanded Finite-State Transducer for Tsuut’ina Verbs
Joshua Holden | Christopher Cox | Antti Arppe

This paper describes the expansion of a finite state transducer (FST) for the transitive verb system of Tsuut’ina (ISO 639-3: srs), a Dene (Athabaskan) language spoken in Alberta, Canada. Dene languages have unique templatic morphology, in which lexical, inflectional and derivational tiers are interlaced. Drawing on data from close to 9,000 verbal forms, the expanded model can handle a great range of common and rare argument structure types, including ditransitive and uniquely Dene object experiencer verbs. While challenges of speed remain, this expansion shows the ability of FST modelling to handle morphology of this type, and the expnded FST shows great promise for community language applications such as a morphologically informed online dictionary and word predictor, and for further FST development. This paper describes the expansion of a finite state transducer (FST) for the transitive verb system of Tsuut’ina (ISO 639-3: srs), a Dene (Athabaskan) language spoken in Alberta, Canada. Dene languages have unique templatic morphology, in which lexical, inflectional and derivational tiers are interlaced. Drawing on data from over 12,000 verbs forms, the expanded model can handle a great range of common and rare argument structure types, including ditransitive and uniquely Dene object experiencer verbs. While challenges of speed remain, this expansion shows the ability of FST modelling to handle morphology of this type, and the expnded FST shows great promise for community language applications such as a morphologically informed online dictionary and word predictor, and for further FST development.

Social media platforms and online streaming services have spawned a new breed of Hate Speech (HS). Due to the massive amount of user-generated content on these sites, modern machine learning techniques are found to be feasible and cost-effective to tackle this problem. However, linguistically diverse datasets covering different social contexts in which offensive language is typically used are required to train generalizable models. In this paper, we identify the shortcomings of existing Bangla HS datasets and introduce a large manually labeled dataset BD-SHS that includes HS in different social contexts. The labeling criteria were prepared following a hierarchical annotation process, which is the first of its kind in Bangla HS to the best of our knowledge. The dataset includes more than 50,200 offensive comments crawled from online social networking sites and is at least 60% larger than any existing Bangla HS datasets. We present the benchmark result of our dataset by training different NLP models resulting in the best one achieving an F1-score of 91.0%. In our experiments, we found that a word embedding trained exclusively using 1.47 million comments from social media and streaming sites consistently resulted in better modeling of HS detection in comparison to other pre-trained embeddings. Our dataset and all accompanying codes is publicly available at github.com/naurosromim/hate-speech-dataset-for-Bengali-social-media

Knowledge graphs applications, in industry and academia, motivate substantial research directions towards large-scale information extraction from various types of resources. Nowadays, most of the available knowledge graphs are either in English or multilingual. In this paper, we introduce RezoJDM16k, a French knowledge graph dataset based on RezoJDM. With 16k nodes, 832k triplets, and 53 relation types, RezoJDM16k can be employed in many NLP downstream tasks for the French language such as machine translation, question-answering, and recommendation systems. Moreover, we provide strong knowledge graph embedding baselines that are used in link prediction tasks for future benchmarking. Compared to the state-of-the-art English knowledge graph datasets used in link prediction, RezoJDM16k shows a similar promising predictive behavior.

We present in this paper the first natural conversation corpus recorded with all modalities and neuro-physiological signals. 5 dyads (10 participants) have been recorded three times, during three sessions (30mns each) with 4 days interval. During each session, audio and video are captured as well as the neural signal (EEG with Emotiv-EPOC) and the electro-physiological one (with Empatica-E4). This resource original in several respects. Technically, it is the first one gathering all these types of data in a natural conversation situation. Moreover, the recording of the same dyads at different periods opens the door to new longitudinal investigations such as the evolution of interlocutors’ alignment during the time. The paper situates this new type of resources with in the literature, presents the experimental setup and describes different annotations enriching the corpus.

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Reading Time and Vocabulary Rating in the Japanese Language: Large-Scale Japanese Reading Time Data Collection Using Crowdsourcing
Masayuki Asahara

This study examines how differences in human vocabulary affect reading time. Specifically, we assumed vocabulary to be the random effect of research participants when applying a generalized linear mixed model to the ratings of participants in the word familiarity survey. Thereafter, we asked the participants to take part in a self-paced reading task to collect their reading times. Through fixed effect of vocabulary when applying a generalized linear mixed model to reading time, we clarified the tendency that vocabulary differences give to reading time.

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Thematic Fit Bits: Annotation Quality and Quantity Interplay for Event Participant Representation
Yuval Marton | Asad Sayeed

Modeling thematic fit (a verb-argument compositional semantics task) currently requires a very large burden of labeled data. We take a linguistically machine-annotated large corpus and replace corpus layers with output from higher-quality, more modern taggers. We compare the old and new corpus versions’ impact on a verb-argument fit modeling task, using a high-performing neural approach. We discover that higher annotation quality dramatically reduces our data requirement while demonstrating better supervised predicate-argument classification. But in applying the model to psycholinguistic tasks outside the training objective, we see clear gains at scale, but only in one of two thematic fit estimation tasks, and no clear gains on the other. We also see that quality improves with training size, but perhaps plateauing or even declining in one task. Last, we tested the effect of role set size. All this suggests that the quality/quantity interplay is not all you need. We replicate previous studies while modifying certain role representation details and set a new state-of-the-art in event modeling, using a fraction of the data. We make the new corpus version public.

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ChiSense-12: An English Sense-Annotated Child-Directed Speech Corpus
Francesco Cabiddu | Lewis Bott | Gary Jones | Chiara Gambi

Language acquisition research has benefitted from the use of annotated corpora of child-directed speech to examine key questions about how children learn and process language in real-world contexts. However, a lack of sense-annotated corpora has limited investigations of child word sense disambiguation in naturalistic contexts. In this work, we sense-tagged 53 corpora of American and English speech directed to 958 target children up to 59 months of age, comprising a large-scale sample of 15,581 utterances for 12 ambiguous words. Importantly, we carefully selected target senses that we know - from previous investigations - young children understand. As such work was part of a project focused on investigating the role of verbs in child word sense disambiguation, we additionally coded for verb instances which took a target ambiguous word as verb object. We present experimental work where we leveraged our sense-tagged corpus ChiSense-12 to examine the role of verb-event structure in child word sense disambiguation, and we outline our plan to use Transformer-based computational architectures to test hypotheses on the role of different learning mechanisms underlying children word sense disambiguation performance.

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Making People Laugh like a Pro: Analysing Humor Through Stand-Up Comedy
Beatrice Turano | Carlo Strapparava

The analysis of humor using computational tools has gained popularity in the past few years, and a lot of resources have been built for this purpose. However, most of these resources focus on standalone jokes or on occasional humorous sentences during presentations. In this paper I present a new dataset, SCRIPTS, built using stand-up comedy shows transcripts: the humor that this dataset collects is inserted in a larger narrative, composed of daily events made humorous by the ability of the comedian. This different perspective on the humor problem can allow us to think and study humor in a different way and possibly to open the path to new lines of research.

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Testing Focus and Non-at-issue Frameworks with a Question-under-Discussion-Annotated Corpus
Christoph Hesse | Maurice Langner | Ralf Klabunde | Anton Benz

We present an annotated corpus of German driving reports for the analysis of Question-under-Discussion (QUD) based information structural distinctions. Since QUDs can hardly be defined in advance for providing a corresponding tagset, several theoretical issues arise concerning the scope and quality of the corpus and the development of an appropriate annotation tool for creating the corpus. We developed the corpus for testing the adequacy of QUD-based pragmatic frameworks of information structure. First analyses of the annotated information structures show that focus-related meaning aspects are essentially confirmed, indicating a sufficent accuracy of the annotations. Assumptions on non-at-issueness expressed by non-restrictive relative clauses made in the literature seem to be too strong, given the corpus data.

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Development of a Multilingual CCG Treebank via Universal Dependencies Conversion
Tu-Anh Tran | Yusuke Miyao

This paper introduces an algorithm to convert Universal Dependencies (UD) treebanks to Combinatory Categorial Grammar (CCG) treebanks. As CCG encodes almost all grammatical information into the lexicon, obtaining a high-quality CCG derivation from a dependency tree is a challenging task. Our algorithm relies on hand-crafted rules to assign categories to constituents, and a non-statistical parser to derive full CCG parses given the assigned categories. To evaluate our converted treebanks, we perform lexical, sentential, and syntactic rule coverage analysis, as well as CCG parsing experiments. Finally, we discuss how our method handles complex constructions, and propose possible future extensions.

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The Automatic Extraction of Linguistic Biomarkers as a Viable Solution for the Early Diagnosis of Mental Disorders
Gloria Gagliardi | Fabio Tamburini

Digital Linguistic Biomarkers extracted from spontaneous language productions proved to be very useful for the early detection of various mental disorders. This paper presents a computational pipeline for the automatic processing of oral and written texts: the tool enables the computation of a rich set of linguistic features at the acoustic, rhythmic, lexical, and morphosyntactic levels. Several applications of the instrument - for the detection of Mild Cognitive Impairments, Anorexia Nervosa, and Developmental Language Disorders - are also briefly discussed.

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Singlish Where Got Rules One? Constructing a Computational Grammar for Singlish
Siew Yeng Chow | Francis Bond

Singlish is a variety of English spoken in Singapore. In this paper, we share some of its grammar features and how they are implemented in the construction of a computational grammar of Singlish as a branch of English grammar. New rules were created and existing ones from standard English grammar of the English Resource Grammar (ERG) were changed in this branch to cater to how Singlish works. In addition, Singlish lexicon was added into the grammar together with some new lexical types. We used Head-driven Phrase Structure Grammar (HPSG) as the framework for this project of a creating a working computational grammar. As part of building the language resource, we also collected and formatted some data from the internet as part of a test suite for Singlish. Finally, the computational grammar was tested against a set of gold standard trees and compared with the standard English grammar to find out how well the grammar fares in analysing Singlish.

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COSMOS: Experimental and Comparative Studies of Concept Representations in Schoolchildren
Jeanne Villaneau | Farida Said

COSMOS is a multidisciplinary research project investigating schoolchildren’s beliefs and representations of specific concepts under control variables (age, gender, language spoken at home). Seven concepts are studied: friend, father, mother, villain, work, television and dog. We first present the protocol used and the data collected from a survey of 184 children in two age groups (6-7 and 9-11 years) in four schools in Brittany (France). A word-level lexical study shows that children’s linguistic proficiency and lexical diversity increase with age, and we observe an interaction effect between gender and age on lexical diversity as measured with MLR (Measure of Lexical Richness). In contrast, none of the control variables affects lexical density. We also present the lemmas that schoolchildren most often associate with each concept. Generalized linear mixed-effects models reveal significant effects of age, gender, and home language on some concept-lemma associations and specific interactions between age and gender. Most of the identified effects are documented in the child development literature. To better understand the process of semantic construction in children, additional lexical analyses at the n-gram, chunk, and clause levels would be helpful. We briefly present ongoing and planned work in this direction. The COSMOS data will soon be made freely available to the scientific community.

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Features of Perceived Metaphoricity on the Discourse Level: Abstractness and Emotionality
Prisca Piccirilli | Sabine Schulte im Walde

Research on metaphorical language has shown ties between abstractness and emotionality with regard to metaphoricity; prior work is however limited to the word and sentence levels, and up to date there is no empirical study establishing the extent to which this is also true on the discourse level. This paper explores which textual and perceptual features human annotators perceive as important for the metaphoricity of discourses and expressions, and addresses two research questions more specifically. First, is a metaphorically-perceived discourse more abstract and more emotional in comparison to a literally- perceived discourse? Second, is a metaphorical expression preceded by a more metaphorical/abstract/emotional context than a synonymous literal alternative? We used a dataset of 1,000 corpus-extracted discourses for which crowdsourced annotators (1) provided judgements on whether they perceived the discourses as more metaphorical or more literal, and (2) systematically listed lexical terms which triggered their decisions in (1). Our results indicate that metaphorical discourses are more emotional and to a certain extent more abstract than literal discourses. However, neither the metaphoricity nor the abstractness and emotionality of the preceding discourse seem to play a role in triggering the choice between synonymous metaphorical vs. literal expressions. Our dataset is available at https://www.ims.uni-stuttgart.de/data/discourse-met-lit.

Movies reflect society and also hold power to transform opinions. Social biases and stereotypes present in movies can cause extensive damage due to their reach. These biases are not always found to be the need of storyline but can creep in as the author’s bias. Movie production houses would prefer to ascertain that the bias present in a script is the story’s demand. Today, when deep learning models can give human-level accuracy in multiple tasks, having an AI solution to identify the biases present in the script at the writing stage can help them avoid the inconvenience of stalled release, lawsuits, etc. Since AI solutions are data intensive and there exists no domain specific data to address the problem of biases in scripts, we introduce a new dataset of movie scripts that are annotated for identity bias. The dataset contains dialogue turns annotated for (i) bias labels for seven categories, viz., gender, race/ethnicity, religion, age, occupation, LGBTQ, and other, which contains biases like body shaming, personality bias, etc. (ii) labels for sensitivity, stereotype, sentiment, emotion, emotion intensity, (iii) all labels annotated with context awareness, (iv) target groups and reason for bias labels and (v) expert-driven group-validation process for high quality annotations. We also report various baseline performances for bias identification and category detection on our dataset.

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VoxCommunis: A Corpus for Cross-linguistic Phonetic Analysis
Emily Ahn | Eleanor Chodroff

Cross-linguistic phonetic analysis has long been limited by data scarcity and insufficient computational resources. In the past few years, the availability of large-scale cross-linguistic spoken corpora has increased dramatically, but the data still require considerable computational power and processing for downstream phonetic analysis. To facilitate large-scale cross-linguistic phonetic research in the field, we release the VoxCommunis Corpus, which contains acoustic models, pronunciation lexicons, and word- and phone-level alignments, derived from the publicly available Mozilla Common Voice Corpus. The current release includes data from 36 languages. The corpus also contains acoustic-phonetic measurements, which currently consist of formant frequencies (F1–F4) from all vowel quartiles. Major advantages of this corpus for phonetic analysis include the number of available languages, the large amount of speech per language, as well as the fact that most language datasets have dozens to hundreds of contributing speakers. We demonstrate the utility of this corpus for downstream phonetic research in a descriptive analysis of language-specific vowel systems, as well as an analysis of “uniformity” in vowel realization across languages. The VoxCommunis Corpus is free to download and use under a CC0 license.

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Tracking Textual Similarities in Neo-Latin Drama Networks
Andrea Peverelli | Marieke van Erp | Jan Bloemendal

This paper describes the first experiments towards tracking the complex and international network of text reuse within the Early Modern (XV-XVII centuries) community of Neo-Latin humanists. Our research, conducted within the framework of the TransLatin project, aims at gaining more evidence on the topic of textual similarities and semi-conscious reuse of literary models. It consists of two experiments conveyed through two main research fields (Information Retrieval and Stylometry), as a means to a better understanding of the complex and subtle literary mechanisms underlying the drama production of Modern Age authors and their transnational network of relations. The experiments led to the construction of networks of works and authors that fashion different patterns of similarity and models of evolution and interaction between texts.

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Named Entity Recognition in Estonian 19th Century Parish Court Records
Siim Orasmaa | Kadri Muischnek | Kristjan Poska | Anna Edela

This paper presents a new historical language resource, a corpus of Estonian Parish Court records from the years 1821-1920, annotated for named entities (NE), and reports on named entity recognition (NER) experiments using this corpus. The hand-written records have been transcribed manually via a crowdsourcing project, so the transcripts are of high quality, but the variation of language and spelling is high in these documents due to dialectal variation and the fact that there was a considerable change in Estonian spelling conventions during the time of their writing. The typology of NEs for manual annotation includes 7 categories, but the inter-annotator agreement is as good as 95.0 (mean F1-score). We experimented with fine-tuning BERT-like transfer learning approaches for NER, and found modern Estonian BERT models highly applicable, despite the difficulty of the historical material. Our best model, finetuned Est-RoBERTa, achieved microaverage F1 score of 93.6, which is comparable to state-of-the-art NER performance on the contemporary Estonian.

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Investigating Independence vs. Control: Agenda-Setting in Russian News Coverage on Social Media
Annerose Eichel | Gabriella Lapesa | Sabine Schulte im Walde

Agenda-setting is a widely explored phenomenon in political science: powerful stakeholders (governments or their financial supporters) have control over the media and set their agenda: political and economical powers determine which news should be salient. This is a clear case of targeted manipulation to divert the public attention from serious issues affecting internal politics (such as economic downturns and scandals) by flooding the media with potentially distracting information. We investigate agenda-setting in the Russian social media landscape, exploring the relation between economic indicators and mentions of foreign geopolitical entities, as well as of Russia itself. Our contributions are at three levels: at the level of the domain of the investigation, our study is the first to substructure the Russian media landscape in state-controlled vs. independent outlets in the context of strategic distraction from negative economic trends; at the level of the scope of the investigation, we involve a large set of geopolitical entities (while previous work has focused on the U.S.); at the qualitative level, our analysis of posts on Ukraine, whose relationship with Russia is of high geopolitical relevance, provides further insights into the contrast between state-controlled and independent outlets.

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SLäNDa version 2.0: Improved and Extended Annotation of Narrative and Dialogue in Swedish Literature
Sara Stymne | Carin Östman

In this paper, we describe version 2.0 of the SLäNDa corpus. SLäNDa, the Swedish Literary corpus of Narrative and Dialogue, now contains excerpts from 19 novels, written between 1809–1940. The main focus of the SLäNDa corpus is to distinguish between direct speech and the main narrative. In order to isolate the narrative, we also annotate everything else which does not belong to the narrative, such as thoughts, quotations, and letters. SLäNDa version 2.0 has a slightly updated annotation scheme from version 1.0. In addition, we added new texts from eleven authors and performed quality control on the previous version. We are specifically interested in different ways of marking speech segments, such as quotation marks, dashes, or no marking at all. To allow a detailed evaluation of this aspect, we added dedicated test sets to SLäNDa for these different types of speech marking. In a pilot experiment, we explore the impact of typographic speech marking by using these test sets, as well as artificially stripping the training data of speech markers.

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AGILe: The First Lemmatizer for Ancient Greek Inscriptions
Evelien de Graaf | Silvia Stopponi | Jasper K. Bos | Saskia Peels-Matthey | Malvina Nissim

To facilitate corpus searches by classicists as well as to reduce data sparsity when training models, we focus on the automatic lemmatization of ancient Greek inscriptions, which have not received as much attention in this sense as literary text data has. We show that existing lemmatizers for ancient Greek, trained on literary data, are not performant on epigraphic data, due to major language differences between the two types of texts. We thus train the first inscription-specific lemmatizer achieving above 80% accuracy, and make both the models and the lemmatized data available to the community. We also provide a detailed error analysis highlighting peculiarities of inscriptions which again highlights the importance of a lemmatizer dedicated to inscriptions.

We present the steps taken towards an exploration platform for a multi-modal corpus of German lyric poetry from the Romantic era developed in the project »textklang«. This interdisciplinary project develops a mixed-methods approach for the systematic investigation of the relationship between written text (here lyric poetry) and its potential and actual sonic realisation (in recitations, musical performances etc.). The multi-modal »textklang« platform will be designed to technically and analytically combine three modalities: the poetic text, the audio signal of a recorded recitation and, at a later stage, music scores of a musical setting of a poem. The methodological workflow will enable scholars to develop hypotheses about the relationship between textual form and sonic/prosodic realisation based on theoretical considerations, text interpretation and evidence from recorded recitations. The full workflow will support hypothesis testing either through systematic corpus analysis alone or with addtional contrastive perception experiments. For the experimental track, researchers will be enabled to manipulate prosodic parameters in (re-)synthesised variants of the original recordings. The focus of this paper is on the design of the base corpus and on tools for systematic exploration – placing special emphasis on our response to challenges stemming from multi-modality and the methodologically diverse interdisciplinary setup.

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Predicting the Proficiency Level of Nonnative Hebrew Authors
Isabelle Nguyen | Shuly Wintner

We present classifiers that can accurately predict the proficiency level of nonnative Hebrew learners. This is important for practical (mainly educational) applications, but the endeavor also sheds light on the features that support the classification, thereby improving our understanding of learner language in general, and transfer effects from Arabic, French, and Russian on nonnative Hebrew in particular.

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Trends, Limitations and Open Challenges in Automatic Readability Assessment Research
Sowmya Vajjala

Readability assessment is the task of evaluating the reading difficulty of a given piece of text. This article takes a closer look at contemporary NLP research on developing computational models for readability assessment, identifying the common approaches used for this task, their shortcomings, and some challenges for the future. Where possible, the survey also connects computational research with insights from related work in other disciplines such as education and psychology.

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HateCheckHIn: Evaluating Hindi Hate Speech Detection Models
Mithun Das | Punyajoy Saha | Binny Mathew | Animesh Mukherjee

Due to the sheer volume of online hate, the AI and NLP communities have started building models to detect such hateful content. Recently, multilingual hate is a major emerging challenge for automated detection where code-mixing or more than one language have been used for conversation in social media. Typically, hate speech detection models are evaluated by measuring their performance on the held-out test data using metrics such as accuracy and F1-score. While these metrics are useful, it becomes difficult to identify using them where the model is failing, and how to resolve it. To enable more targeted diagnostic insights of such multilingual hate speech models, we introduce a set of functionalities for the purpose of evaluation. We have been inspired to design this kind of functionalities based on real-world conversation on social media. Considering Hindi as a base language, we craft test cases for each functionality. We name our evaluation dataset HateCheckHIn. To illustrate the utility of these functionalities , we test state-of-the-art transformer based m-BERT model and the Perspective API.

Fast-developing fields such as Artificial Intelligence (AI) often outpace the efforts of encyclopedic sources such as Wikipedia, which either do not completely cover recently-introduced topics or lack such content entirely. As a result, methods for automatically producing content are valuable tools to address this information overload. We show that recent advances in pretrained language modeling can be combined for a two-stage extractive and abstractive approach for Wikipedia lead paragraph generation. We extend this approach to generate longer Wikipedia-style summaries with sections and examine how such methods struggle in this application through detailed studies with 100 reference human-collected surveys. This is the first study on utilizing web resources for long Wikipedia-style summaries to the best of our knowledge.

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MS-LaTTE: A Dataset of Where and When To-do Tasks are Completed
Sujay Kumar Jauhar | Nirupama Chandrasekaran | Michael Gamon | Ryen White

Tasks are a fundamental unit of work in the daily lives of people, who are increasingly using digital means to keep track of, organize, triage, and act on them. These digital tools – such as task management applications – provide a unique opportunity to study and understand tasks and their connection to the real world, and through intelligent assistance, help people be more productive. By logging signals such as text, timestamp information, and social connectivity graphs, an increasingly rich and detailed picture of how tasks are created and organized, what makes them important, and who acts on them, can be progressively developed. Yet the context around actual task completion remains fuzzy, due to the basic disconnect between actions taken in the real world and telemetry recorded in the digital world. Thus, in this paper we compile and release a novel, real-life, large-scale dataset called MS-LaTTE that captures two core aspects of the context surrounding task completion: location and time. We describe our annotation framework and conduct a number of analyses on the data that were collected, demonstrating that it captures intuitive contextual properties for common tasks. Finally, we test the dataset on the two problems of predicting spatial and temporal task co-occurrence, concluding that predictors for co-location and co-time are both learnable, with a BERT fine-tuned model outperforming several other baselines. The MS-LaTTE dataset provides an opportunity to tackle many new modeling challenges in contextual task understanding and we hope that its release will spur future research in task intelligence more broadly.

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KazakhTTS2: Extending the Open-Source Kazakh TTS Corpus With More Data, Speakers, and Topics
Saida Mussakhojayeva | Yerbolat Khassanov | Huseyin Atakan Varol

We present an expanded version of our previously released Kazakh text-to-speech (KazakhTTS) synthesis corpus. In the new KazakhTTS2 corpus, the overall size has increased from 93 hours to 271 hours, the number of speakers has risen from two to five (three females and two males), and the topic coverage has been diversified with the help of new sources, including a book and Wikipedia articles. This corpus is necessary for building high-quality TTS systems for Kazakh, a Central Asian agglutinative language from the Turkic family, which presents several linguistic challenges. We describe the corpus construction process and provide the details of the training and evaluation procedures for the TTS system. Our experimental results indicate that the constructed corpus is sufficient to build robust TTS models for real-world applications, with a subjective mean opinion score ranging from 3.6 to 4.2 for all the five speakers. We believe that our corpus will facilitate speech and language research for Kazakh and other Turkic languages, which are widely considered to be low-resource due to the limited availability of free linguistic data. The constructed corpus, code, and pretrained models are publicly available in our GitHub repository.

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A Graph-Based Method for Unsupervised Knowledge Discovery from Financial Texts
Joel Oksanen | Abhilash Majumder | Kumar Saunack | Francesca Toni | Arun Dhondiyal

The need for manual review of various financial texts, such as company filings and news, presents a major bottleneck in financial analysts’ work. Thus, there is great potential for the application of NLP methods, tools and resources to fulfil a genuine industrial need in finance. In this paper, we show how this potential can be fulfilled by presenting an end-to-end, fully unsupervised method for knowledge discovery from financial texts. Our method creatively integrates existing resources to construct automatically a knowledge graph of companies and related entities as well as to carry out unsupervised analysis of the resulting graph to provide quantifiable and explainable insights from the produced knowledge. The graph construction integrates entity processing and semantic expansion, before carrying out open relation extraction. We illustrate our method by calculating automatically the environmental rating for companies in the S&P 500, based on company filings with the SEC (Securities and Exchange Commission). We then show the usefulness of our method in this setting by providing an assessment of our method’s outputs with an independent MSCI source.

The number of depression and suicide risk cases on social media platforms is ever-increasing, and the lack of depression detection mechanisms on these platforms is becoming increasingly apparent. A majority of work in this area has focused on leveraging linguistic features while dealing with small-scale datasets. However, one faces many obstacles when factoring into account the vastness and inherent imbalance of social media content. In this paper, we aim to optimize the performance of user-level depression classification to lessen the burden on computational resources. The resulting system executes in a quicker, more efficient manner, in turn making it suitable for deployment. To simulate a platform agnostic framework, we simultaneously replicate the size and composition of social media to identify victims of depression. We systematically design a solution that categorizes post embeddings, obtained by fine-tuning transformer models such as RoBERTa, and derives user-level representations using hierarchical attention networks. We also introduce a novel mental health dataset to enhance the performance of depression categorization. We leverage accounts of depression taken from this dataset to infuse domain-specific elements into our framework. Our proposed methods outperform numerous baselines across standard metrics for the task of depression detection in text.

In this paper, we compare the performance of two BERT-based text classifiers whose task is to classify patients (more precisely, their medical histories) as having or not having implant(s) in their body. One classifier is a fully-supervised BERT classifier. The other one is a semi-supervised GAN-BERT classifier. Both models are compared against a fully-supervised SVM classifier. Since fully-supervised classification is expensive in terms of data annotation, with the experiments presented in this paper, we investigate whether we can achieve a competitive performance with a semi-supervised classifier based only on a small amount of annotated data. Results are promising and show that the semi-supervised classifier has a competitive performance with the fully-supervised classifier.

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Standardisation of Dialect Comments in Social Networks in View of Sentiment Analysis : Case of Tunisian Dialect
Saméh Kchaou | Rahma Boujelbane | Emna Fsih | Lamia Hadrich-Belguith

With the growing access to the internet, the spoken Arabic dialect language becomes informal languages written in social media. Most users post comments using their own dialect. This linguistic situation inhibits mutual understanding between internet users and makes difficult to use computational approaches since most Arabic resources are intended for the formal language: Modern Standard Arabic (MSA). In this paper, we present a pipeline to standardize the written texts in social networks by translating them to the standard language MSA. We fine-tun at first an identification bert-based model to select Tunisian Dialect (TD) from MSA and other dialects. Then, we learned transformer model to translate TD to MSA. The final system includes the translated TD text and the originally text written in MSA. Each of these steps was evaluated on the same test corpus. In order to test the effectiveness of the approach, we compared two opinion analysis models, the first intended for the Sentiment Analysis (SA) of dialect texts and the second for the MSA texts. We concluded that through standardization we obtain the best score.

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EnsyNet: A Dataset for Encouragement and Sympathy Detection
Tiberiu Sosea | Cornelia Caragea

More and more people turn to Online Health Communities to seek social support during their illnesses. By interacting with peers with similar medical conditions, users feel emotionally and socially supported, which in turn leads to better adherence to therapy. Current studies in Online Health Communities focus only on the presence or absence of emotional support, while the available datasets are scarce or limited in terms of size. To enable development on emotional support detection, we introduce EnsyNet, a dataset of 6,500 sentences annotated with two types of support: encouragement and sympathy. We train BERT-based classifiers on this dataset, and apply our best BERT model in two large scale experiments. The results of these experiments show that receiving encouragements or sympathy improves users’ emotional state, while the lack of emotional support negatively impacts patients’ emotional state.

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Preliminary Results on the Evaluation of Computational Tools for the Analysis of Quechua and Aymara
Marcelo Yuji Himoro | Antonio Pareja-Lora

This research has focused on evaluating the existing open-source morphological analyzers for two of the most widely spoken indigenous macrolanguages in South America, namely Quechua and Aymara. Firstly, we have evaluated their performance (precision, recall and F1 score) for the individual languages for which they were developed (Cuzco Quechua and Aymara). Secondly, in order to assess how these tools handle other individual languages of the macrolanguage, we have extracted some sample text from school textbooks and educational resources. This sample text was edited in the different countries where these macrolanguages are spoken (Colombia, Ecuador, Peru, Bolivia, Chile and Argentina for Quechua; and Bolivia, Peru and Chile for Aymara), and it includes their different standardized forms (10 individual languages of Quechua and 3 of Aymara). Processing this text by means of the tools, we have (i) calculated their coverage (number of words recognized and analyzed) and (ii) studied in detail the cases for which each tool was unable to generate any output. Finally, we discuss different ways in which these tools could be optimized, either to improve their performances or, in the specific case of Quechua, to cover more individual languages of this macrolanguage in future works as well.

Over the past decade, researchers have started to explore the use of NLP to develop tools aimed at helping the public, vendors, and regulators analyze disclosures made in privacy policies. With the introduction of new privacy regulations, the language of privacy policies is also evolving, and disclosures made by the same organization are not always the same in different languages, especially when used to communicate with users who fall under different jurisdictions. This work explores the use of language technologies to capture and analyze these differences at scale. We introduce an annotation scheme designed to capture the nuances of two new landmark privacy regulations, namely the EU’s GDPR and California’s CCPA/CPRA. We then introduce the first bilingual corpus of mobile app privacy policies consisting of 64 privacy policies in English (292K words) and 91 privacy policies in German (478K words), respectively with manual annotations for 8K and 19K fine-grained data practices. The annotations are used to develop computational methods that can automatically extract “disclosures” from privacy policies. Analysis of a subset of 59 “semi-parallel” policies reveals differences that can be attributed to different regulatory regimes, suggesting that systematic analysis of policies using automated language technologies is indeed a worthwhile endeavor.

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MeSHup: Corpus for Full Text Biomedical Document Indexing
Xindi Wang | Robert E. Mercer | Frank Rudzicz

Medical Subject Heading (MeSH) indexing refers to the problem of assigning a given biomedical document with the most relevant labels from an extremely large set of MeSH terms. Currently, the vast number of biomedical articles in the PubMed database are manually annotated by human curators, which is time consuming and costly; therefore, a computational system that can assist the indexing is highly valuable. When developing supervised MeSH indexing systems, the availability of a large-scale annotated text corpus is desirable. A publicly available, large corpus that permits robust evaluation and comparison of various systems is important to the research community. We release a large scale annotated MeSH indexing corpus, MeSHup, which contains 1,342,667 full text articles, together with the associated MeSH labels and metadata, authors and publication venues that are collected from the MEDLINE database. We train an end-to-end model that combines features from documents and their associated labels on our corpus and report the new baseline.

Applying methods in natural language processing on electronic health records (EHR) data has attracted rising interests. Existing corpus and annotation focus on modeling textual features and relation prediction. However, there are a paucity of annotated corpus built to model clinical diagnostic thinking, a processing involving text understanding, domain knowledge abstraction and reasoning. In this work, we introduce a hierarchical annotation schema with three stages to address clinical text understanding, clinical reasoning and summarization. We create an annotated corpus based on a large collection of publicly available daily progress notes, a type of EHR that is time-sensitive, problem-oriented, and well-documented by the format of Subjective, Objective, Assessment and Plan (SOAP). We also define a new suite of tasks, Progress Note Understanding, with three tasks utilizing the three annotation stages. This new suite aims at training and evaluating future NLP models for clinical text understanding, clinical knowledge representation, inference and summarization.

The multilingual parallel corpus is an important resource for many applications of natural language processing (NLP). For machine translation, the size and quality of the training corpus mainly affects the quality of the translation models. In this work, we present the method for building high-quality multilingual parallel corpus in the news domain and for some low-resource languages, including Vietnamese, Laos, and Khmer, to improve the quality of multilingual machine translation in these areas. We also publicized this one that includes 500.000 Vietnamese-Chinese bilingual sentence pairs; 150.000 Vietnamese-Laos bilingual sentence pairs, and 150.000 Vietnamese-Khmer bilingual sentence pairs.

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Developing A Multilabel Corpus for the Quality Assessment of Online Political Talk
Kokil Jaidka

This paper motivates and presents the Twitter Deliberative Politics dataset, a corpus of political tweets labeled for its deliberative characteristics. The corpus was randomly sampled from replies to US congressmen and women. It is expected to be useful to a general community of computational linguists, political scientists, and social scientists interested in the study of online political expression, computer-mediated communication, and political deliberation. The data sampling and annotation methods are discussed and classical machine learning approaches are evaluated for their predictive performance on the different deliberative facets. The paper concludes with a discussion of future work aimed at developing dictionaries for the quality assessment of online political talk in English. The dataset and a demo dashboard are available at https://github.com/kj2013/twitter-deliberative-politics.

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BILinMID: A Spanish-English Corpus of the US Midwest
Irati Hurtado

This paper describes the Bilinguals in the Midwest (BILinMID) Corpus, a comparable text corpus of the Spanish and English spoken in the US Midwest by various types of bilinguals. Unlike other areas within the US where language contact has been widely documented (e.g., the Southwest), Spanish-English bilingualism in the Midwest has been understudied despite an increase in its Hispanic population. The BILinMID Corpus contains short stories narrated in Spanish and in English by 72 speakers representing different types of bilinguals: early simultaneous bilinguals, early sequential bilinguals, and late second language learners. All stories have been transcribed and annotated using various natural language processing tools. Additionally, a user interface has also been created to facilitate searching for specific patterns in the corpus as well as to filter out results according to specified criteria. Guidelines and procedures followed to create the corpus and the user interface are described in detail in the paper. The corpus is fully available online and it might be particularly interesting for researchers working on language variation and contact.

Document authoring involves a lengthy revision process, marked by individual edits that are frequently linked to comments. Modeling the relationship between edits and comments leads to a better understanding of document evolution, potentially benefiting applications such as content summarization, and task triaging. Prior work on understanding revisions has primarily focused on classifying edit intents, but falling short of a deeper understanding of the nature of these edits. In this paper, we present explore the challenge of describing an edit at two levels: identifying the edit intent, and describing the edit using free-form text. We begin by defining a taxonomy of general edit intents and introduce a new dataset of full revision histories of Wikipedia pages, annotated with each revision’s edit intent. Using this dataset, we train a classifier that achieves a 90% accuracy in identifying edit intent. We use this classifier to train a distantly-supervised model that generates a high-level description of a revision in free-form text. Our experimental results show that incorporating edit intent information aids in generating better edit descriptions. We establish a set of baselines for the edit description task, achieving a best score of 28 ROUGE, thus demonstrating the effectiveness of our layered approach to edit understanding.

The construct of linguistic complexity has been widely used in language learning research. Several text analysis tools have been created to automatically analyze linguistic complexity. However, the indexes supported by several existing Chinese text analysis tools are limited and different because of different research purposes. CTAP is an open-source linguistic complexity measurement extraction tool, which prompts any research purposes. Although it was originally developed for English, the Unstructured Information Management (UIMA) framework it used allows the integration of other languages. In this study, we integrated the Chinese component into CTAP, describing the index sets it incorporated and comparing it with three linguistic complexity tools for Chinese. The index set includes four levels of 196 linguistic complexity indexes: character level, word level, sentence level, and discourse level. So far, CTAP has implemented automatic calculation of complexity characteristics for four languages, aiming to help linguists without NLP background study language complexity.

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A Corpus for Suggestion Mining of German Peer Feedback
Dominik Pfütze | Eva Ritz | Julius Janda | Roman Rietsche

Peer feedback in online education becomes increasingly important to meet the demand for feedback in large scale classes, such as e.g. Massive Open Online Courses (MOOCs). However, students are often not experts in how to write helpful feedback to their fellow students. In this paper, we introduce a corpus compiled from university students’ peer feedback to be able to detect suggestions on how to improve the students’ work and therefore being able to capture peer feedback helpfulness. To the best of our knowledge, this corpus is the first student peer feedback corpus in German which additionally was labelled with a new annotation scheme. The corpus consists of more than 600 written feedback (about 7,500 sentences). The utilisation of the corpus is broadly ranged from Dependency Parsing to Sentiment Analysis to Suggestion Mining, etc. We applied the latter to empirically validate the utility of the new corpus. Suggestion Mining is the extraction of sentences that contain suggestions from unstructured text. In this paper, we present a new annotation scheme to label sentences for Suggestion Mining. Two independent annotators labelled the corpus and achieved an inter-annotator agreement of 0.71. With the help of an expert arbitrator a gold standard was created. An automatic classification using BERT achieved an accuracy of 75.3%.

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CLGC: A Corpus for Chinese Literary Grace Evaluation
Yi Li | Dong Yu | Pengyuan Liu

In this paper, we construct a Chinese literary grace corpus, CLGC, with 10,000 texts and more than 1.85 million tokens. Multi-level annotations are provided for each text in our corpus, including literary grace level, sentence category, and figure-of-speech type. Based on the corpus, we dig deep into the correlation between fine-grained features (semantic information, part-of-speech and figure-of-speech, etc.) and literary grace level. We also propose a new Literary Grace Evaluation (LGE) task, which aims at making a comprehensive assessment of the literary grace level according to the text. In the end, we build some classification models with machine learning algorithms (such as SVM, TextCNN) to prove the effectiveness of our features and corpus for LGE. The results of our preliminary classification experiments have achieved 79.71% on the weighted average F1-score.

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Anonymising the SAGT Speech Corpus and Treebank
Özlem Çetinoğlu | Antje Schweitzer

Anonymisation, that is identifying and neutralising sensitive references, is a crucial part of dataset creation. In this paper, we describe the anonymisation process of a Turkish-German code-switching corpus, namely SAGT, which consists of speech data and a treebank that is built on its transcripts. We employed a selective pseudonymisation approach where we manually identified sensitive references to anonymise and replaced them with surrogate values on the treebank side. In addition to maintaining data privacy, our primary concerns in surrogate selection were keeping the integrity of code-switching properties, morphosyntactic annotation layers, and semantics. After the treebank anonymisation, we anonymised the speech data by mapping between the treebank sentences and audio transcripts with the help of Praat scripts. The treebank is publicly available for research purposes and the audio files can be obtained via an individual licence agreement.

In grammatical error correction (GEC), automatic evaluation is considered as an important factor for research and development of GEC systems. Previous studies on automatic evaluation have shown that quality estimation models built from datasets with manual evaluation can achieve high performance in automatic evaluation of English GEC. However, quality estimation models have not yet been studied in Japanese, because there are no datasets for constructing quality estimation models. In this study, therefore, we created a quality estimation dataset with manual evaluation to build an automatic evaluation model for Japanese GEC. By building a quality estimation model using this dataset and conducting a meta-evaluation, we verified the usefulness of the quality estimation model for Japanese GEC.

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Enhanced Distant Supervision with State-Change Information for Relation Extraction
Jui Shah | Dongxu Zhang | Sam Brody | Andrew McCallum

In this work, we introduce a method for enhancing distant supervision with state-change information for relation extraction. We provide a training dataset created via this process, along with manually annotated development and test sets. We present an analysis of the curation process and data, and compare it to standard distant supervision. We demonstrate that the addition of state-change information reduces noise when used for static relation extraction, and can also be used to train a relation-extraction system that detects a change of state in relations.

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The Hebrew Essay Corpus
Chen Gafni | Anat Prior | Shuly Wintner

We present the Hebrew Essay Corpus: an annotated corpus of Hebrew language argumentative essays authored by prospective higher-education students. The corpus includes both essays by native speakers, written as part of the psychometric exam that is used to assess their future success in academic studies; and essays authored by non-native speakers, with three different native languages, that were written as part of a language aptitude test. The corpus is uniformly encoded and stored. The non-native essays were annotated with target hypotheses whose main goal is to make the texts amenable to automatic processing (morphological and syntactic analysis). The corpus is available for academic purposes upon request. We describe the corpus and the error correction and annotation schemes used in its analysis. In addition to introducing this new resource, we discuss the challenges of identifying and analyzing non-native language use in general, and propose various ways for dealing with these challenges.

We have constructed the Corpus of Everyday Japanese Conversation (CEJC) and published it in March 2022. The CEJC is designed to contain various kinds of everyday conversations in a balanced manner to capture their diversity. The CEJC features not only audio but also video data to facilitate precise understanding of the mechanism of real-life social behavior. The publication of a large-scale corpus of everyday conversations that includes video data is a new approach. The CEJC contains 200 hours of speech, 577 conversations, about 2.4 million words, and a total of 1675 conversants. In this paper, we present an overview of the corpus, including the recording method and devices, structure of the corpus, formats of video and audio files, transcription, and annotations. We then report some results of the evaluation of the CEJC in terms of conversant and conversation attributes. We show that the CEJC includes a good balance of adult conversants in terms of gender and age, as well as a variety of conversations in terms of conversation forms, places, activities, and numbers of conversants.

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Developing Language Resources and NLP Tools for the North Korean Language
Arda Akdemir | Yeojoo Jeon | Tetsuo Shibuya

Since the division of Korea, the two Korean languages have diverged significantly over the last 70 years. However, due to the lack of linguistic source of the North Korean language, there is no DPRK-based language model. Consequently, scholars rely on the Korean language model by utilizing South Korean linguistic data. In this paper, we first present a large-scale dataset for the North Korean language. We use the dataset to train a BERT-based language model, DPRK-BERT. Second, we annotate a subset of this dataset for the sentiment analysis task. Finally, we compare the performance of different language models for masked language modeling and sentiment analysis tasks.

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Developing a Dataset of Overridden Information in Wikipedia
Masatoshi Tsuchiya | Yasutaka Yokoi

This paper proposes a new task of detecting information override. Since all information on the Web is not updated in a timely manner, the necessity is created for information that is overridden by another information source to be discarded. The task is formalized as a binary classification problem to determine whether a reference sentence has overridden a target sentence. In investigating this task, this paper describes a construction procedure for the dataset of overridden information by collecting sentence pairs from the difference between two versions of Wikipedia. Our developing dataset shows that the old version of Wikipedia contains much overridden information and that the detection of information override is necessary.

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BRATECA (Brazilian Tertiary Care Dataset): a Clinical Information Dataset for the Portuguese Language
Bernardo Consoli | Henrique D. P. dos Santos | Ana Helena D. P. S. Ulbrich | Renata Vieira | Rafael H. Bordini

Computational medicine research requires clinical data for training and testing purposes, so the development of datasets composed of real hospital data is of utmost importance in this field. Most such data collections are in the English language, were collected in anglophone countries, and do not reflect other clinical realities, which increases the importance of national datasets for projects that hope to positively impact public health. This paper presents a new Brazilian Clinical Dataset containing over 70,000 admissions from 10 hospitals in two Brazilian states, composed of a sum total of over 2.5 million free-text clinical notes alongside data pertaining to patient information, prescription information, and exam results. This data was collected, organized, deidentified, and is being distributed via credentialed access for the use of the research community. In the course of presenting the new dataset, this paper will explore the new dataset’s structure, population, and potential benefits of using this dataset in clinical AI tasks.

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Universal Grammatical Dependencies for Portuguese with CINTIL Data, LX Processing and CLARIN support
António Branco | João Ricardo Silva | Luís Gomes | João António Rodrigues

The grammatical framework for the mapping between linguistic form and meaning representation known as Universal Dependencies relies on a non-constituency syntactic analysis that is centered on the notion of grammatical relation (e.g. Subject, Object, etc.). Given its core goal of providing a common set of analysis primitives suitable to every natural language, and its practical objective of fostering their computational grammatical processing, it keeps being an active domain of research in science and technology of language. This paper presents a new collection of quality language resources for the computational processing of the Portuguese language under the Universal Dependencies framework (UD). This is an all-encompassing, publicly available open collection of mutually consistent and inter-operable scientific resources that includes reliably annotated corpora, top-performing processing tools and expert support services: a new UPOS-annotated corpus, CINTIL-UPos, with 675K tokens and a new UD treebank, CINTIL-UDep Treebank, with nearly 38K sentences; a UPOS tagger, LX-UTagger, and a UD parser, LX-UDParser, trained on these corpora, available both as local stand-alone tools and as remote web-based services; and helpdesk support ensured by the Knowledge Center for the Science and Technology of Portuguese of the CLARIN research infrastructure.

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CWID-hi: A Dataset for Complex Word Identification in Hindi Text
Gayatri Venugopal | Dhanya Pramod | Ravi Shekhar

Text simplification is a method for improving the accessibility of text by converting complex sentences into simple sentences. Multiple studies have been done to create datasets for text simplification. However, most of these datasets focus on high-resource languages only. In this work, we proposed a complex word dataset for Hindi, a language largely ignored in text simplification literature. We used various Hindi knowledge annotators for annotation to capture the annotator’s language knowledge. Our analysis shows a significant difference between native and non-native annotators’ perception of word complexity. We also built an automatic complex word classifier using a soft voting approach based on the predictions from tree-based ensemble classifiers. These models behave differently for annotations made by different categories of users, such as native and non-native speakers. Our dataset and analysis will help simplify Hindi text depending on the user’s language understanding. The dataset is available at https://zenodo.org/record/5229160.

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Automatic Classification of Russian Learner Errors
Alla Rozovskaya

Grammatical Error Correction systems are typically evaluated overall, without taking into consideration performance on individual error types because system output is not annotated with respect to error type. We introduce a tool that automatically classifies errors in Russian learner texts. The tool takes an edit pair consisting of the original token(s) and the corresponding replacement and provides a grammatical error category. Manual evaluation of the output reveals that in more than 93% of cases the error categories are judged as correct or acceptable. We apply the tool to carry out a fine-grained evaluation on the performance of two error correction systems for Russian.

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Annotation of metaphorical expressions in the Basic Corpus of Polish Metaphors
Elżbieta Hajnicz

This paper presents a corpus of Polish texts annotated with metaphorical expressions. It is composed of two parts of comparable size, selected from two subcorpora of the Polish National Corpus: the subcorpus manually annotated on morphosyntactic level, named entities level etc., and the Polish Coreference Corpus, with manually annotated mentions and the coreference relations between them, but automatically annotated on the morphosyntactic level (only the second part is actually annotated). In the paper we briefly outline the method for identifying metaphorical expressions in a text, based on the MIPVU procedure. The main difference is the stress put on novel metaphors and considering neologistic derivatives that have metaphorical properties. The annotation procedure is based on two notions: vehicle – a part of an expression used metaphorically, representing a source domain and its topic – a part referring to reality, representing a target domain. Next, we propose several features (text form, conceptual structure, conventionality and contextuality) to classify metaphorical expressions identified in texts. Additionally, some metaphorical expressions are identified as concerning personal identity matters and classified w.r.t. their properties. Finally, we analyse and evaluate the results of the annotation.

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ChiMST: A Chinese Medical Corpus for Word Segmentation and Medical Term Recognition
Yuanhe Tian | Han Qin | Fei Xia | Yan Song

Chinese word segmentation (CWS) and named entity recognition (NER) are two important tasks in Chinese natural language processing. To achieve good model performance on these tasks, existing neural approaches normally require a large amount of labeled training data, which is often unavailable for specific domains such as the Chinese medical domain due to privacy and legal issues. To address this problem, we have developed a Chinese medical corpus named ChiMST which consists of question-answer pairs collected from an online medical healthcare platform and is annotated with word boundary and medical term information. For word boundary, we mainly follow the word segmentation guidelines for the Penn Chinese Treebank (Xia, 2000); for medical terms, we define 9 categories and 18 sub-categories after consulting medical experts. To provide baselines on this corpus, we train existing state-of-the-art models on it and achieve good performance. We believe that the corpus and the baseline systems will be a valuable resource for CWS and NER research on the medical domain.

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Building a Synthetic Biomedical Research Article Citation Linkage Corpus
Sudipta Singha Roy | Robert E. Mercer

Citations are frequently used in publications to support the presented results and to demonstrate the previous discoveries while also assisting the reader in following the chronological progression of information through publications. In scientific publications, a citation refers to the referenced document, but it makes no mention of the exact span of text that is being referred to. Connecting the citation to this span of text is called citation linkage. In this paper, to find these citation linkages in biomedical research publications using deep learning, we provide a synthetic silver standard corpus as well as the method to build this corpus. The motivation for building this corpus is to provide a training set for deep learning models that will locate the text spans in a reference article, given a citing statement, based on semantic similarity. This corpus is composed of sentence pairs, where one sentence in each pair is the citing statement and the other one is a candidate cited statement from the referenced paper. The corpus is annotated using an unsupervised sentence embedding method. The effectiveness of this silver standard corpus for training citation linkage models is validated against a human-annotated gold standard corpus.

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Dataset Construction for Scientific-Document Writing Support by Extracting Related Work Section and Citations from PDF Papers
Keita Kobayashi | Kohei Koyama | Hiromi Narimatsu | Yasuhiro Minami

To augment datasets used for scientific-document writing support research, we extract texts from “Related Work” sections and citation information in PDF-formatted papers published in English. The previous dataset was constructed entirely with Tex-formatted papers, from which it is easy to extract citation information. However, since many publicly available papers in various fields are provided only in PDF format, a dataset constructed using only Tex papers has limited utility. To resolve this problem, we augment the existing dataset by extracting the titles of sections using the visual features of PDF documents and extracting the Related Work section text using the explicit title information. Since text generated from the figures and footnotes appearing in the extraction target areas is considered noise, we remove instances of such text. Moreover, we map the cited paper’s information obtained using existing tools to citation marks detected by regular expression rules, resulting in pairs of cited paper information and text of the Related Work section. By evaluating body text extraction and citation mapping in the constructed dataset, the accuracy of the proposed dataset was found to be close to that of the previous dataset. Accordingly, we demonstrated the possibility of building a significantly augmented dataset.

Paraphrase identification task can be easily challenged by changing word order, e.g. as in “Can a good person become bad?”. While for English this problem was tackled by the PAWS dataset (Zhang et al., 2019), datasets for Russian paraphrase detection lack non-paraphrase examples with high lexical overlap. We present RuPAWS, the first adversarial dataset for Russian paraphrase identification. Our dataset consists of examples from PAWS translated to the Russian language and manually annotated by native speakers. We compare it to the largest available dataset for Russian ParaPhraser and show that the best available paraphrase identifiers for the Russian language fail on the RuPAWS dataset. At the same time, the state-of-the-art paraphrasing model RuBERT trained on both RuPAWS and ParaPhraser obtains high performance on the RuPAWS dataset while maintaining its accuracy on the ParaPhraser benchmark. We also show that RuPAWS can measure the sensitivity of models to word order and syntax structure since simple baselines fail even when given RuPAWS training samples.

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Atril: an XML Visualization System for Corpus Texts
Andressa Rodrigues Gomide | Conceição Carapinha | Cornelia Plag

This paper presents Atril, an XML visualization system for corpus texts, developed for, but not restricted to, the project Corpus de Audiências (CorAuDis), a corpus composed of transcripts of sessions of criminal proceedings recorded at the Coimbra Court. The main aim of the tool is to provide researchers with a web-based environment that allows for an easily customizable visualization of corpus texts with heavy structural annotation. Existing corpus analysis tools such as SketchEngine, TEITOK and CQPweb offer some kind of visualization mechanisms, but, to our knowledge, none meets our project’s main needs. Our requirements are a system that is open-source; that can be easily connected to CQPweb and TEITOK, that provides a full text-view with switchable visualization templates, that allows for the visualization of overlapping utterances. To meet those requirements, we created Atril, a module with a corpus XML file viewer, a visualization management system, and a word alignment tool.

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MASALA: Modelling and Analysing the Semantics of Adpositions in Linguistic Annotation of Hindi
Aryaman Arora | Nitin Venkateswaran | Nathan Schneider

We present a completed, publicly available corpus of annotated semantic relations of adpositions and case markers in Hindi. We used the multilingual SNACS annotation scheme, which has been applied to a variety of typologically diverse languages. Building on past work examining linguistic problems in SNACS annotation, we use language models to attempt automatic labelling of SNACS supersenses in Hindi and achieve results competitive with past work on English. We look towards upstream applications in semantic role labelling and extension to related languages such as Gujarati.

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Universal Dependencies for Punjabi
Aryaman Arora

We introduce the first Universal Dependencies treebank for Punjabi (written in the Gurmukhi script) and discuss corpus design and linguistic phenomena encountered in annotation. The treebank covers a variety of genres and has been annotated for POS tags, dependency relations, and graph-based Enhanced Dependencies. We aim to expand the diversity of coverage of Indo-Aryan languages in UD.

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TeSum: Human-Generated Abstractive Summarization Corpus for Telugu
Ashok Urlana | Nirmal Surange | Pavan Baswani | Priyanka Ravva | Manish Shrivastava

Expert human annotation for summarization is definitely an expensive task, and can not be done on huge scales. But with this work, we show that even with a crowd sourced summary generation approach, quality can be controlled by aggressive expert informed filtering and sampling-based human evaluation. We propose a pipeline that crowd-sources summarization data and then aggressively filters the content via: automatic and partial expert evaluation. Using this pipeline we create a high-quality Telugu Abstractive Summarization dataset (TeSum) which we validate with sampling-based human evaluation. We also provide baseline numbers for various models commonly used for summarization. A number of recently released datasets for summarization, scraped the web-content relying on the assumption that summary is made available with the article by the publishers. While this assumption holds for multiple resources (or news-sites) in English, it should not be generalised across languages without thorough analysis and verification. Our analysis clearly shows that this assumption does not hold true for most Indian language news resources. We show that our proposed filtration pipeline can even be applied to these large-scale scraped datasets to extract better quality article-summary pairs.

We present a corpus of simulated counselling sessions consisting of speech- and text-based dialogs in Cantonese. Consisting of 152K Chinese characters, the corpus labels the dialog act of both client and counsellor utterances, segments each dialog into stages, and identifies the forward and backward links in the dialog. We analyze the distribution of client and counsellor communicative intentions in the various stages, and discuss significant patterns of the dialog flow.

The ultimate goal of dialog research is to develop systems that can be effectively used in interactive settings by real users. To this end, we introduced the Interactive Evaluation of Dialog Track at the 9th Dialog System Technology Challenge. This track consisted of two sub-tasks. The first sub-task involved building knowledge-grounded response generation models. The second sub-task aimed to extend dialog models beyond static datasets by assessing them in an interactive setting with real users. Our track challenges participants to develop strong response generation models and explore strategies that extend them to back-and-forth interactions with real users. The progression from static corpora to interactive evaluation introduces unique challenges and facilitates a more thorough assessment of open-domain dialog systems. This paper provides an overview of the track, including the methodology and results. Furthermore, it provides insights into how to best evaluate open-domain dialog models.

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HADREB: Human Appraisals and (English) Descriptions of Robot Emotional Behaviors
Josue Torres-Fonseca | Casey Kennington

Humans sometimes anthropomorphize everyday objects, but especially robots that have human-like qualities and that are often able to interact with and respond to humans in ways that other objects cannot. Humans especially attribute emotion to robot behaviors, partly because humans often use and interpret emotions when interacting with other humans, and they apply that capability when interacting with robots. Moreover, emotions are a fundamental part of the human language system and emotions are used as scaffolding for language learning, making them an integral part of language learning and meaning. However, there are very few datasets that explore how humans perceive the emotional states of robots and how emotional behaviors relate to human language. To address this gap we have collected HADREB, a dataset of human appraisals and English descriptions of robot emotional behaviors collected from over 30 participants. These descriptions and human emotion appraisals are collected using the Mistyrobotics Misty II and the Digital Dream Labs Cozmo (formerly Anki) robots. The dataset contains English descriptions and emotion appraisals of more than 500 descriptions and graded valence labels of 8 emotion pairs for each behavior and each robot. In this paper we describe the process of collecting and cleaning the data, give a general analysis of the data, and evaluate the usefulness of the dataset in two experiments, one using a language model to map descriptions to emotions, the other maps robot behaviors to emotions.

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Dialogue Collection for Recording the Process of Building Common Ground in a Collaborative Task
Koh Mitsuda | Ryuichiro Higashinaka | Yuhei Oga | Sen Yoshida

To develop a dialogue system that can build common ground with users, the process of building common ground through dialogue needs to be clarified. However, the studies on the process of building common ground have not been well conducted; much work has focused on finding the relationship between a dialogue in which users perform a collaborative task and its task performance represented by the final result of the task. In this study, to clarify the process of building common ground, we propose a data collection method for automatically recording the process of building common ground through a dialogue by using the intermediate result of a task. We collected 984 dialogues, and as a result of investigating the process of building common ground, we found that the process can be classified into several typical patterns and that conveying each worker’s understanding through affirmation of a counterpart’s utterances especially contributes to building common ground. In addition, toward dialogue systems that can build common ground, we conducted an automatic estimation of the degree of built common ground and found that its degree can be estimated quite accurately.

When individuals communicate with each other, they use different vocabulary, speaking speed, facial expressions, and body language depending on the people they talk to. This paper focuses on the speaker’s age as a factor that affects the change in communication. We collected a multimodal dialogue corpus with a wide range of speaker ages. As a dialogue task, we focus on travel, which interests people of all ages, and we set up a task based on a tourism consultation between an operator and a customer at a travel agency. This paper provides details of the dialogue task, the collection procedure and annotations, and the analysis on the characteristics of the dialogues and facial expressions focusing on the age of the speakers. Results of the analysis suggest that the adult speakers have more independent opinions, the older speakers more frequently express their opinions frequently compared with other age groups, and the operators expressed a smile more frequently to the minor speakers.

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Strategy-level Entrainment of Dialogue System Users in a Creative Visual Reference Resolution Task
Deepthi Karkada | Ramesh Manuvinakurike | Maike Paetzel-Prüsmann | Kallirroi Georgila

In this work, we study entrainment of users playing a creative reference resolution game with an autonomous dialogue system. The language understanding module in our dialogue system leverages annotated human-wizard conversational data, openly available knowledge graphs, and crowd-augmented data. Unlike previous entrainment work, our dialogue system does not attempt to make the human conversation partner adopt lexical items in their dialogue, but rather to adapt their descriptive strategy to one that is simpler to parse for our natural language understanding unit. By deploying this dialogue system through a crowd-sourced study, we show that users indeed entrain on a “strategy-level” without the change of strategy impinging on their creativity. Our work thus presents a promising future research direction for developing dialogue management systems that can strategically influence people’s descriptive strategy to ease the system’s language understanding in creative tasks.

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MMChat: Multi-Modal Chat Dataset on Social Media
Yinhe Zheng | Guanyi Chen | Xin Liu | Jian Sun

Incorporating multi-modal contexts in conversation is an important step for developing more engaging dialogue systems. In this work, we explore this direction by introducing MMChat: a large scale Chinese multi-modal dialogue corpus (32.4M raw dialogues and 120.84K filtered dialogues). Unlike previous corpora that are crowd-sourced or collected from fictitious movies, MMChat contains image-grounded dialogues collected from real conversations on social media, in which the sparsity issue is observed. Specifically, image-initiated dialogues in common communications may deviate to some non-image-grounded topics as the conversation proceeds. To better investigate this issue, we manually annotate 100K dialogues from MMChat and further filter the corpus accordingly, which yields MMChat-hf. We develop a benchmark model to address the sparsity issue in dialogue generation tasks by adapting the attention routing mechanism on image features. Experiments demonstrate the usefulness of incorporating image features and the effectiveness in handling the sparsity of image features.

There has been a growing interest in developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations. Compared to the traditional recommendation, it advocates wealthier interactions and provides possibilities to obtain users’ exact preferences explicitly. Nevertheless, the corresponding research on this topic is limited due to the lack of broad-coverage dialogue corpus, especially real-world dialogue corpus. To handle this issue and facilitate our exploration, we construct E-ConvRec, an authentic Chinese dialogue dataset consisting of over 25k dialogues and 770k utterances, which contains user profile, product knowledge base (KB), and multiple sequential real conversations between users and recommenders. Next, we explore conversational recommendation in a real scene from multiple facets based on the dataset. Therefore, we particularly design three tasks: user preference recognition, dialogue management, and personalized recommendation. In the light of the three tasks, we establish baseline results on E-ConvRec to facilitate future studies.

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SHONGLAP: A Large Bengali Open-Domain Dialogue Corpus
Syed Mostofa Monsur | Sakib Chowdhury | Md Shahrar Fatemi | Shafayat Ahmed

We introduce SHONGLAP, a large annotated open-domain dialogue corpus in Bengali language. Due to unavailability of high-quality dialogue datasets for low-resource languages like Bengali, existing neural open-domain dialogue systems suffer from data scarcity. We propose a framework to prepare large-scale open-domain dialogue datasets from publicly available multi-party discussion podcasts, talk-shows and label them based on weak-supervision techniques which is particularly suitable for low-resource settings. Using this framework, we prepared our corpus, the first reported Bengali open-domain dialogue corpus (7.7k+ fully annotated dialogues in total) which can serve as a strong baseline for future works. Experimental results show that our corpus improves performance of large language models (BanglaBERT) in case of downstream classification tasks during fine-tuning.

Praising behavior is considered to an important method of communication in daily life and social activities. An engineering analysis of praising behavior is therefore valuable. However, a dialogue corpus for this analysis has not yet been developed. Therefore, we develop corpuses for face-to-face and remote two-party dialogues with ratings of praising skills. The corpuses enable us to clarify how to use verbal and nonverbal behaviors for successfully praise. In this paper, we analyze the differences between the face-to-face and remote corpuses, in particular the expressions in adjudged praising scenes in both corpuses, and also evaluated praising skills. We also compare differences in head motion, gaze behavior, facial expression in high-rated praising scenes in both corpuses. The results showed that the distribution of praising scores was similar in face-to-face and remote dialogues, although the ratio of the number of praising scenes to the number of utterances was different. In addition, we confirmed differences in praising behavior in face-to-face and remote dialogues.

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Comparing Approaches to Language Understanding for Human-Robot Dialogue: An Error Taxonomy and Analysis
Ada Tur | David Traum

In this paper, we compare two different approaches to language understanding for a human-robot interaction domain in which a human commander gives navigation instructions to a robot. We contrast a relevance-based classifier with a GPT-2 model, using about 2000 input-output examples as training data. With this level of training data, the relevance-based model outperforms the GPT-2 based model 79% to 8%. We also present a taxonomy of types of errors made by each model, indicating that they have somewhat different strengths and weaknesses, so we also examine the potential for a combined model.

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SPORTSINTERVIEW: A Large-Scale Sports Interview Benchmark for Entity-centric Dialogues
Hanfei Sun | Ziyuan Cao | Diyi Yang

We propose a novel knowledge grounded dialogue (interview) dataset SPORTSINTERVIEW set in the domain of sports interview. Our dataset contains two types of external knowledge sources as knowledge grounding, and is rich in content, containing about 150K interview sessions and 34K distinct interviewees. Compared to existing knowledge grounded dialogue datasets, our interview dataset is larger in size, comprises natural dialogues revolving around real-world sports matches, and have more than one dimension of external knowledge linking. We performed several experiments on SPORTSINTERVIEW and found that models such as BART fine-tuned on our dataset are able to learn lots of relevant domain knowledge and generate meaningful sentences (questions or responses). However, their performance is still far from humans (by comparing to gold sentences in the dataset) and hence encourages future research utilizing SPORTSINTERVIEW.

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EmoInHindi: A Multi-label Emotion and Intensity Annotated Dataset in Hindi for Emotion Recognition in Dialogues
Gopendra Vikram Singh | Priyanshu Priya | Mauajama Firdaus | Asif Ekbal | Pushpak Bhattacharyya

The long-standing goal of Artificial Intelligence (AI) has been to create human-like conversational systems. Such systems should have the ability to develop an emotional connection with the users, consequently, emotion recognition in dialogues has gained popularity. Emotion detection in dialogues is a challenging task because humans usually convey multiple emotions with varying degrees of intensities in a single utterance. Moreover, emotion in an utterance of a dialogue may be dependent on previous utterances making the task more complex. Recently, emotion recognition in low-resource languages like Hindi has been in great demand. However, most of the existing datasets for multi-label emotion and intensity detection in conversations are in English. To this end, we propose a large conversational dataset in Hindi named EmoInHindi for multi-label emotion and intensity recognition in conversations containing 1,814 dialogues with a total of 44,247 utterances. We prepare our dataset in a Wizard-of-Oz manner for mental health and legal counselling of crime victims. Each utterance of dialogue is annotated with one or more emotion categories from 16 emotion labels including neutral and their corresponding intensity. We further propose strong contextual baselines that can detect the emotion(s) and corresponding emotional intensity of an utterance given the conversational context.

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The Project Dialogism Novel Corpus: A Dataset for Quotation Attribution in Literary Texts
Krishnapriya Vishnubhotla | Adam Hammond | Graeme Hirst

We present the Project Dialogism Novel Corpus, or PDNC, an annotated dataset of quotations for English literary texts. PDNC contains annotations for 35,978 quotations across 22 full-length novels, and is by an order of magnitude the largest corpus of its kind. Each quotation is annotated for the speaker, addressees, type of quotation, referring expression, and character mentions within the quotation text. The annotated attributes allow for a comprehensive evaluation of models of quotation attribution and coreference for literary texts.

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Who’s in, who’s out? Predicting the Inclusiveness or Exclusiveness of Personal Pronouns in Parliamentary Debates
Ines Rehbein | Josef Ruppenhofer

This paper presents a compositional annotation scheme to capture the clusivity properties of personal pronouns in context, that is their ability to construct and manage in-groups and out-groups by including/excluding the audience and/or non-speech act participants in reference to groups that also include the speaker. We apply and test our schema on pronoun instances in speeches taken from the German parliament. The speeches cover a time period from 2017-2021 and comprise manual annotations for 3,126 sentences. We achieve high inter-annotator agreement for our new schema, with a Cohen’s κ in the range of 89.7-93.2 and a percentage agreement of > 96%. Our exploratory analysis of in/exclusive pronoun use in the parliamentary setting provides some face validity for our new schema. Finally, we present baseline experiments for automatically predicting clusivity in political debates, with promising results for many referential constellations, yielding an overall 84.9% micro F1 for all pronouns.

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A Language Modelling Approach to Quality Assessment of OCR’ed Historical Text
Callum Booth | Robert Shoemaker | Robert Gaizauskas

We hypothesise and evaluate a language model-based approach for scoring the quality of OCR transcriptions in the British Library Newspapers (BLN) corpus parts 1 and 2, to identify the best quality OCR for use in further natural language processing tasks, with a wider view to link individual newspaper reports of crime in nineteenth-century London to the Digital Panopticon—a structured repository of criminal lives. We mitigate the absence of gold standard transcriptions of the BLN corpus by utilising a corpus of genre-adjacent texts that capture the common and legal parlance of nineteenth-century London—the Proceedings of the Old Bailey Online—with a view to rank the BLN transcriptions by their OCR quality.

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Identifying Copied Fragments in a 18th Century Dutch Chronicle
Roser Morante | Eleanor L. T. Smith | Lianne Wilhelmus | Alie Lassche | Erika Kuijpers

We apply computational stylometric techniques to an 18th century Dutch chronicle to determine which fragments of the manuscript represent the author’s own original work and which show signs of external source use through either direct copying or paraphrasing. Through stylometric methods the majority of text fragments in the chronicle can be correctly labelled as either the author’s own words, direct copies from sources or paraphrasing. Our results show that clustering text fragments based on stylometric measures is an effective methodology for authorship verification of this document; however, this approach is less effective when personal writing style is masked by author independent styles or when applied to paraphrased text.

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A Study of Distant Viewing of ukiyo-e prints
Konstantina Liagkou | John Pavlopoulos | Ewa Machotka

This paper contributes to studying relationships between Japanese topography and places featured in early modern landscape prints, so-called ukiyo-e or ‘pictures of the floating world’. The printed inscriptions on these images feature diverse place-names, both man-made and natural formations. However, due to the corpus’s richness and diversity, the precise nature of artistic mediation of the depicted places remains little understood. In this paper, we explored a new analytical approach based on the macroanalysis of images facilitated by Natural Language Processing technologies. This paper presents a small dataset with inscriptions on prints that have been annotated by an art historian for included place-name entities. Our dataset is released for public use. By fine-tuning and applying a Japanese BERT-based Name Entity Recogniser, we provide a use-case of a macroanalysis of a visual dataset that is hosted by the digital database of the Art Research Center at the Ritsumeikan University, Kyoto. Our work studies the relationship between topography and its visual renderings in early modern Japanese ukiyo-e landscape prints, demonstrating how an art historian’s work can be improved with Natural Language Processing toward distant viewing of visual datasets. We release our dataset and code for public use: https://github.com/connalia/ukiyo-e_meisho_nlp

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CCTAA: A Reproducible Corpus for Chinese Authorship Attribution Research
Haining Wang | Allen Riddell

Authorship attribution infers the likely author of an unsigned, single-authored document from a pool of candidates. Despite recent advances, a lack of standard, reproducible testbeds for Chinese language documents impedes progress. In this paper, we present the Chinese Cross-Topic Authorship Attribution (CCTAA) corpus. It is the first standard testbed for authorship attribution on contemporary Chinese prose. The cross-topic design and relatively inflexible genre of newswire contribute to an appropriate level of difficulty. It supports reproducible research by using pre-defined data splits. We show that a sequence classifier based on pre-trained Chinese RoBERTa embedding and a support vector machine classifier using function character n-gram frequency features perform below expectations on this task. The code for generating the corpus and reproducing the baselines is freely available at https://codeberg.org/haining/cctaa.

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An automatic model and Gold Standard for translation alignment of Ancient Greek
Tariq Yousef | Chiara Palladino | Farnoosh Shamsian | Anise d’Orange Ferreira | Michel Ferreira dos Reis

This paper illustrates a workflow for developing and evaluating automatic translation alignment models for Ancient Greek. We designed an annotation Style Guide and a gold standard for the alignment of Ancient Greek-English and Ancient Greek-Portuguese, measured inter-annotator agreement and used the resulting dataset to evaluate the performance of various translation alignment models. We proposed a fine-tuning strategy that employs unsupervised training with mono- and bilingual texts and supervised training using manually aligned sentences. The results indicate that the fine-tuned model based on XLM-Roberta is superior in performance, and it achieved good results on language pairs that were not part of the training data.

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Rhetorical Structure Approach for Online Deception Detection: A Survey
Francielle Vargas | Jonas D‘Alessandro | Zohar Rabinovich | Fabrício Benevenuto | Thiago Pardo

Most information is passed on in the form of language. Therefore, research on how people use language to inform and misinform, and how this knowledge may be automatically extracted from large amounts of text is surely relevant. This survey provides first-hand experiences and a comprehensive review of rhetorical-level structure analysis for online deception detection. We systematically analyze how discourse structure, aligned or not with other approaches, is applied to automatic fake news and fake reviews detection on the web and social media. Moreover, we categorize discourse-tagged corpora along with results, hence offering a summary and accessible introductions to new researchers.

Providing feedback on the argumentation of the learner is essential for developing critical thinking skills, however, it requires a lot of time and effort. To mitigate the overload on teachers, we aim to automate a process of providing feedback, especially giving diagnostic comments which point out the weaknesses inherent in the argumentation. It is recommended to give specific diagnostic comments so that learners can recognize the diagnosis without misinterpretation. However, it is not obvious how the task of providing specific diagnostic comments should be formulated. We present a formulation of the task as template selection and slot filling to make an automatic evaluation easier and the behavior of the model more tractable. The key to the formulation is the possibility of creating a template set that is sufficient for practical use. In this paper, we define three criteria that a template set should satisfy: expressiveness, informativeness, and uniqueness, and verify the feasibility of creating a template set that satisfies these criteria as a first trial. We will show that it is feasible through an annotation study that converts diagnostic comments given in a text to a template format. The corpus used in the annotation study is publicly available.

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Towards Speaker Verification for Crowdsourced Speech Collections
John Mendonca | Rui Correia | Mariana Lourenço | João Freitas | Isabel Trancoso

Crowdsourcing the collection of speech provides a scalable setting to access a customisable demographic according to each dataset’s needs. The correctness of speaker metadata is especially relevant for speaker-centred collections - ones that require the collection of a fixed amount of data per speaker. This paper identifies two different types of misalignment present in these collections: Multiple Accounts misalignment (different contributors map to the same speaker), and Multiple Speakers misalignment (multiple speakers map to the same contributor). Based on state-of-the-art approaches to Speaker Verification, this paper proposes an unsupervised method for measuring speaker metadata plausibility of a collection, i.e., evaluating the match (or lack thereof) between contributors and speakers. The solution presented is composed of an embedding extractor and a clustering module. Results indicate high precision in automatically classifying contributor alignment (>0.94).

Abstract Meaning Representation is a sentence-level meaning representation, which abstracts the meaning of sentences into a rooted acyclic directed graph. With the continuous expansion of Chinese AMR corpus, more and more scholars have developed parsing systems to automatically parse sentences into Chinese AMR. However, the current parsers can’t deal with concept alignment and relation alignment, let alone the evaluation methods for AMR parsing. Therefore, to make up for the vacancy of Chinese AMR parsing evaluation methods, based on AMR evaluation metric smatch, we have improved the algorithm of generating triples so that to make it compatible with concept alignment and relation alignment. Finally, we obtain a new integrity metric align-smatch for paring evaluation. A comparative research then was conducted on 20 manually annotated AMR and gold AMR, with the result that align-smatch works well in alignments and more robust in evaluating arcs. We also put forward some fine-grained metric for evaluating concept alignment, relation alignment and implicit concepts, in order to further measure parsers’ performance in subtasks.

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Dynamic Human Evaluation for Relative Model Comparisons
Thórhildur Thorleiksdóttir | Cedric Renggli | Nora Hollenstein | Ce Zhang

Collecting human judgements is currently the most reliable evaluation method for natural language generation systems. Automatic metrics have reported flaws when applied to measure quality aspects of generated text and have been shown to correlate poorly with human judgements. However, human evaluation is time and cost-intensive, and we lack consensus on designing and conducting human evaluation experiments. Thus there is a need for streamlined approaches for efficient collection of human judgements when evaluating natural language generation systems. Therefore, we present a dynamic approach to measure the required number of human annotations when evaluating generated outputs in relative comparison settings. We propose an agent-based framework of human evaluation to assess multiple labelling strategies and methods to decide the better model in a simulation and a crowdsourcing case study. The main results indicate that a decision about the superior model can be made with high probability across different labelling strategies, where assigning a single random worker per task requires the least overall labelling effort and thus the least cost.

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Please, Don’t Forget the Difference and the Confidence Interval when Seeking for the State-of-the-Art Status
Yves Bestgen

This paper argues for the widest possible use of bootstrap confidence intervals for comparing NLP system performances instead of the state-of-the-art status (SOTA) and statistical significance testing. Their main benefits are to draw attention to the difference in performance between two systems and to help assessing the degree of superiority of one system over another. Two cases studies, one comparing several systems and the other based on a K-fold cross-validation procedure, illustrate these benefits.

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PCR4ALL: A Comprehensive Evaluation Benchmark for Pronoun Coreference Resolution in English
Xinran Zhao | Hongming Zhang | Yangqiu Song

Pronoun Coreference Resolution (PCR) is the task of resolving pronominal expressions to all mentions they refer to. The correct resolution of pronouns typically involves the complex inference over both linguistic knowledge and general world knowledge. Recently, with the help of pre-trained language representation models, the community has made significant progress on various PCR tasks. However, as most existing works focus on developing PCR models for specific datasets and measuring the accuracy or F1 alone, it is still unclear whether current PCR systems are reliable in real applications. Motivated by this, we propose PCR4ALL, a new benchmark and a toolbox that evaluates and analyzes the performance of PCR systems from different perspectives (i.e., knowledge source, domain, data size, frequency, relevance, and polarity). Experiments demonstrate notable performance differences when the models are examined from different angles. We hope that PCR4ALL can motivate the community to pay more attention to solving the overall PCR problem and understand the performance comprehensively. All data and codes are available at: https://github.com/HKUST-KnowComp/PCR4ALL.

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Estimating Confidence of Predictions of Individual Classifiers and TheirEnsembles for the Genre Classification Task
Mikhail Lepekhin | Serge Sharoff

Genre identification is a kind of non-topic text classification. The main difference between this task and topic classification is that genre, unlike topic, usually cannot be expressed just by some keywords and is defined as a functional space. Neural models based on pre-trained transformers, such as BERT or XLM-RoBERTa, demonstrate SOTA results in many NLP tasks, including non-topical classification. However, in many cases, their downstream application to very large corpora, such as those extracted from social media, can lead to unreliable results because of dataset shifts, when some raw texts do not match the profile of the training set. To mitigate this problem, we experiment with individual models as well as with their ensembles. To evaluate the robustness of all models we use a prediction confidence metric, which estimates the reliability of a prediction in the absence of a gold standard label. We can evaluate robustness via the confidence gap between the correctly classified texts and the misclassified ones on a labeled test corpus, higher gaps make it easier to identify whether a text is classified correctly. Our results show that for all of the classifiers tested in this study, there is a confidence gap, but for the ensembles, the gap is wider, meaning that ensembles are more robust than their individual models.

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What do we really know about State of the Art NER?
Sowmya Vajjala | Ramya Balasubramaniam

Named Entity Recognition (NER) is a well researched NLP task and is widely used in real world NLP scenarios. NER research typically focuses on the creation of new ways of training NER, with relatively less emphasis on resources and evaluation. Further, state of the art (SOTA) NER models, trained on standard datasets, typically report only a single performance measure (F-score) and we don’t really know how well they do for different entity types and genres of text, or how robust are they to new, unseen entities. In this paper, we perform a broad evaluation of NER using a popular dataset, that takes into consideration various text genres and sources constituting the dataset at hand. Additionally, we generate six new adversarial test sets through small perturbations in the original test set, replacing select entities while retaining the context. We also train and test our models on randomly generated train/dev/test splits followed by an experiment where the models are trained on a select set of genres but tested genres not seen in training. These comprehensive evaluation strategies were performed using three SOTA NER models. Based on our results, we recommend some useful reporting practices for NER researchers, that could help in providing a better understanding of a SOTA model’s performance in future.

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ProQE: Proficiency-wise Quality Estimation dataset for Grammatical Error Correction
Yujin Takahashi | Masahiro Kaneko | Masato Mita | Mamoru Komachi

This study investigates how supervised quality estimation (QE) models of grammatical error correction (GEC) are affected by the learners’ proficiency with the data. QE models for GEC evaluations in prior work have obtained a high correlation with manual evaluations. However, when functioning in a real-world context, the data used for the reported results have limitations because prior works were biased toward data by learners with relatively high proficiency levels. To address this issue, we created a QE dataset that includes multiple proficiency levels and explored the necessity of performing proficiency-wise evaluation for QE of GEC. Our experiments demonstrated that differences in evaluation dataset proficiency affect the performance of QE models, and proficiency-wise evaluation helps create more robust models.

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Evaluation of Off-the-shelf Speech Recognizers on Different Accents in a Dialogue Domain
Divya Tadimeti | Kallirroi Georgila | David Traum

We evaluate several publicly available off-the-shelf (commercial and research) automatic speech recognition (ASR) systems on dialogue agent-directed English speech from speakers with General American vs. non-American accents. Our results show that the performance of the ASR systems for non-American accents is considerably worse than for General American accents. Depending on the recognizer, the absolute difference in performance between General American accents and all non-American accents combined can vary approximately from 2% to 12%, with relative differences varying approximately between 16% and 49%. This drop in performance becomes even larger when we consider specific categories of non-American accents indicating a need for more diligent collection of and training on non-native English speaker data in order to narrow this performance gap. There are performance differences across ASR systems, and while the same general pattern holds, with more errors for non-American accents, there are some accents for which the best recognizer is different than in the overall case. We expect these results to be useful for dialogue system designers in developing more robust inclusive dialogue systems, and for ASR providers in taking into account performance requirements for different accents.

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Sentence Pair Embeddings Based Evaluation Metric for Abstractive and Extractive Summarization
Ramya Akula | Ivan Garibay

The development of an automatic evaluation metric remains an open problem in text generation. Widely used evaluation metrics, like ROUGE and BLEU, are based on exact word matching and fail to capture semantic similarity. Recent works, such as BERTScore, MoverScore and, Sentence Mover’s Similarity, are an improvement over these standard metrics as they use the contextualized word or sentence embeddings to capture semantic similarity. We in this work, propose a novel evaluation metric, Sentence Pair EmbEDdings (SPEED) Score, for text generation which is based on semantic similarity between sentence pairs as opposed to earlier approaches. To find semantic similarity between a pair of sentences, we obtain sentence-level embeddings from multiple transformer models pre-trained specifically on various sentence pair tasks such as Paraphrase Detection (PD), Semantic Text Similarity (STS), and Natural Language Inference (NLI). As these sentence pair tasks involve capturing the semantic similarity between a pair of input texts, we leverage these models in our metric computation. Our proposed evaluation metric shows an impressive performance in evaluating both abstractive and extractive summarization models and achieves state-of-the-art results on the SummEval dataset, demonstrating the effectiveness of our approach. Also, we perform the run-time analysis to show that our proposed metric is faster than the current state-of-the-art.

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On “Human Parity” and “Super Human Performance” in Machine Translation Evaluation
Thierry Poibeau

In this paper, we reassess claims of human parity and super human performance in machine translation. Although these terms have already been discussed, as well as the evaluation protocols used to achieved these conclusions (human-parity is achieved i) only for a very reduced number of languages, ii) on very specific types of documents and iii) with very literal translations), we show that the terms used are themselves problematic, and that human translation involves much more than what is embedded in automatic systems. We also discuss ethical issues related to the way results are presented and advertised. Finally, we claim that a better assessment of human capacities should be put forward and that the goal of replacing humans by machines is not a desirable one.

Due to the success of pre-trained language models, versions of languages other than English have been released in recent years. This fact implies the need for resources to evaluate these models. In the case of Spanish, there are few ways to systematically assess the models’ quality. In this paper, we narrow the gap by building two evaluation benchmarks. Inspired by previous work (Conneau and Kiela, 2018; Chen et al., 2019), we introduce Spanish SentEval and Spanish DiscoEval, aiming to assess the capabilities of stand-alone and discourse-aware sentence representations, respectively. Our benchmarks include considerable pre-existing and newly constructed datasets that address different tasks from various domains. In addition, we evaluate and analyze the most recent pre-trained Spanish language models to exhibit their capabilities and limitations. As an example, we discover that for the case of discourse evaluation tasks, mBERT, a language model trained on multiple languages, usually provides a richer latent representation than models trained only with documents in Spanish. We hope our contribution will motivate a fairer, more comparable, and less cumbersome way to evaluate future Spanish language models.

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UMUTextStats: A linguistic feature extraction tool for Spanish
José Antonio García-Díaz | Pedro José Vivancos-Vicente | Ángela Almela | Rafael Valencia-García

Feature Engineering consists in the application of domain knowledge to select and transform relevant features to build efficient machine learning models. In the Natural Language Processing field, the state of the art concerning automatic document classification tasks relies on word and sentence embeddings built upon deep learning models based on transformers that have outperformed the competition in several tasks. However, the models built from these embeddings are usually difficult to interpret. On the contrary, linguistic features are easy to understand, they result in simpler models, and they usually achieve encouraging results. Moreover, both linguistic features and embeddings can be combined with different strategies which result in more reliable machine-learning models. The de facto tool for extracting linguistic features in Spanish is LIWC. However, this software does not consider specific linguistic phenomena of Spanish such as grammatical gender and lacks certain verb tenses. In order to solve these drawbacks, we have developed UMUTextStats, a linguistic extraction tool designed from scratch for Spanish. Furthermore, this tool has been validated to conduct different experiments in areas such as infodemiology, hate-speech detection, author profiling, authorship verification, humour or irony detection, among others. The results indicate that the combination of linguistic features and embeddings based on transformers are beneficial in automatic document classification.

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Problem-solving Recognition in Scientific Text
Kevin Heffernan | Simone Teufel

As far back as Aristotle, problems and solutions have been recognised as a core pattern of thought, and in particular of the scientific method. In this work, we present the novel task of problem-solving recognition in scientific text. Previous work on problem-solving either is not computational, is not adapted to scientific text, or has been narrow in scope. This work provides a new annotation scheme of problem-solving tailored to the scientific domain. We validate the scheme with an annotation study, and model the task using state-of-the-art baselines such as a Neural Relational Topic Model. The agreement study indicates that our annotation is reliable, and results from modelling show that problem-solving expressions in text can be recognised to a high degree of accuracy.

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HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method
Yuxiang Zhang | Hayato Yamana

Multiple-choice question answering (MCQA) for machine reading comprehension (MRC) is challenging. It requires a model to select a correct answer from several candidate options related to text passages or dialogue. To select the correct answer, such models must have the ability to understand natural languages, comprehend textual representations, and infer the relationship between candidate options, questions, and passages. Previous models calculated representations between passages and question-option pairs separately, thereby ignoring the effect of other relation-pairs. In this study, we propose a human reading comprehension attention (HRCA) model and a passage-question-option (PQO) matrix-guided HRCA model called HRCA+ to increase accuracy. The HRCA model updates the information learned from the previous relation-pair to the next relation-pair. HRCA+ utilizes the textual information and the interior relationship between every two parts in a passage, a question, and the corresponding candidate options. Our proposed method outperforms other state-of-the-art methods. On the Semeval-2018 Task 11 dataset, our proposed method improved accuracy levels from 95.8% to 97.2%, and on the DREAM dataset, it improved accuracy levels from 90.4% to 91.6% without extra training data, from 91.8% to 92.6% with extra training data.

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HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings
Maulik Parmar | Apurva Narayan

Hypernymy plays a fundamental role in many AI tasks like taxonomy learning, ontology learning, etc. This has motivated the development of many automatic identification methods for extracting this relation, most of which rely on word distribution. We present a novel model HyperBox to learn box embeddings for hypernym discovery. Given an input term, HyperBox retrieves its suitable hypernym from a target corpus. For this task, we use the dataset published for SemEval 2018 Shared Task on Hypernym Discovery. We compare the performance of our model on two specific domains of knowledge: medical and music. Experimentally, we show that our model outperforms existing methods on the majority of the evaluation metrics. Moreover, our model generalize well over unseen hypernymy pairs using only a small set of training data.

Space situational awareness typically makes use of physical measurements from radar, telescopes, and other assets to monitor satellites and other spacecraft for operational, navigational, and defense purposes. In this work we explore using textual input for the space situational awareness task. We construct a corpus of 48.5k news articles spanning all known active satellites between 2009 and 2020. Using a dependency-rule-based extraction system designed to target three high-impact events – spacecraft launches, failures, and decommissionings, we identify 1,787 space-event sentences that are then annotated by humans with 15.9k labels for event slots. We empirically demonstrate a state-of-the-art neural extraction system achieves an overall F1 between 53 and 91 per slot for event extraction in this low-resource, high-impact domain.

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PerCQA: Persian Community Question Answering Dataset
Naghme Jamali | Yadollah Yaghoobzadeh | Heshaam Faili

Community Question Answering (CQA) forums provide answers to many real-life questions. These forums are trendy among machine learning researchers due to their large size. Automatic answer selection, answer ranking, question retrieval, expert finding, and fact-checking are example learning tasks performed using CQA data. This paper presents PerCQA, the first Persian dataset for CQA. This dataset contains the questions and answers crawled from the most well-known Persian forum. After data acquisition, we provide rigorous annotation guidelines in an iterative process and then the annotation of question-answer pairs in SemEvalCQA format. PerCQA contains 989 questions and 21,915 annotated answers. We make PerCQA publicly available to encourage more research in Persian CQA. We also build strong benchmarks for the task of answer selection in PerCQA by using mono- and multi-lingual pre-trained language models.

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GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns
Piyawat Lertvittayakumjorn | Leshem Choshen | Eyal Shnarch | Francesca Toni

Data exploration is an important step of every data science and machine learning project, including those involving textual data. We provide a novel language tool, in the form of a publicly available Python library for extracting patterns from textual data. The library integrates a first public implementation of the existing GrASP algorithm. It allows users to extract patterns using a number of general-purpose built-in linguistic attributes (such as hypernyms, part-of-speech tags, and syntactic dependency tags), as envisaged for the original algorithm, as well as domain-specific custom attributes which can be incorporated into the library by implementing two functions. The library is equipped with a web-based interface empowering human users to conveniently explore data via the extracted patterns, using complementary pattern-centric and example-centric views: the former includes a reading in natural language and statistics of each extracted pattern; the latter shows applications of each extracted pattern to training examples. We demonstrate the usefulness of the library in classification (spam detection and argument mining), model analysis (machine translation), and artifact discovery in datasets (SNLI and 20Newsgroups).

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Recurrent Neural Networks with Mixed Hierarchical Structures and EM Algorithm for Natural Language Processing
Zhaoxin Luo | Michael Zhu

How to obtain hierarchical representations with an increasing level of abstraction becomes one of the key issues of learning with deep neural networks. A variety of RNN models have recently been proposed to incorporate both explicit and implicit hierarchical information in modeling languages in the literature. In this paper, we propose a novel approach called the latent indicator layer to identify and learn implicit hierarchical information (e.g., phrases), and further develop an EM algorithm to handle the latent indicator layer in training. The latent indicator layer further simplifies a text’s hierarchical structure, which allows us to seamlessly integrate different levels of attention mechanisms into the structure. We called the resulting architecture as the EM-HRNN model. Furthermore, we develop two bootstrap strategies to effectively and efficiently train the EM-HRNN model on long text documents. Simulation studies and real data applications demonstrate that the EM-HRNN model with bootstrap training outperforms other RNN-based models in document classification tasks. The performance of the EM-HRNN model is comparable to a Transformer-based method called Bert-base, though the former is much smaller model and does not require pre-training.

Existing question answering systems mainly focus on dealing with text data. However, much of the data produced daily is stored in the form of tables that can be found in documents and relational databases, or on the web. To solve the task of question answering over tables, there exist many datasets for table question answering written in English, but few Korean datasets. In this paper, we demonstrate how we construct Korean-specific datasets for table question answering: Korean tabular dataset is a collection of 1.4M tables with corresponding descriptions for unsupervised pre-training language models. Korean table question answering corpus consists of 70k pairs of questions and answers created by crowd-sourced workers. Subsequently, we then build a pre-trained language model based on Transformer and fine-tune the model for table question answering with these datasets. We then report the evaluation results of our model. We make our datasets publicly available via our GitHub repository and hope that those datasets will help further studies for question answering over tables, and for the transformation of table formats.

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GerCCT: An Annotated Corpus for Mining Arguments in German Tweets on Climate Change
Robin Schaefer | Manfred Stede

While the field of argument mining has grown notably in the last decade, research on the Twitter medium remains relatively understudied. Given the difficulty of mining arguments in tweets, recent work on creating annotated resources mainly utilized simplified annotation schemes that focus on single argument components, i.e., on claim or evidence. In this paper we strive to fill this research gap by presenting GerCCT, a new corpus of German tweets on climate change, which was annotated for a set of different argument components and properties. Additionally, we labelled sarcasm and toxic language to facilitate the development of tools for filtering out non-argumentative content. This, to the best of our knowledge, renders our corpus the first tweet resource annotated for argumentation, sarcasm and toxic language. We show that a comparatively complex annotation scheme can still yield promising inter-annotator agreement. We further present first good supervised classification results yielded by a fine-tuned BERT architecture.

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Budget Argument Mining Dataset Using Japanese Minutes from the National Diet and Local Assemblies
Yasutomo Kimura | Hokuto Ototake | Minoru Sasaki

Budget argument mining attempts to identify argumentative components related to a budget item, and then classifies these argumentative components, given budget information and minutes. We describe the construction of the dataset for budget argument mining, a subtask of QA Lab-PoliInfo-3 in NTCIR-16. Budget argument mining analyses the argument structure of the minutes, focusing on monetary expressions (amount of money). In this task, given sufficient budget information (budget item, budget amount, etc.), relevant argumentative components in the minutes are identified and argument labels (claim, premise, and other) are assigned their components. In this paper, we describe the design of the data format, the annotation procedure, and release information of budget argument mining dataset, to link budget information to minutes.

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Context-based Virtual Adversarial Training for Text Classification with Noisy Labels
Do-Myoung Lee | Yeachan Kim | Chang gyun Seo

Deep neural networks (DNNs) have a high capacity to completely memorize noisy labels given sufficient training time, and its memorization unfortunately leads to performance degradation. Recently, virtual adversarial training (VAT) attracts attention as it could further improve the generalization of DNNs in semi-supervised learning. The driving force behind VAT is to prevent the models from overffiting to data points by enforcing consistency between the inputs and the perturbed inputs. These strategy could be helpful in learning from noisy labels if it prevents neural models from learning noisy samples while encouraging the models to generalize clean samples. In this paper, we propose context-based virtual adversarial training (ConVAT) to prevent a text classifier from overfitting to noisy labels. Unlike the previous works, the proposed method performs the adversarial training in the context level rather than the inputs. It makes the classifier not only learn its label but also its contextual neighbors, which alleviate the learning from noisy labels by preserving contextual semantics on each data point. We conduct extensive experiments on four text classification datasets with two types of label noises. Comprehensive experimental results clearly show that the proposed method works quite well even with extremely noisy settings.

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FinMath: Injecting a Tree-structured Solver for Question Answering over Financial Reports
Chenying Li | Wenbo Ye | Yilun Zhao

Answering questions over financial reports containing both tabular and textual data (hybrid data) is challenging as it requires models to select information from financial reports and perform complex quantitative analyses. Although current models have demonstrated a solid capability to solve simple questions, they struggle with complex questions that require a multiple-step numerical reasoning process. This paper proposes a new framework named FinMath, which improves the model’s numerical reasoning capacity by injecting a tree-structured neural model to perform multi-step numerical reasoning. Specifically, FinMath extracts supporting evidence from the financial reports given the question in the first phase. In the second phase, a tree-structured neural model is applied to generate a tree expression in a top-down recursive way. Experiments on the TAT-QA dataset show that our proposed approach improves the previous best result by 8.5% absolute for Exact Match (EM) score (50.1% to 58.6%) and 6.1% absolute for numeracy-focused F1 score (58.0% to 64.1%).

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HeadlineCause: A Dataset of News Headlines for Detecting Causalities
Ilya Gusev | Alexey Tikhonov

Detecting implicit causal relations in texts is a task that requires both common sense and world knowledge. Existing datasets are focused either on commonsense causal reasoning or explicit causal relations. In this work, we present HeadlineCause, a dataset for detecting implicit causal relations between pairs of news headlines. The dataset includes over 5000 headline pairs from English news and over 9000 headline pairs from Russian news labeled through crowdsourcing. The pairs vary from totally unrelated or belonging to the same general topic to the ones including causation and refutation relations. We also present a set of models and experiments that demonstrates the dataset validity, including a multilingual XLM-RoBERTa based model for causality detection and a GPT-2 based model for possible effects prediction.

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Incorporating Zoning Information into Argument Mining from Biomedical Literature
Boyang Liu | Viktor Schlegel | Riza Batista-Navarro | Sophia Ananiadou

The goal of text zoning is to segment a text into zones (i.e., Background, Conclusion) that serve distinct functions. Argumentative zoning, a specific text zoning scheme for the scientific domain, is considered as the antecedent for argument mining by many researchers. Surprisingly, however, little work is concerned with exploiting zoning information to improve the performance of argument mining models, despite the relatedness of the two tasks. In this paper, we propose two transformer-based models to incorporate zoning information into argumentative component identification and classification tasks. One model is for the sentence-level argument mining task and the other is for the token-level task. In particular, we add the zoning labels predicted by an off-the-shelf model to the beginning of each sentence, inspired by the convention commonly used biomedical abstracts. Moreover, we employ multi-head attention to transfer the sentence-level zoning information to each token in a sentence. Based on experiment results, we find a significant improvement in F1-scores for both sentence- and token-level tasks. It is worth mentioning that these zoning labels can be obtained with high accuracy by utilising readily available automated methods. Thus, existing argument mining models can be improved by incorporating zoning information without any additional annotation cost.

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MAKED: Multi-lingual Automatic Keyword Extraction Dataset
Yash Verma | Anubhav Jangra | Sriparna Saha | Adam Jatowt | Dwaipayan Roy

Keyword extraction is an integral task for many downstream problems like clustering, recommendation, search and classification. Development and evaluation of keyword extraction techniques require an exhaustive dataset; however, currently, the community lacks large-scale multi-lingual datasets. In this paper, we present MAKED, a large-scale multi-lingual keyword extraction dataset comprising of 540K+ news articles from British Broadcasting Corporation News (BBC News) spanning 20 languages. It is the first keyword extraction dataset for 11 of these 20 languages. The quality of the dataset is examined by experimentation with several baselines. We believe that the proposed dataset will help advance the field of automatic keyword extraction given its size, diversity in terms of languages used, topics covered and time periods as well as its focus on under-studied languages.

While deep learning approaches to information extraction have had many successes, they can be difficult to augment or maintain as needs shift. Rule-based methods, on the other hand, can be more easily modified. However, crafting rules requires expertise in linguistics and the domain of interest, making it infeasible for most users. Here we attempt to combine the advantages of these two directions while mitigating their drawbacks. We adapt recent advances from the adjacent field of program synthesis to information extraction, synthesizing rules from provided examples. We use a transformer-based architecture to guide an enumerative search, and show that this reduces the number of steps that need to be explored before a rule is found. Further, we show that without training the synthesis algorithm on the specific domain, our synthesized rules achieve state-of-the-art performance on the 1-shot scenario of a task that focuses on few-shot learning for relation classification, and competitive performance in the 5-shot scenario.

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Enhancing Relation Extraction via Adversarial Multi-task Learning
Han Qin | Yuanhe Tian | Yan Song

Relation extraction (RE) is a sub-field of information extraction, which aims to extract the relation between two given named entities (NEs) in a sentence and thus requires a good understanding of contextual information, especially the entities and their surrounding texts. However, limited attention is paid by most existing studies to re-modeling the given NEs and thus lead to inferior RE results when NEs are sometimes ambiguous. In this paper, we propose a RE model with two training stages, where adversarial multi-task learning is applied to the first training stage to explicitly recover the given NEs so as to enhance the main relation extractor, which is trained alone in the second stage. In doing so, the RE model is optimized by named entity recognition (NER) and thus obtains a detailed understanding of entity-aware context. We further propose the adversarial mechanism to enhance the process, which controls the effect of NER on the main relation extractor and allows the extractor to benefit from NER while keep focusing on RE rather than the entire multi-task learning. Experimental results on two English benchmark datasets for RE demonstrate the effectiveness of our approach, where state-of-the-art performance is observed on both datasets.

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Query Obfuscation by Semantic Decomposition
Danushka Bollegala | Tomoya Machide | Ken-ichi Kawarabayashi

We propose a method to protect the privacy of search engine users by decomposing the queries using semantically related and unrelated distractor terms. Instead of a single query, the search engine receives multiple decomposed query terms. Next, we reconstruct the search results relevant to the original query term by aggregating the search results retrieved for the decomposed query terms. We show that the word embeddings learnt using a distributed representation learning method can be used to find semantically related and distractor query terms. We derive the relationship between the obfuscity achieved through the proposed query anonymisation method and the reconstructability of the original search results using the decomposed queries. We analytically study the risk of discovering the search engine users’ information intents under the proposed query obfuscation method, and empirically evaluate its robustness against clustering-based attacks. Our experimental results show that the proposed method can accurately reconstruct the search results for user queries, without compromising the privacy of the search engine users.

Foodborne illness is a serious but preventable public health problem – with delays in detecting the associated outbreaks resulting in productivity loss, expensive recalls, public safety hazards, and even loss of life. While social media is a promising source for identifying unreported foodborne illnesses, there is a dearth of labeled datasets for developing effective outbreak detection models. To accelerate the development of machine learning-based models for foodborne outbreak detection, we thus present TWEET-FID (TWEET-Foodborne Illness Detection), the first publicly available annotated dataset for multiple foodborne illness incident detection tasks. TWEET-FID collected from Twitter is annotated with three facets: tweet class, entity type, and slot type, with labels produced by experts as well as by crowdsource workers. We introduce several domain tasks leveraging these three facets: text relevance classification (TRC), entity mention detection (EMD), and slot filling (SF). We describe the end-to-end methodology for dataset design, creation, and labeling for supporting model development for these tasks. A comprehensive set of results for these tasks leveraging state-of-the-art single-and multi-task deep learning methods on the TWEET-FID dataset are provided. This dataset opens opportunities for future research in foodborne outbreak detection.

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Named Entity Recognition to Detect Criminal Texts on the Web
Paweł Skórzewski | Mikołaj Pieniowski | Grazyna Demenko

This paper presents a toolkit that applies named-entity extraction techniques to identify information related to criminal activity in texts from the Polish Internet. The methodological and technical assumptions were established following the requirements of our application users from the Border Guard. Due to the specificity of the users’ needs and the specificity of web texts, we used original methodologies related to the search for desired texts, the creation of domain lexicons, the annotation of the collected text resources, and the combination of rule-based and machine-learning techniques for extracting the information desired by the user. The performance of our tools has been evaluated on 6240 manually annotated text fragments collected from Internet sources. Evaluation results and user feedback show that our approach is feasible and has potential value for real-life applications in the daily work of border guards. Lexical lookup combined with hand-crafted rules and regular expressions, supported by text statistics, can make a decent specialized entity recognition system in the absence of large data sets required for training a good neural network.

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Task-Driven and Experience-Based Question Answering Corpus for In-Home Robot Application in the House3D Virtual Environment
Zhuoqun Xu | Liubo Ouyang | Yang Liu

At present, more and more work has begun to pay attention to the long-term housekeeping robot scene. Naturally, we wonder whether the robot can answer the questions raised by the owner according to the actual situation at home. These questions usually do not have a clear text context, are directly related to the actual scene, and it is difficult to find the answer from the general knowledge base (such as Wikipedia). Therefore, the experience accumulated from the task seems to be a more natural choice. We present a corpus called TEQA (task-driven and experience-based question answering) in the long-term household task. Based on a popular in-house virtual environment (AI2-THOR) and agent task experiences of ALFRED, we design six types of questions along with answering including 24 question templates, 37 answer templates, and nearly 10k different question answering pairs. Our corpus aims at investigating the ability of task experience understanding of agents for the daily question answering scenario on the ALFRED dataset.

We describe the language technology (LT) assessments carried out in the ELRC action (European Language Resource Coordination) of the European Commission, which aims towards minimising language barriers across the EU. We zoom in on the two most extensive assessments. These LT specifications do not only involve experiments with tools and techniques but also an extensive consultation round with stakeholders from public organisations, academia and industry, in order to gather insights into scenarios and best practices. The LT specifications concern (1) the field of automated anonymisation, which is motivated by the need of public and other organisations to be able to store and share data, and (2) the field of multilingual fake news processing, which is motivated by the increasingly pressing problem of disinformation and the limited language coverage of systems for automatically detecting misleading articles. For each specification, we set up a corresponding proof-of-concept software to demonstrate the opportunities and challenges involved in the field.

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RadQA: A Question Answering Dataset to Improve Comprehension of Radiology Reports
Sarvesh Soni | Meghana Gudala | Atieh Pajouhi | Kirk Roberts

We present a radiology question answering dataset, RadQA, with 3074 questions posed against radiology reports and annotated with their corresponding answer spans (resulting in a total of 6148 question-answer evidence pairs) by physicians. The questions are manually created using the clinical referral section of the reports that take into account the actual information needs of ordering physicians and eliminate bias from seeing the answer context (and, further, organically create unanswerable questions). The answer spans are marked within the Findings and Impressions sections of a report. The dataset aims to satisfy the complex clinical requirements by including complete (yet concise) answer phrases (which are not just entities) that can span multiple lines. We conduct a thorough analysis of the proposed dataset by examining the broad categories of disagreement in annotation (providing insights on the errors made by humans) and the reasoning requirements to answer a question (uncovering the huge dependence on medical knowledge for answering the questions). The advanced transformer language models achieve the best F1 score of 63.55 on the test set, however, the best human performance is 90.31 (with an average of 84.52). This demonstrates the challenging nature of RadQA that leaves ample scope for future method research.

In the commercial aviation domain, there are a large number of documents, like accident reports of NTSB and ASRS, and regulatory directives ADs. There is a need for a system to efficiently access these diverse repositories to serve the demands of the aviation industry, such as maintenance, compliance, and safety. In this paper, we propose a Knowledge Graph (KG) guided Deep Learning (DL) based Question Answering (QA) system to cater to these requirements. We construct a KG from aircraft accident reports and contribute this resource to the community of researchers. The efficacy of this resource is tested and proved by the proposed QA system. Questions in Natural Language are converted into SPARQL (the interface language of the RDF graph database) queries and are answered from the KG. On the DL side, we examine two different QA models, BERT-QA and GPT3-QA, covering the two paradigms of answer formulation in QA. We evaluate our system on a set of handcrafted queries curated from the accident reports. Our hybrid KG + DL QA system, KGQA + BERT-QA, achieves 7% and 40.3% increase in accuracy over KGQA and BERT-QA systems respectively. Similarly, the other combined system, KGQA + GPT3-QA, achieves 29.3% and 9.3% increase in accuracy over KGQA and GPT3-QA systems respectively. Thus, we infer that the combination of KG and DL is better than either KG or DL individually for QA, at least in our chosen domain.

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A Bayesian Topic Model for Human-Evaluated Interpretability
Justin Wood | Corey Arnold | Wei Wang

One desiderata of topic modeling is to produce interpretable topics. Given a cluster of document-tokens comprising a topic, we can order the topic by counting each word. It is natural to think that each topic could easily be labeled by looking at the words with the highest word count. However, this is not always the case. A human evaluator can often have difficulty identifying a single label that accurately describes the topic as many top words seem unrelated. This paper aims to improve interpretability in topic modeling by providing a novel, outperforming interpretable topic model Our approach combines two previously established subdomains in topic modeling: nonparametric and weakly-supervised topic models. Given a nonparametric topic model, we can include weakly-supervised input using novel modifications to the nonparametric generative model. These modifications lay the groundwork for a compelling setting—one in which most corpora, without any previous supervised or weakly-supervised input, can discover interpretable topics. This setting also presents various challenging sub-problems of which we provide resolutions. Combining nonparametric topic models with weakly-supervised topic models leads to an exciting discovery—a complete, self-contained and outperforming topic model for interpretability.

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A Large Interlinked Knowledge Graph of the Italian Cultural Heritage
Stefano Faralli | Andrea Lenzi | Paola Velardi

Knowledge is the lifeblood for a plethora of applications such as search, recommender systems and natural language understanding. Thanks to the efforts in the fields of Semantic Web and Linked Open Data a growing number of interlinked knowledge bases are supporting the development of advanced knowledge-based applications. Unfortunately, for a large number of domain-specific applications, these knowledge bases are unavailable. In this paper, we present a resource consisting of a large knowledge graph linking the Italian cultural heritage entities (defined in the ArCo ontology) with the concepts defined on well-known knowledge bases (i.e., DBpedia and the Getty GVP ontology). We describe the methodologies adopted for the semi-automatic resource creation and provide an in-depth analysis of the resulting interlinked graph.

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Training on Lexical Resources
Kenneth Church | Xingyu Cai | Yuchen Bian

We propose using lexical resources (thesaurus, VAD) to fine-tune pretrained deep nets such as BERT and ERNIE. Then at inference time, these nets can be used to distinguish synonyms from antonyms, as well as VAD distances. The inference method can be applied to words as well as texts such as multiword expressions (MWEs), out of vocabulary words (OOVs), morphological variants and more. Code and data are posted on https://github.com/kwchurch/syn_ant.

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Challenging the Assumption of Structure-based embeddings in Few- and Zero-shot Knowledge Graph Completion
Filip Cornell | Chenda Zhang | Jussi Karlgren | Sarunas Girdzijauskas

In this paper, we report experiments on Few- and Zero-shot Knowledge Graph completion, where the objective is to add missing relational links between entities into an existing Knowledge Graph with few or no previous examples of the relation in question. While previous work has used pre-trained embeddings based on the structure of the graph as input for a neural network, nobody has, to the best of our knowledge, addressed the task by only using textual descriptive data associated with the entities and relations, much since current standard benchmark data sets lack such information. We therefore enrich the benchmark data sets for these tasks by collecting textual description data to provide a new resource for future research to bridge the gap between structural and textual Knowledge Graph completion. Our results show that we can improve the results for Knowledge Graph completion for both Few- and Zero-shot scenarios with up to a two-fold increase of all metrics in the Zero-shot setting. From a more general perspective, our experiments demonstrate the value of using textual resources to enrich more formal representations of human knowledge and in the utility of transfer learning from textual data and text collections to enrich and maintain knowledge resources.

Consolidated access to current and reliable terms from different subject fields and languages is necessary for content creators and translators. Terminology is also needed in AI applications such as machine translation, speech recognition, information extraction, and other natural language processing tools. In this work, we facilitate standards-based sharing and management of terminology resources by providing an open terminology management solution - the EuroTermBank Toolkit. It allows organisations to manage and search their terms, create term collections, and share them within and outside the organisation by participating in the network of federated databases. The data curated in the federated databases are automatically shared with EuroTermBank, the largest multilingual terminology resource in Europe, allowing translators and language service providers as well as researchers and students to access terminology resources in their most current version.

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RELATE: Generating a linguistically inspired Knowledge Graph for fine-grained emotion classification
Annika Marie Schoene | Nina Dethlefs | Sophia Ananiadou

Several existing resources are available for sentiment analysis (SA) tasks that are used for learning sentiment specific embedding (SSE) representations. These resources are either large, common-sense knowledge graphs (KG) that cover a limited amount of polarities/emotions or they are smaller in size (e.g.: lexicons), which require costly human annotation and cover fine-grained emotions. Therefore using knowledge resources to learn SSE representations is either limited by the low coverage of polarities/emotions or the overall size of a resource. In this paper, we first introduce a new directed KG called ‘RELATE’, which is built to overcome both the issue of low coverage of emotions and the issue of scalability. RELATE is the first KG of its size to cover Ekman’s six basic emotions that are directed towards entities. It is based on linguistic rules to incorporate the benefit of semantics without relying on costly human annotation. The performance of ‘RELATE’ is evaluated by learning SSE representations using a Graph Convolutional Neural Network (GCN).

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Language technology practitioners as language managers: arbitrating data bias and predictive bias in ASR
Nina Markl | Stephen Joseph McNulty

Despite the fact that variation is a fundamental characteristic of natural language, automatic speech recognition systems perform systematically worse on non-standardised and marginalised language varieties. In this paper we use the lens of language policy to analyse how current practices in training and testing ASR systems in industry lead to the data bias giving rise to these systematic error differences. We believe that this is a useful perspective for speech and language technology practitioners to understand the origins and harms of algorithmic bias, and how they can mitigate it. We also propose a re-framing of language resources as (public) infrastructure which should not solely be designed for markets, but for, and with meaningful cooperation of, speech communities.

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Masader: Metadata Sourcing for Arabic Text and Speech Data Resources
Zaid Alyafeai | Maraim Masoud | Mustafa Ghaleb | Maged S. Al-shaibani

The NLP pipeline has evolved dramatically in the last few years. The first step in the pipeline is to find suitable annotated datasets to evaluate the tasks we are trying to solve. Unfortunately, most of the published datasets lack metadata annotations that describe their attributes. Not to mention, the absence of a public catalogue that indexes all the publicly available datasets related to specific regions or languages. When we consider low-resource dialectical languages, for example, this issue becomes more prominent. In this paper, we create Masader, the largest public catalogue for Arabic NLP datasets, which consists of 200 datasets annotated with 25 attributes. Furthermore, we develop a metadata annotation strategy that could be extended to other languages. We also make remarks and highlight some issues about the current status of Arabic NLP datasets and suggest recommendations to address them.

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Linghub2: Language Resource Discovery Tool for Language Technologies
Cécile Robin | Gautham Vadakkekara Suresh | Víctor Rodriguez-Doncel | John P. McCrae | Paul Buitelaar

Language resources are a key component of natural language processing and related research and applications. Users of language resources have different needs in terms of format, language, topics, etc. for the data they need to use. Linghub (McCrae and Cimiano, 2015) was first developed for this purpose, using the capabilities of linked data to represent metadata, and tackling the heterogeneous metadata issue. Linghub aimed at helping language resources and technology users to easily find and retrieve relevant data, and identify important information on access, topics, etc. This work describes a rejuvenation and modernisation of the 2015 platform into using a popular open source data management system, DSpace, as foundation. The new platform, Linghub2, contains updated and extended resources, more languages offered, and continues the work towards homogenisation of metadata through conversions, through linkage to standardisation strategies and community groups, such as the Open Digital Rights Language (ODRL) community group.

Constructions are direct form-meaning pairs with possible schematic slots. These slots are simultaneously constrained by the embedded construction itself and the sentential context. We propose that the constraint could be described by a conditional probability distribution. However, as this conditional probability is inevitably complex, we utilize language models to capture this distribution. Therefore, we build CxLM, a deep learning-based masked language model explicitly tuned to constructions’ schematic slots. We first compile a construction dataset consisting of over ten thousand constructions in Taiwan Mandarin. Next, an experiment is conducted on the dataset to examine to what extent a pretrained masked language model is aware of the constructions. We then fine-tune the model specifically to perform a cloze task on the opening slots. We find that the fine-tuned model predicts masked slots more accurately than baselines and generates both structurally and semantically plausible word samples. Finally, we release CxLM and its dataset as publicly available resources and hope to serve as new quantitative tools in studying construction grammar.

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The Lexometer: A Shiny Application for Exploratory Analysis and Visualization of Corpus Data
Oufan Hai | Matthew Sundberg | Katherine Trice | Rebecca Friedman | Scott Grimm

Often performing even simple data science tasks with corpus data requires significant expertise in data science and programming languages like R and Python. With the aim of making quantitative research more accessible for researchers in the language sciences, we present the Lexometer, a Shiny application that integrates numerous data analysis and visualization functions into an easy-to-use graphical user interface. Some functions of the Lexometer are: filtering large databases to generate subsets of the data and variables of interest, providing a range of graphing techniques for both single and multiple variable analysis, and providing the data in a table format which can further be filtered as well as provide methods for cleaning the data. The Lexometer aims to be useful to language researchers with differing levels of programming expertise and to aid in broadening the inclusion of corpus-based empirical evidence in the language sciences.

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TallVocabL2Fi: A Tall Dataset of 15 Finnish L2 Learners’ Vocabulary
Frankie Robertson | Li-Hsin Chang | Sini Söyrinki

Previous work concerning measurement of second language learners has tended to focus on the knowledge of small numbers of words, often geared towards measuring vocabulary size. This paper presents a “tall” dataset containing information about a few learners’ knowledge of many words, suitable for evaluating Vocabulary Inventory Prediction (VIP) techniques, including those based on Computerised Adaptive Testing (CAT). In comparison to previous comparable datasets, the learners are from varied backgrounds, so as to reduce the risk of overfitting when used for machine learning based VIP. The dataset contains both a self-rating test and a translation test, used to derive a measure of reliability for learner responses. The dataset creation process is documented, and the relationship between variables concerning the participants, such as their completion time, their language ability level, and the triangulated reliability of their self-assessment responses, are analysed. The word list is constructed by taking into account the extensive derivation morphology of Finnish, and infrequent words are included in order to account for explanatory variables beyond word frequency.

The social NLP researchers and mental health practitioners have witnessed exponential growth in the field of mental health detection and analysis on social media. It has become important to identify the reason behind mental illness. In this context, we introduce a new dataset for Causal Analysis of Mental health in Social media posts (CAMS). We first introduce the annotation schema for this task of causal analysis. The causal analysis comprises of two types of annotations, viz, causal interpretation and causal categorization. We show the efficacy of our scheme in two ways: (i) crawling and annotating 3155 Reddit data and (ii) re-annotate the publicly available SDCNL dataset of 1896 instances for interpretable causal analysis. We further combine them as CAMS dataset and make it available along with the other source codes https://anonymous.4open.science/r/CAMS1/. Our experimental results show that the hybrid CNN-LSTM model gives the best performance over CAMS dataset.

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How Does the Experimental Setting Affect the Conclusions of Neural Encoding Models?
Xiaohan Zhang | Shaonan Wang | Chengqing Zong

Recent years have witnessed the tendency of neural encoding models on exploring brain language processing using naturalistic stimuli. Neural encoding models are data-driven methods that require an encoding model to investigate the mystery of brain mechanisms hidden in the data. As a data-driven method, the performance of encoding models is very sensitive to the experimental setting. However, it is unknown how the experimental setting further affects the conclusions of neural encoding models. This paper systematically investigated this problem and evaluated the influence of three experimental settings, i.e., the data size, the cross-validation training method, and the statistical testing method. Results demonstrate that inappropriate cross-validation training and small data size can substantially decrease the performance of encoding models, especially in the temporal lobe and the frontal lobe. And different null hypotheses in significance testing lead to highly different significant brain regions. Based on these results, we suggest a block-wise cross-validation training method and an adequate data size for increasing the performance of linear encoding models. We also propose two strict null hypotheses to control false positive discovery rates.

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SPADE: A Big Five-Mturk Dataset of Argumentative Speech Enriched with Socio-Demographics for Personality Detection
Elma Kerz | Yu Qiao | Sourabh Zanwar | Daniel Wiechmann

In recent years, there has been increasing interest in automatic personality detection based on language. Progress in this area is highly contingent upon the availability of datasets and benchmark corpora. However, publicly available datasets for modeling and predicting personality traits are still scarce. While recent efforts to create such datasets from social media (Twitter, Reddit) are to be applauded, they often do not include continuous and contextualized language use. In this paper, we introduce SPADE, the first dataset with continuous samples of argumentative speech labeled with the Big Five personality traits and enriched with socio-demographic data (age, gender, education level, language background). We provide benchmark models for this dataset to facilitate further research and conduct extensive experiments. Our models leverage 436 (psycho)linguistic features extracted from transcribed speech and speaker-level metainformation with transformers. We conduct feature ablation experiments to investigate which types of features contribute to the prediction of individual personality traits.

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Progress in Multilingual Speech Recognition for Low Resource Languages Kurmanji Kurdish, Cree and Inuktut
Vishwa Gupta | Gilles Boulianne

This contribution presents our efforts to develop the automatic speech recognition (ASR) systems for three low resource languages: Kurmanji Kurdish, Cree and Inuktut. As a first step, we generate multilingual models from acoustic training data from 12 different languages in the hybrid DNN/HMM framework. We explore different strategies for combining the phones from different languages: either keep the phone labels separate for each language or merge the common phones. For Kurmanji Kurdish and Inuktut, keeping the phones separate gives much lower word error rate (WER), while merging phones gives lower WER for Cree. These WER are lower than training the acoustic models separately for each language. We also compare two different DNN architectures: factored time delay neural network (TDNN-F), and bidirectional long short-term memory (BLSTM) acoustic models. The TDNN-F acoustic models give significantly lower WER for Kurmanji Kurdish and Cree, while BLSTM acoustic models give significantly lower WER for Inuktut. We also show that for each language, training multilingual acoustic models by one more epoch with acoustic data from that language reduces the WER significantly. We also added 512-dimensional embedding features from cross-lingual pre-trained wav2vec2.0 XLSR-53 models, but they lead to only a small reduction in WER.

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Efficient Entity Candidate Generation for Low-Resource Languages
Alberto Garcia-Duran | Akhil Arora | Robert West

Candidate generation is a crucial module in entity linking. It also plays a key role in multiple NLP tasks that have been proven to beneficially leverage knowledge bases. Nevertheless, it has often been overlooked in the monolingual English entity linking literature, as naïve approaches obtain very good performance. Unfortunately, the existing approaches for English cannot be successfully transferred to poorly resourced languages. This paper constitutes an in-depth analysis of the candidate generation problem in the context of cross-lingual entity linking with a focus on low-resource languages. Among other contributions, we point out limitations in the evaluation conducted in previous works. We introduce a characterization of queries into types based on their difficulty, which improves the interpretability of the performance of different methods. We also propose a light-weight and simple solution based on the construction of indexes whose design is motivated by more complex transfer learning based neural approaches. A thorough empirical analysis on 9 real-world datasets under 2 evaluation settings shows that our simple solution outperforms the state-of-the-art approach in terms of both quality and efficiency for almost all datasets and query types.

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What a Creole Wants, What a Creole Needs
Heather Lent | Kelechi Ogueji | Miryam de Lhoneux | Orevaoghene Ahia | Anders Søgaard

In recent years, the natural language processing (NLP) community has given increased attention to the disparity of efforts directed towards high-resource languages over low-resource ones. Efforts to remedy this delta often begin with translations of existing English datasets into other languages. However, this approach ignores that different language communities have different needs. We consider a group of low-resource languages, creole languages. Creoles are both largely absent from the NLP literature, and also often ignored by society at large due to stigma, despite these languages having sizable and vibrant communities. We demonstrate, through conversations with creole experts and surveys of creole-speaking communities, how the things needed from language technology can change dramatically from one language to another, even when the languages are considered to be very similar to each other, as with creoles. We discuss the prominent themes arising from these conversations, and ultimately demonstrate that useful language technology cannot be built without involving the relevant community.

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Extensions to Brahmic script processing within the Nisaba library: new scripts, languages and utilities
Alexander Gutkin | Cibu Johny | Raiomond Doctor | Lawrence Wolf-Sonkin | Brian Roark

The Brahmic family of scripts is used to record some of the most spoken languages in the world and is arguably the most diverse family of writing systems. In this work, we present several substantial extensions to Brahmic script functionality within the open-source Nisaba library of finite-state script normalization and processing utilities (Johny et al., 2021). First, we extend coverage from the original ten scripts to an additional ten scripts of South Asia and beyond, including some used to record endangered languages such as Dogri. Second, we augment the language layer so that scripts used by multiple languages in distinct ways can be processed correctly for more languages, such as the Bengali script when used for the low-resource language Santali. We document key changes to the finite-state engine required to support these new languages and scripts. Finally, we add new script processing utilities, including lightweight script-level reading normalization that (unlike existing visual normalization) does not preserve visual invariance, and a fixed-input transliteration mechanism specifically tailored to Brahmic text entry with ASCII characters.

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Predicting Embedding Reliability in Low-Resource Settings Using Corpus Similarity Measures
Jonathan Dunn | Haipeng Li | Damian Sastre

This paper simulates a low-resource setting across 17 languages in order to evaluate embedding similarity, stability, and reliability under different conditions. The goal is to use corpus similarity measures before training to predict properties of embeddings after training. The main contribution of the paper is to show that it is possible to predict downstream embedding similarity using upstream corpus similarity measures. This finding is then applied to low-resource settings by modelling the reliability of embeddings created from very limited training data. Results show that it is possible to estimate the reliability of low-resource embeddings using corpus similarity measures that remain robust on small amounts of data. These findings have significant implications for the evaluation of truly low-resource languages in which such systematic downstream validation methods are not possible because of data limitations.

Multi-modal Machine Translation (MMT) enables the use of visual information to enhance the quality of translations, especially where the full context is not available to enable the unambiguous translation in standard machine translation. Despite the increasing popularity of such technique, it lacks sufficient and qualitative datasets to maximize the full extent of its potential. Hausa, a Chadic language, is a member of the Afro-Asiatic language family. It is estimated that about 100 to 150 million people speak the language, with more than 80 million indigenous speakers. This is more than any of the other Chadic languages. Despite the large number of speakers, the Hausa language is considered as a low resource language in natural language processing (NLP). This is due to the absence of enough resources to implement most of the tasks in NLP. While some datasets exist, they are either scarce, machine-generated or in the religious domain. Therefore, there is the need to create training and evaluation data for implementing machine learning tasks and bridging the research gap in the language. This work presents the Hausa Visual Genome (HaVG), a dataset that contains the description of an image or a section within the image in Hausa and its equivalent in English. The dataset was prepared by automatically translating the English description of the images in the Hindi Visual Genome (HVG). The synthetic Hausa data was then carefully postedited, taking into cognizance the respective images. The data is made of 32,923 images and their descriptions that are divided into training, development, test, and challenge test set. The Hausa Visual Genome is the first dataset of its kind and can be used for Hausa-English machine translation, multi-modal research, image description, among various other natural language processing and generation tasks.

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A Survey of Machine Translation Tasks on Nigerian Languages
Ebelechukwu Nwafor | Anietie Andy

Machine translation is an active area of research that has received a significant amount of attention over the past decade. With the advent of deep learning models, the translation of several languages has been performed with high accuracy and precision. In spite of the development in machine translation techniques, there is very limited work focused on translating low-resource African languages, particularly Nigerian languages. Nigeria is one of the most populous countries in Africa with diverse language and ethnic groups. In this paper, we survey the current state of the art of machine translation research on Nigerian languages with a major emphasis on neural machine translation techniques. We outline the limitations of research in machine translation on Nigerian languages and propose future directions in increasing research and participation.

Automatic speech recognition (ASR) on low resource languages improves the access of linguistic minorities to technological advantages provided by artificial intelligence (AI). In this paper, we address the problem of data scarcity for the Hong Kong Cantonese language by creating a new Cantonese dataset. Our dataset, Multi-Domain Cantonese Corpus (MDCC), consists of 73.6 hours of clean read speech paired with transcripts, collected from Cantonese audiobooks from Hong Kong. It comprises philosophy, politics, education, culture, lifestyle and family domains, covering a wide range of topics. We also review all existing Cantonese datasets and analyze them according to their speech type, data source, total size and availability. We further conduct experiments with Fairseq S2T Transformer, a state-of-the-art ASR model, on the biggest existing dataset, Common Voice zh-HK, and our proposed MDCC, and the results show the effectiveness of our dataset. In addition, we create a powerful and robust Cantonese ASR model by applying multi-dataset learning on MDCC and Common Voice zh-HK.

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Survey on Thai NLP Language Resources and Tools
Ratchakrit Arreerard | Stephen Mander | Scott Piao

Over the past decades, Natural Language Processing (NLP) research has been expanding to cover more languages. Recently particularly, NLP community has paid increasing attention to under-resourced languages. However, there are still many languages for which NLP research is limited in terms of both language resources and software tools. Thai language is one of the under-resourced languages in the NLP domain, although it is spoken by nearly 70 million people globally. In this paper, we report on our survey on the past development of Thai NLP research to help understand its current state and future research directions. Our survey shows that, although Thai NLP community has achieved a significant achievement over the past three decades, particularly on NLP upstream tasks such as tokenisation, research on downstream tasks such as syntactic parsing and semantic analysis is still limited. But we foresee that Thai NLP research will advance rapidly as richer Thai language resources and more robust NLP techniques become available.

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LaoPLM: Pre-trained Language Models for Lao
Nankai Lin | Yingwen Fu | Chuwei Chen | Ziyu Yang | Shengyi Jiang

Trained on the large corpus, pre-trained language models (PLMs) can capture different levels of concepts in context and hence generate universal language representations. They can benefit from multiple downstream natural language processing (NLP) tasks. Although PTMs have been widely used in most NLP applications, especially for high-resource languages such as English, it is under-represented in Lao NLP research. Previous work on Lao has been hampered by the lack of annotated datasets and the sparsity of language resources. In this work, we construct a text classification dataset to alleviate the resource-scarce situation of the Lao language. In addition, we present the first transformer-based PTMs for Lao with four versions: BERT-Small , BERT-Base , ELECTRA-Small , and ELECTRA-Base . Furthermore, we evaluate them on two downstream tasks: part-of-speech (POS) tagging and text classification. Experiments demonstrate the effectiveness of our Lao models. We release our models and datasets to the community, hoping to facilitate the future development of Lao NLP applications.

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The Maaloula Aramaic Speech Corpus (MASC): From Printed Material to a Lemmatized and Time-Aligned Corpus
Ghattas Eid | Esther Seyffarth | Ingo Plag

This paper presents the first electronic speech corpus of Maaloula Aramaic, an endangered Western Neo-Aramaic variety spoken in Syria. This 64,845-word corpus is available in four formats: (1) transcriptions, (2) lemmatized transcriptions, (3) audio files and time-aligned phonetic transcriptions, and (4) an SQLite database. The transcription files are a digitized and corrected version of authentic transcriptions of tape-recorded narratives coming from a fieldwork trip conducted in the 1980s and published in the early 1990s (Arnold, 1991a, 1991b). They contain no annotation, except for some informative tagging (e.g. to mark loanwords and misspoken words). In the lemmatized version of the files, each word form is followed by its lemma in angled brackets. The time-aligned TextGrid annotations consist of four tiers: the sentence level (Tier 1), the word level (Tiers 2 and 3), and the segment level (Tier 4). These TextGrid files are downloadable together with their audio files (for the original source of the audio data see Arnold, 2003). The SQLite database enables users to access the data on the level of tokens, types, lemmas, sentences, narratives, or speakers. The corpus is now available to the scientific community at https://doi.org/10.5281/zenodo.6496714.

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VIMQA: A Vietnamese Dataset for Advanced Reasoning and Explainable Multi-hop Question Answering
Khang Le | Hien Nguyen | Tung Le Thanh | Minh Nguyen

Vietnamese is the native language of over 98 million people in the world. However, existing Vietnamese Question Answering (QA) datasets do not explore the model’s ability to perform advanced reasoning and provide evidence to explain the answer. We introduce VIMQA, a new Vietnamese dataset with over 10,000 Wikipedia-based multi-hop question-answer pairs. The dataset is human-generated and has four main features: (1) The questions require advanced reasoning over multiple paragraphs. (2) Sentence-level supporting facts are provided, enabling the QA model to reason and explain the answer. (3) The dataset offers various types of reasoning to test the model’s ability to reason and extract relevant proof. (4) The dataset is in Vietnamese, a low-resource language. We also conduct experiments on our dataset using state-of-the-art Multilingual single-hop and multi-hop QA methods. The results suggest that our dataset is challenging for existing methods, and there is room for improvement in Vietnamese QA systems. In addition, we propose a general process for data creation and publish a framework for creating multilingual multi-hop QA datasets. The dataset and framework are publicly available to encourage further research in Vietnamese QA systems.

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Language Identification for Austronesian Languages
Jonathan Dunn | Wikke Nijhof

This paper provides language identification models for low- and under-resourced languages in the Pacific region with a focus on previously unavailable Austronesian languages. Accurate language identification is an important part of developing language resources. The approach taken in this paper combines 29 Austronesian languages with 171 non-Austronesian languages to create an evaluation set drawn from eight data sources. After evaluating six approaches to language identification, we find that a classifier based on skip-gram embeddings reaches a significantly higher performance than alternate methods. We then systematically increase the number of non-Austronesian languages in the model up to a total of 800 languages to evaluate whether an increased language inventory leads to less precise predictions for the Austronesian languages of interest. This evaluation finds that there is only a minimal impact on accuracy caused by increasing the inventory of non-Austronesian languages. Further experiments adapt these language identification models for code-switching detection, achieving high accuracy across all 29 languages.

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A Mapudüngun FST Morphological Analyser and its Web Interface
Andrés Chandía

This paper describes the development and evaluation of a FST-based analyser-generator for Mapudüngun language, which is publicly available through a web interface. As far as we know, it is the first system of this kind for Mapudüngun. Following the Mapuche grammar by Smeets, we have developed a machine including the morphological and phonological aspects of Mapudüngun. Through this computational approach we have produced a finite state morphological analyser-generator capable of classifying and appropriately tagging all the components (roots and suffixes) interacting in a Mapuche word-form. A double evaluation has been carried out showing a good level of reliability. In order to face the lack of standardization of the language, additional components (an enhanced analyser, a spelling unifier and a root guesser) have been integrated in the tool. The generated corpora, the lexicons and the FST grammars are available for further development and comparison results.

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Improving Large-scale Language Models and Resources for Filipino
Jan Christian Blaise Cruz | Charibeth Cheng

In this paper, we improve on existing language resources for the low-resource Filipino language in two ways. First, we outline the construction of the TLUnified dataset, a large-scale pretraining corpus that serves as an improvement over smaller existing pretraining datasets for the language in terms of scale and topic variety. Second, we pretrain new Transformer language models following the RoBERTa pretraining technique to supplant existing models trained with small corpora. Our new RoBERTa models show significant improvements over existing Filipino models in three benchmark datasets with an average gain of 4.47% test accuracy across three classification tasks with varying difficulty.

Thirumurai, also known as Panniru Thirumurai, is a collection of Tamil Shaivite poems dating back to the Hindu revival period between the 6th and the 10th century. These poems are par excellence, in both literary and musical terms. They have been composed based on the ancient, now non-existent Tamil Pann system and can be set to music. We present a large dataset containing all the Thirumurai poems and also attempt to classify the Pann and author of each poem using transformer based architectures. Our work is the first of its kind in dealing with ancient Tamil text datasets, which are severely under-resourced. We explore several Deep Learning-based techniques for solving this challenge effectively and provide essential insights into the problem and how to address it.

Bodo is a scheduled Indian language spoken largely by the Bodo community of Assam and other northeastern Indian states. Due to a lack of resources, it is difficult for young languages to communicate more effectively with the rest of the world. This leads to a lack of research in low-resource languages. The creation of a dataset is a tedious and costly process, particularly for languages with no participatory research. This is more visible for languages that are young and have recently adopted standard writing scripts. In this paper, we present a methodology using Google Keep for OCR to generate a monolingual Bodo corpus from different books. In this work, a Bodo text corpus of 192,327 tokens and 32,268 unique tokens is generated using free, accessible, and daily-usable applications. Moreover, some essential characteristics of the Bodo language are discussed that are neglected by Natural Language Progressing (NLP) researchers.

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AsNER - Annotated Dataset and Baseline for Assamese Named Entity recognition
Dhrubajyoti Pathak | Sukumar Nandi | Priyankoo Sarmah

We present the AsNER, a named entity annotation dataset for low resource Assamese language with a baseline Assamese NER model. The dataset contains about 99k tokens comprised of text from the speech of the Prime Minister of India and Assamese play. It also contains person names, location names and addresses. The proposed NER dataset is likely to be a significant resource for deep neural based Assamese language processing. We benchmark the dataset by training NER models and evaluating using state-of-the-art architectures for supervised named entity recognition (NER) such as Fasttext, BERT, XLM-R, FLAIR, MuRIL etc. We implement several baseline approaches with state-of-the-art sequence tagging Bi-LSTM-CRF architecture. The highest F1-score among all baselines achieves an accuracy of 80.69% when using MuRIL as a word embedding method. The annotated dataset and the top performing model are made publicly available.

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GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages
Fitsum Gaim | Wonsuk Yang | Jong C. Park

Language identification is one of the fundamental tasks in natural language processing that is a prerequisite to data processing and numerous applications. Low-resourced languages with similar typologies are generally confused with each other in real-world applications such as machine translation, affecting the user’s experience. In this work, we present a language identification dataset for five typologically and phylogenetically related low-resourced East African languages that use the Ge’ez script as a writing system; namely Amharic, Blin, Ge’ez, Tigre, and Tigrinya. The dataset is built automatically from selected data sources, but we also performed a manual evaluation to assess its quality. Our approach to constructing the dataset is cost-effective and applicable to other low-resource languages. We integrated the dataset into an existing language-identification tool and also fine-tuned several Transformer based language models, achieving very strong results in all cases. While the task of language identification is easy for the informed person, such datasets can make a difference in real-world deployments and also serve as part of a benchmark for language understanding in the target languages. The data and models are made available at https://github.com/fgaim/geezswitch.

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Handwritten Paleographic Greek Text Recognition: A Century-Based Approach
Paraskevi Platanou | John Pavlopoulos | Georgios Papaioannou

Today classicists are provided with a great number of digital tools which, in turn, offer possibilities for further study and new research goals. In this paper we explore the idea that old Greek handwriting can be machine-readable and consequently, researchers can study the target material fast and efficiently. Previous studies have shown that Handwritten Text Recognition (HTR) models are capable of attaining high accuracy rates. However, achieving high accuracy HTR results for Greek manuscripts is still considered to be a major challenge. The overall aim of this paper is to assess HTR for old Greek manuscripts. To address this statement, we study and use digitized images of the Oxford University Bodleian Library Greek manuscripts. By manually transcribing 77 images, we created and present here a new dataset for Handwritten Paleographic Greek Text Recognition. The dataset instances were organized by establishing as a leading factor the century to which the manuscript and hence the image belongs. Experimenting then with an HTR model we show that the error rate depends on the century of the image.

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Quality Control for Crowdsourced Bilingual Dictionary in Low-Resource Languages
Hiroki Chida | Yohei Murakami | Mondheera Pituxcoosuvarn

In conventional bilingual dictionary creation by using crowdsourcing, the main method is to ask multiple workers to translate the same words or sentences and take a majority vote. However, when this method is applied to the creation of bilingual dictionaries for low-resource languages with few speakers, many low-quality workers are expected to participate in the majority voting, which makes it difficult to maintain the quality of the evaluation by the majority voting. Therefore, we apply an effective aggregation method using a hyper question, which is a set of single questions, for quality control. Furthermore, to select high-quality workers, we design a task-allocation method based on the reliability of workers which is evaluated by their work results.

This paper presents a new inflectional resource for Gitksan, a low-resource Indigenous language of Canada. We use Gitksan data in interlinear glossed format, stemming from language documentation efforts, to build a database of partial inflection tables. We then enrich this morphological resource by filling in blank slots in the partial inflection tables using neural transformer reinflection models. We extend the training data for our transformer reinflection models using two data augmentation techniques: data hallucination and back-translation. Experimental results demonstrate substantial improvements from data augmentation, with data hallucination delivering particularly impressive gains. We also release reinflection models for Gitksan.

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PyCantonese: Cantonese Linguistics and NLP in Python
Jackson Lee | Litong Chen | Charles Lam | Chaak Ming Lau | Tsz-Him Tsui

This paper introduces PyCantonese, an open-source Python library for Cantonese linguistics and natural language processing. After the library design, implementation, corpus data format, and key datasets included are introduced, the paper provides an overview of the currently implemented functionality: stop words, handling Jyutping romanization, word segmentation, part-of-speech tagging, and parsing Cantonese text.

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Afaan Oromo Hate Speech Detection and Classification on Social Media
Teshome Mulugeta Ababu | Michael Melese Woldeyohannis

Hate and offensive speech on social media is targeted to attack an individual or group of community based on protected characteristics such as gender, ethnicity, and religion. Hate and offensive speech on social media is a global problem that suffers the community especially, for an under-resourced language like Afaan Oromo language. One of the most widely spoken Cushitic language families is Afaan Oromo. Our objective is to develop and test a model used to detect and classify Afaan Oromo hate speech on social media. We developed numerous models that were used to detect and classify Afaan Oromo hate speech on social media by using different machine learning algorithms (classical, ensemble, and deep learning) with the combination of different feature extraction techniques such as BOW, TF-IDF, word2vec, and Keras Embedding layers. To perform the task, we required Afaan Oromo datasets, but the datasets were unavailable. By concentrating on four thematic areas of hate speech, such as gender, religion, race, and offensive speech, we were able to collect a total of 12,812 posts and comments from Facebook. BiLSTM with pre-trained word2vec feature extraction is an outperformed algorithm that achieves better accuracy of 0.84 and 0.88 for eight classes and two classes, respectively.

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Cross-lingual Linking of Automatically Constructed Frames and FrameNet
Ryohei Sasano

A semantic frame is a conceptual structure describing an event, relation, or object along with its participants. Several semantic frame resources have been manually elaborated, and there has been much interest in the possibility of applying semantic frames designed for a particular language to other languages, which has led to the development of cross-lingual frame knowledge. However, manually developing such cross-lingual lexical resources is labor-intensive. To support the development of such resources, this paper presents an attempt at automatic cross-lingual linking of automatically constructed frames and manually crafted frames. Specifically, we link automatically constructed example-based Japanese frames to English FrameNet by using cross-lingual word embeddings and a two-stage model that first extracts candidate FrameNet frames for each Japanese frame by taking only the frame-evoking words into account, then finds the best alignment of frames by also taking frame elements into account. Experiments using frame-annotated sentences in Japanese FrameNet indicate that our approach will facilitate the manual development of cross-lingual frame resources.

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Aligning the Romanian Reference Treebank and the Valence Lexicon of Romanian Verbs
Ana-Maria Barbu | Verginica Barbu Mititelu | Cătălin Mititelu

We present here the efforts of aligning two language resources for Romanian: the Romanian Reference Treebank and the Valence Lexicon of Romanian Verbs: for each occurrence of those verbs in the treebank that were included as entries in the lexicon, a set of valence frames is automatically assigned, then manually validated by two linguists and, when necessary, corrected. Validating a valence frame also means semantically disambiguating the verb in the respective context. The validation is done by two linguists, on complementary datasets. However, a subset of verbs were validated by both annotators and Cohen’s κ is 0.87 for this subset. The alignment we have made also serves as a method of enhancing the quality of the two resources, as in the process we identify morpho-syntactic annotation mistakes, incomplete valence frames or missing ones. Information from each resource complements the information from the other, thus their value increases. The treebank and the lexicon are freely available, while the links discovered between them are also made available on GitHub.

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PortiLexicon-UD: a Portuguese Lexical Resource according to Universal Dependencies Model
Lucelene Lopes | Magali Duran | Paulo Fernandes | Thiago Pardo

This paper presents PortiLexicon-UD, a large and freely available lexicon for Portuguese delivering morphosyntactic information according to the Universal Dependencies model. This lexical resource includes part of speech tags, lemmas, and morphological information for words, with 1,221,218 entries (considering word duplication due to different combination of PoS tag, lemma, and morphological features). We report the lexicon creation process, its computational data structure, and its evaluation over an annotated corpus, showing that it has a high language coverage and good quality data.

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Extended Parallel Corpus for Amharic-English Machine Translation
Andargachew Mekonnen Gezmu | Andreas Nürnberger | Tesfaye Bayu Bati

This paper describes the acquisition, preprocessing, segmentation, and alignment of an Amharic-English parallel corpus. It will be helpful for machine translation of a low-resource language, Amharic. We freely released the corpus for research purposes. Furthermore, we developed baseline statistical and neural machine translation systems; we trained statistical and neural machine translation models using the corpus. In the experiments, we also used a large monolingual corpus for the language model of statistical machine translation and back-translation of neural machine translation. In the automatic evaluation, neural machine translation models outperform statistical machine translation models by approximately six to seven Bilingual Evaluation Understudy (BLEU) points. Besides, among the neural machine translation models, the subword models outperform the word-based models by three to four BLEU points. Moreover, two other relevant automatic evaluation metrics, Translation Edit Rate on Character Level and Better Evaluation as Ranking, reflect corresponding differences among the trained models.

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Low-resource Neural Machine Translation: Benchmarking State-of-the-art Transformer for Wolof<->French
Cheikh M. Bamba Dione | Alla Lo | Elhadji Mamadou Nguer | Sileye Ba

In this paper, we propose two neural machine translation (NMT) systems (French-to-Wolof and Wolof-to-French) based on sequence-to-sequence with attention and Transformer architectures. We trained our models on the parallel French-Wolof corpus (Nguer et al., 2020) of about 83k sentence pairs. Because of the low-resource setting, we experimented with advanced methods for handling data sparsity, including subword segmentation, backtranslation and the copied corpus method. We evaluate the models using BLEU score and find that the transformer outperforms the classic sequence-to-sequence model in all settings, in addition to being less sensitive to noise. In general, the best scores are achieved when training the models on subword-level based units. For such models, using backtranslation proves to be slightly beneficial in low-resource Wolof to high-resource French language translation for the transformer-based models. A slight improvement can also be observed when injecting copied monolingual text in the target language. Moreover, combining the copied method data with backtranslation leads to a slight improvement of the translation quality.

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Criteria for Useful Automatic Romanization in South Asian Languages
Isin Demirsahin | Cibu Johny | Alexander Gutkin | Brian Roark

This paper presents a number of possible criteria for systems that transliterate South Asian languages from their native scripts into the Latin script, a process known as romanization. These criteria are related to either fidelity to human linguistic behavior (pronunciation transparency, naturalness and conventionality) or processing utility for people (ease of input) as well as under-the-hood in systems (invertibility and stability across languages and scripts). When addressing these differing criteria several linguistic considerations, such as modeling of prominent phonological processes and their relation to orthography, need to be taken into account. We discuss these key linguistic details in the context of Brahmic scripts and languages that use them, such as Hindi and Malayalam. We then present the core features of several romanization algorithms, implemented in a finite state transducer (FST) formalism, that address differing criteria. Implementations of these algorithms have been released as part of the Nisaba finite-state script processing library.

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BERTology for Machine Translation: What BERT Knows about Linguistic Difficulties for Translation
Yuqian Dai | Marc de Kamps | Serge Sharoff

Pre-trained transformer-based models, such as BERT, have shown excellent performance in most natural language processing benchmark tests, but we still lack a good understanding of the linguistic knowledge of BERT in Neural Machine Translation (NMT). Our work uses syntactic probes and Quality Estimation (QE) models to analyze the performance of BERT’s syntactic dependencies and their impact on machine translation quality, exploring what kind of syntactic dependencies are difficult for NMT engines based on BERT. While our probing experiments confirm that pre-trained BERT “knows” about syntactic dependencies, its ability to recognize them often decreases after fine-tuning for NMT tasks. We also detect a relationship between syntactic dependencies in three languages and the quality of their translations, which shows which specific syntactic dependencies are likely to be a significant cause of low-quality translations.

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CVSS Corpus and Massively Multilingual Speech-to-Speech Translation
Ye Jia | Michelle Tadmor Ramanovich | Quan Wang | Heiga Zen

We introduce CVSS, a massively multilingual-to-English speech-to-speech translation (S2ST) corpus, covering sentence-level parallel S2ST pairs from 21 languages into English. CVSS is derived from the Common Voice speech corpus and the CoVoST 2 speech-to-text translation (ST) corpus, by synthesizing the translation text from CoVoST 2 into speech using state-of-the-art TTS systems. Two versions of translation speech in English are provided: 1) CVSS-C: All the translation speech is in a single high-quality canonical voice; 2) CVSS-T: The translation speech is in voices transferred from the corresponding source speech. In addition, CVSS provides normalized translation text which matches the pronunciation in the translation speech. On each version of CVSS, we built baseline multilingual direct S2ST models and cascade S2ST models, verifying the effectiveness of the corpus. To build strong cascade S2ST baselines, we trained an ST model on CoVoST 2, which outperforms the previous state-of-the-art trained on the corpus without extra data by 5.8 BLEU. Nevertheless, the performance of the direct S2ST models approaches the strong cascade baselines when trained from scratch, and with only 0.1 or 0.7 BLEU difference on ASR transcribed translation when initialized from matching ST models.

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JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus
Makoto Morishita | Katsuki Chousa | Jun Suzuki | Masaaki Nagata

Most current machine translation models are mainly trained with parallel corpora, and their translation accuracy largely depends on the quality and quantity of the corpora. Although there are billions of parallel sentences for a few language pairs, effectively dealing with most language pairs is difficult due to a lack of publicly available parallel corpora. This paper creates a large parallel corpus for English-Japanese, a language pair for which only limited resources are available, compared to such resource-rich languages as English-German. It introduces a new web-based English-Japanese parallel corpus named JParaCrawl v3.0. Our new corpus contains more than 21 million unique parallel sentence pairs, which is more than twice as many as the previous JParaCrawl v2.0 corpus. Through experiments, we empirically show how our new corpus boosts the accuracy of machine translation models on various domains. The JParaCrawl v3.0 corpus will eventually be publicly available online for research purposes.

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Learning How to Translate North Korean through South Korean
Hwichan Kim | Sangwhan Moon | Naoaki Okazaki | Mamoru Komachi

South and North Korea both use the Korean language. However, Korean NLP research has focused on South Korean only, and existing NLP systems of the Korean language, such as neural machine translation (NMT) models, cannot properly handle North Korean inputs. Training a model using North Korean data is the most straightforward approach to solving this problem, but there is insufficient data to train NMT models. In this study, we create data for North Korean NMT models using a comparable corpus. First, we manually create evaluation data for automatic alignment and machine translation, and then, investigate automatic alignment methods suitable for North Korean. Finally, we show that a model trained by North Korean bilingual data without human annotation significantly boosts North Korean translation accuracy compared to existing South Korean models in zero-shot settings.

Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios. One representative of such challenging scenarios is to deploy a translation system for a conference with a specific topic, e.g., global warming or coronavirus, where there are usually extremely less resources due to the limited schedule. To motivate wider investigation in such a scenario, we present a real-world fine-grained domain adaptation task in machine translation (FGraDA). The FGraDA dataset consists of Chinese-English translation task for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone. Each sub-domain is equipped with a development set and test set for evaluation purposes. To be closer to reality, FGraDA does not employ any in-domain bilingual training data but provides bilingual dictionaries and wiki knowledge base, which can be easier obtained within a short time. We benchmark the fine-grained domain adaptation task and present in-depth analyses showing that there are still challenging problems to further improve the performance with heterogeneous resources.

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SansTib, a Sanskrit - Tibetan Parallel Corpus and Bilingual Sentence Embedding Model
Sebastian Nehrdich

This paper presents the development of SansTib, a Sanskrit - Classical Tibetan parallel corpus automatically aligned on sentence-level, and a bilingual sentence embedding model. The corpus has a size of about 317,289 sentence pairs and 14,420,771 tokens and thereby is a considerable improvement over previous resources for these two languages. The data is incorporated into the BuddhaNexus database to make it accessible to a larger audience. It also presents a gold evaluation dataset and assesses the quality of the automatic alignment.

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VISA: An Ambiguous Subtitles Dataset for Visual Scene-aware Machine Translation
Yihang Li | Shuichiro Shimizu | Weiqi Gu | Chenhui Chu | Sadao Kurohashi

Existing multimodal machine translation (MMT) datasets consist of images and video captions or general subtitles which rarely contain linguistic ambiguity, making visual information not so effective to generate appropriate translations. We introduce VISA, a new dataset that consists of 40k Japanese-English parallel sentence pairs and corresponding video clips with the following key features: (1) the parallel sentences are subtitles from movies and TV episodes; (2) the source subtitles are ambiguous, which means they have multiple possible translations with different meanings; (3) we divide the dataset into Polysemy and Omission according to the cause of ambiguity. We show that VISA is challenging for the latest MMT system, and we hope that the dataset can facilitate MMT research.

This paper presents a new benchmark test dataset for multi-level complexity-controllable machine translation (MLCC-MT), which is MT controlling the complexity of the output at more than two levels. In previous research, MLCC-MT models have been evaluated on a test dataset automatically constructed from the Newsela corpus, which is a document-level comparable corpus with document-level complexity. The existing test dataset has the following three problems: (i) A source language sentence and its target language sentence are not necessarily an exact translation pair because they are automatically detected. (ii) A target language sentence and its simplified target language sentence are not necessarily exactly parallel because they are automatically aligned. (iii) A sentence-level complexity is not necessarily appropriate because it is transferred from an article-level complexity attached to the Newsela corpus. Therefore, we create a benchmark test dataset for Japanese-to-English MLCC-MT from the Newsela corpus by introducing an automatic filtering of data with inappropriate sentence-level complexity, manual check for parallel target language sentences with different complexity levels, and manual translation. Moreover, we implement two MLCC-NMT frameworks with a Transformer architecture and report their performance on our test dataset as baselines for future research. Our test dataset and codes are released.

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gaHealth: An English–Irish Bilingual Corpus of Health Data
Séamus Lankford | Haithem Afli | Órla Ní Loinsigh | Andy Way

Machine Translation is a mature technology for many high-resource language pairs. However in the context of low-resource languages, there is a paucity of parallel data datasets available for developing translation models. Furthermore, the development of datasets for low-resource languages often focuses on simply creating the largest possible dataset for generic translation. The benefits and development of smaller in-domain datasets can easily be overlooked. To assess the merits of using in-domain data, a dataset for the specific domain of health was developed for the low-resource English to Irish language pair. Our study outlines the process used in developing the corpus and empirically demonstrates the benefits of using an in-domain dataset for the health domain. In the context of translating health-related data, models developed using the gaHealth corpus demonstrated a maximum BLEU score improvement of 22.2 points (40%) when compared with top performing models from the LoResMT2021 Shared Task. Furthermore, we define linguistic guidelines for developing gaHealth, the first bilingual corpus of health data for the Irish language, which we hope will be of use to other creators of low-resource data sets. gaHealth is now freely available online and is ready to be explored for further research.

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Translation Memories as Baselines for Low-Resource Machine Translation
Rebecca Knowles | Patrick Littell

Low-resource machine translation research often requires building baselines to benchmark estimates of progress in translation quality. Neural and statistical phrase-based systems are often used with out-of-the-box settings to build these initial baselines before analyzing more sophisticated approaches, implicitly comparing the first machine translation system to the absence of any translation assistance. We argue that this approach overlooks a basic resource: if you have parallel text, you have a translation memory. In this work, we show that using available text as a translation memory baseline against which to compare machine translation systems is simple, effective, and can shed light on additional translation challenges.

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N24News: A New Dataset for Multimodal News Classification
Zhen Wang | Xu Shan | Xiangxie Zhang | Jie Yang

Current news datasets merely focus on text features on the news and rarely leverage the feature of images, excluding numerous essential features for news classification. In this paper, we propose a new dataset, N24News, which is generated from New York Times with 24 categories and contains both text and image information in each news. We use a multitask multimodal method and the experimental results show multimodal news classification performs better than text-only news classification. Depending on the length of the text, the classification accuracy can be increased by up to 8.11%. Our research reveals the relationship between the performance of a multimodal classifier and its sub-classifiers, and also the possible improvements when applying multimodal in news classification. N24News is shown to have great potential to prompt the multimodal news studies.

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MultiSubs: A Large-scale Multimodal and Multilingual Dataset
Josiah Wang | Josiel Figueiredo | Lucia Specia

This paper introduces a large-scale multimodal and multilingual dataset that aims to facilitate research on grounding words to images in their contextual usage in language. The dataset consists of images selected to unambiguously illustrate concepts expressed in sentences from movie subtitles. The dataset is a valuable resource as (i) the images are aligned to text fragments rather than whole sentences; (ii) multiple images are possible for a text fragment and a sentence; (iii) the sentences are free-form and real-world like; (iv) the parallel texts are multilingual. We also set up a fill-in-the-blank game for humans to evaluate the quality of the automatic image selection process of our dataset. Finally, we propose a fill-in-the-blank task to demonstrate the utility of the dataset, and present some baseline prediction models. The dataset will benefit research on visual grounding of words especially in the context of free-form sentences, and can be obtained from https://doi.org/10.5281/zenodo.5034604 under a Creative Commons licence.

With the rise of deep learning and intelligent vehicles, the smart assistant has become an essential in-car component to facilitate driving and provide extra functionalities. In-car smart assistants should be able to process general as well as car-related commands and perform corresponding actions, which eases driving and improves safety. However, there is a data scarcity issue for low resource languages, hindering the development of research and applications. In this paper, we introduce a new dataset, Cantonese In-car Audio-Visual Speech Recognition (CI-AVSR), for in-car command recognition in the Cantonese language with both video and audio data. It consists of 4,984 samples (8.3 hours) of 200 in-car commands recorded by 30 native Cantonese speakers. Furthermore, we augment our dataset using common in-car background noises to simulate real environments, producing a dataset 10 times larger than the collected one. We provide detailed statistics of both the clean and the augmented versions of our dataset. Moreover, we implement two multimodal baselines to demonstrate the validity of CI-AVSR. Experiment results show that leveraging the visual signal improves the overall performance of the model. Although our best model can achieve a considerable quality on the clean test set, the speech recognition quality on the noisy data is still inferior and remains an extremely challenging task for real in-car speech recognition systems. The dataset and code will be released at https://github.com/HLTCHKUST/CI-AVSR.

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Multimodal Negotiation Corpus with Various Subjective Assessments for Social-Psychological Outcome Prediction from Non-Verbal Cues
Nobukatsu Hojo | Satoshi Kobashikawa | Saki Mizuno | Ryo Masumura

This study investigates social-psychological negotiation-outcome prediction (SPNOP), a novel task for estimating various subjective evaluation scores of negotiation, such as satisfaction and trust, from negotiation dialogue data. To investigate SPNOP, a corpus with various psychological measurements is beneficial because the interaction process of negotiation relates to many aspects of psychology. However, current negotiation corpora only include information related to objective outcomes or a single aspect of psychology. In addition, most use the “laboratory setting” that uses non-skilled negotiators and over simplified negotiation scenarios. There is a concern that such a gap with actual negotiation will intrinsically affect the behavior and psychology of negotiators in the corpus, which can degrade the performance of models trained from the corpus in real situations. Therefore, we created a negotiation corpus with three features; 1) was assessed with various psychological measurements, 2) used skilled negotiators, and 3) used scenarios of context-rich negotiation. We recorded video and audio of negotiations in Japanese to investigate SPNOP in the context of social signal processing. Experimental results indicate that social-psychological outcomes can be effectively estimated from multimodal information.

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MMDAG: Multimodal Directed Acyclic Graph Network for Emotion Recognition in Conversation
Shuo Xu | Yuxiang Jia | Changyong Niu | Hongying Zan

Emotion recognition in conversation is important for an empathetic dialogue system to understand the user’s emotion and then generate appropriate emotional responses. However, most previous researches focus on modeling conversational contexts primarily based on the textual modality or simply utilizing multimodal information through feature concatenation. In order to exploit multimodal information and contextual information more effectively, we propose a multimodal directed acyclic graph (MMDAG) network by injecting information flows inside modality and across modalities into the DAG architecture. Experiments on IEMOCAP and MELD show that our model outperforms other state-of-the-art models. Comparative studies validate the effectiveness of the proposed modality fusion method.

Securing sufficient data to enable automatic sign language translation modeling is challenging. The data insufficiency issue exists in both video and text modalities; however, fewer studies have been performed on text data augmentation compared to video data. In this study, we present three methods of augmenting sign language text modality data, comprising 3,052 Gloss-level Korean Sign Language (GKSL) and Word-level Korean Language (WKL) sentence pairs. Using each of the three methods, the following number of sentence pairs were created: blank replacement 10,654, sentence paraphrasing 1,494, and synonym replacement 899. Translation experiment results using the augmented data showed that when translating from GKSL to WKL and from WKL to GKSL, Bi-Lingual Evaluation Understudy (BLEU) scores improved by 0.204 and 0.170 respectively, compared to when only the original data was used. The three contributions of this study are as follows. First, we demonstrated that three different augmentation techniques used in existing Natural Language Processing (NLP) can be applied to sign language. Second, we propose an automatic data augmentation method which generates quality data by utilizing the Korean sign language gloss dictionary. Lastly, we publish the Gloss-level Korean Sign Language 13k dataset (GKSL13k), which has verified data quality through expert reviews.

We focus on image description and a corresponding assessment system for language learners. To achieve automatic assessment of image description, we construct a novel dataset, the Language Learner Image Description (LLID) dataset, which consists of images, their descriptions, and assessment annotations. Then, we propose a novel task of automatic error correction for image description, and we develop a baseline model that encodes multimodal information from a learner sentence with an image and accurately decodes a corrected sentence. Our experimental results show that the developed model can revise errors that cannot be revised without an image.

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The Multimodal Annotation Software Tool (MAST)
Bruno Cardoso | Neil Cohn

Multimodal combinations of writing and pictures have become ubiquitous in contemporary society, and scholars have increasingly been turning to analyzing these media. Here we present a resource for annotating these complex documents: the Multimodal Annotation Software Tool (MAST). MAST is an application that allows users to analyze visual and multimodal documents by selecting and annotating visual regions, and to establish relations between annotations that create dependencies and/or constituent structures. By means of schema publications, MAST allows annotation theories to be citable, while evolving and being shared. Documents can be annotated using multiple schemas simultaneously, offering more comprehensive perspectives. As a distributed, client-server system MAST allows for collaborative annotations across teams of users, and features team management and resource access functionalities, facilitating the potential for implementing open science practices. Altogether, we aim for MAST to provide a powerful and innovative annotation tool with application across numerous fields engaging with multimodal media.

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A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning
Gerald Schwiebert | Cornelius Weber | Leyuan Qu | Henrique Siqueira | Stefan Wermter

Large datasets as required for deep learning of lip reading do not exist in many languages. In this paper we present the dataset GLips (German Lips) consisting of 250,000 publicly available videos of the faces of speakers of the Hessian Parliament, which was processed for word-level lip reading using an automatic pipeline. The format is similar to that of the English language LRW (Lip Reading in the Wild) dataset, with each video encoding one word of interest in a context of 1.16 seconds duration, which yields compatibility for studying transfer learning between both datasets. By training a deep neural network, we investigate whether lip reading has language-independent features, so that datasets of different languages can be used to improve lip reading models. We demonstrate learning from scratch and show that transfer learning from LRW to GLips and vice versa improves learning speed and performance, in particular for the validation set.

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Multimodality for NLP-Centered Applications: Resources, Advances and Frontiers
Muskan Garg | Seema Wazarkar | Muskaan Singh | Ondřej Bojar

With the development of multimodal systems and natural language generation techniques, the resurgence of multimodal datasets has attracted significant research interests, which aims to provide new information to enrich the representation of textual data. However, there remains a lack of a comprehensive survey for this task. To this end, we take the first step and present a thorough review of this research field. This paper provides an overview of a publicly available dataset with different modalities according to the applications. Furthermore, we discuss the new frontier and give our thoughts. We hope this survey of multimodal datasets can provide the community with quick access and a general picture of the multimodal dataset for specific Natural Language Processing (NLP) applications and motivates future researches. In this context, we release the collection of all multimodal datasets easily accessible here: https://github.com/drmuskangarg/Multimodal-datasets

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Cross-lingual and Multilingual CLIP
Fredrik Carlsson | Philipp Eisen | Faton Rekathati | Magnus Sahlgren

The long-standing endeavor of relating the textual and the visual domain recently underwent a pivotal breakthrough, as OpenAI released CLIP. This model distinguishes how well an English text corresponds with a given image with unprecedented accuracy. Trained via a contrastive learning objective over a huge dataset of 400M of images and captions, it is a work that is not easily replicated, especially for low resource languages. Capitalizing on the modularization of the CLIP architecture, we propose to use cross-lingual teacher learning to re-train the textual encoder for various non-English languages. Our method requires no image data and relies entirely on machine translation which removes the need for data in the target language. We find that our method can efficiently train a new textual encoder with relatively low computational cost, whilst still outperforming previous baselines on multilingual image-text retrieval.

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BAN-Cap: A Multi-Purpose English-Bangla Image Descriptions Dataset
Mohammad Faiyaz Khan | S.M. Sadiq-Ur-Rahman Shifath | Md Saiful Islam

As computers have become efficient at understanding visual information and transforming it into a written representation, research interest in tasks like automatic image captioning has seen a significant leap over the last few years. While most of the research attention is given to the English language in a monolingual setting, resource-constrained languages like Bangla remain out of focus, predominantly due to a lack of standard datasets. Addressing this issue, we present a new dataset BAN-Cap following the widely used Flickr8k dataset, where we collect Bangla captions of the images provided by qualified annotators. Our dataset represents a wider variety of image caption styles annotated by trained people from different backgrounds. We present a quantitative and qualitative analysis of the dataset and the baseline evaluation of the recent models in Bangla image captioning. We investigate the effect of text augmentation and demonstrate that an adaptive attention-based model combined with text augmentation using Contextualized Word Replacement (CWR) outperforms all state-of-the-art models for Bangla image captioning. We also present this dataset’s multipurpose nature, especially on machine translation for Bangla-English and English-Bangla. This dataset and all the models will be useful for further research.

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SSR7000: A Synchronized Corpus of Ultrasound Tongue Imaging for End-to-End Silent Speech Recognition
Naoki Kimura | Zixiong Su | Takaaki Saeki | Jun Rekimoto

This article presents SSR7000, a corpus of synchronized ultrasound tongue and lip images designed for end-to-end silent speech recognition (SSR). Although neural end-to-end models are successfully updating the state-of-the-art technology in the field of automatic speech recognition, SSR research based on ultrasound tongue imaging has still not evolved past cascaded DNN-HMM models due to the absence of a large dataset. In this study, we constructed a large dataset, namely SSR7000, to exploit the performance of the end-to-end models. The SSR7000 dataset contains ultrasound tongue and lip images of 7484 utterances by a single speaker. It contains more utterances per person than any other SSR corpus based on ultrasound imaging. We also describe preprocessing techniques to tackle data variances that are inevitable when collecting a large dataset and present benchmark results using an end-to-end model. The SSR7000 corpus is publicly available under the CC BY-NC 4.0 license.

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A Simple Yet Effective Corpus Construction Method for Chinese Sentence Compression
Yang Zhao | Hiroshi Kanayama | Issei Yoshida | Masayasu Muraoka | Akiko Aizawa

Deletion-based sentence compression in the English language has made significant progress over the past few decades. However, there is a lack of large-scale and high-quality parallel corpus (i.e., (sentence, compression) pairs) for the Chinese language to train an efficient compression system. To remedy this shortcoming, we present a dependency-tree-based method to construct a Chinese corpus with 151k pairs of sentences and compression based on Chinese language-specific characteristics. Subsequently, we trained both extractive and generative neural compression models using the constructed corpus. The experimental results show that our compression model can generate high-quality compressed sentences on both automatic and human evaluation metrics compared with the baselines. The results of the faithfulness evaluation also indicated that the Chinese compression model trained on our constructed corpus can produce more faithful compressed sentences. Furthermore, a dataset with 1,000 pairs of sentences and ground truth compression was manually created for automatic evaluation, which, we believe, will benefit future research on Chinese sentence compression.

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JADE: Corpus for Japanese Definition Modelling
Han Huang | Tomoyuki Kajiwara | Yuki Arase

This study investigated and released the JADE, a corpus for Japanese definition modelling, which is a technique that automatically generates definitions of a given target word and phrase. It is a crucial technique for practical applications that assist language learning and education, as well as for those supporting reading documents in unfamiliar domains. Although corpora for development of definition modelling techniques have been actively created, their languages are mostly limited to English. In this study, a corpus for Japanese, named JADE, was created following the previous study that mines an online encyclopedia. The JADE provides about 630k sets of targets, their definitions, and usage examples as contexts for about 41k unique targets, which is sufficiently large to train neural models. The targets are both words and phrases, and the coverage of domains and topics is diverse. The performance of a pre-trained sequence-to-sequence model and the state-of-the-art definition modelling method was also benchmarked on JADE for future development of the technique in Japanese. The JADE corpus has been released and available online.

As neural Text Generation Models (TGM) have become more and more capable of generating text indistinguishable from human-written ones, the misuse of text generation technologies can have serious ramifications. Although a neural classifier often achieves high detection accuracy, the reason for it is not well studied. Most previous work revolves around studying the impact of model structure and the decoding strategy on ease of detection, but little work has been done to analyze the forms of artifacts left by the TGM. We propose to systematically study the forms and scopes of artifacts by corrupting texts, replacing them with linguistic or statistical features, and applying the interpretable method of Integrated Gradients. Comprehensive experiments show artifacts a) primarily relate to token co-occurrence, b) feature more heavily at the head of vocabulary, c) appear more in content word than stopwords, d) are sometimes detrimental in the form of number of token occurrences, e) are less likely to exist in high-level semantics or syntaxes, f) manifest in low concreteness values for higher-order n-grams.

Natural language generation in real-time settings with raw sensor data is a challenging task. We find that formulating the task as an end-to-end problem leads to two major challenges in content selection – the sensor data is both redundant and diverse across environments, thereby making it hard for the encoders to select and reason on the data. We here present a new corpus for a specific domain that instantiates these properties. It includes handover utterances that an assistant for a semi-autonomous drone uses to communicate with humans during the drone flight. The corpus consists of sensor data records and utterances in 8 different environments. As a structured intermediary representation between data records and text, we explore the use of description logic (DL). We also propose a neural generation model that can alert the human pilot of the system state and environment in preparation of the handover of control.

Stock market investors debate and heavily discuss stock ideas, investing strategies, news and market movements on social media platforms. The discussions are significantly longer in length and require extensive domain expertise for understanding. In this paper, we curate such discussions and construct a first-of-its-kind of abstractive summarization dataset. Our curated dataset consists of 7888 Reddit posts and manually constructed summaries for 400 posts. We robustly evaluate the summaries and conduct experiments on SOTA summarization tools to showcase their limitations. We plan to make the dataset publicly available. The sample dataset is available here: https://dhyeyjani.github.io/RSMC

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Combination of Contextualized and Non-Contextualized Layers for Lexical Substitution in French
Kévin Espasa | Emmanuel Morin | Olivier Hamon

Lexical substitution task requires to substitute a target word by candidates in a given context. Candidates must keep meaning and grammatically of the sentence. The task, introduced in the SemEval 2007, has two objectives. The first objective is to find a list of substitutes for a target word. This list of substitutes can be obtained with lexical resources like WordNet or generated with a pre-trained language model. The second objective is to rank these substitutes using the context of the sentence. Most of the methods use vector space models or more recently embeddings to rank substitutes. Embedding methods use high contextualized representation. This representation can be over contextualized and in this way overlook good substitute candidates which are more similar on non-contextualized layers. SemDis 2014 introduced the lexical substitution task in French. We propose an application of the state-of-the-art method based on BERT in French and a novel method using contextualized and non-contextualized layers to increase the suggestion of words having a lower probability in a given context but that are more semantically similar. Experiments show our method increases the BERT based system on the OOT measure but decreases on the BEST measure in the SemDis 2014 benchmark.

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SuMe: A Dataset Towards Summarizing Biomedical Mechanisms
Mohaddeseh Bastan | Nishant Shankar | Mihai Surdeanu | Niranjan Balasubramanian

Can language models read biomedical texts and explain the biomedical mechanisms discussed? In this work we introduce a biomedical mechanism summarization task. Biomedical studies often investigate the mechanisms behind how one entity (e.g., a protein or a chemical) affects another in a biological context. The abstracts of these publications often include a focused set of sentences that present relevant supporting statements regarding such relationships, associated experimental evidence, and a concluding sentence that summarizes the mechanism underlying the relationship. We leverage this structure and create a summarization task, where the input is a collection of sentences and the main entities in an abstract, and the output includes the relationship and a sentence that summarizes the mechanism. Using a small amount of manually labeled mechanism sentences, we train a mechanism sentence classifier to filter a large biomedical abstract collection and create a summarization dataset with 22k instances. We also introduce conclusion sentence generation as a pretraining task with 611k instances. We benchmark the performance of large bio-domain language models. We find that while the pretraining task help improves performance, the best model produces acceptable mechanism outputs in only 32% of the instances, which shows the task presents significant challenges in biomedical language understanding and summarization.

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CATAMARAN: A Cross-lingual Long Text Abstractive Summarization Dataset
Zheng Chen | Hongyu Lin

Cross-lingual summarization, which produces the summary in one language from a given source document in another language, could be extremely helpful for humans to obtain information across the world. However, it is still a little-explored task due to the lack of datasets. Recent studies are primarily based on pseudo-cross-lingual datasets obtained by translation. Such an approach would inevitably lead to the loss of information in the original document and introduce noise into the summary, thus hurting the overall performance. In this paper, we present CATAMARAN, the first high-quality cross-lingual long text abstractive summarization dataset. It contains about 20,000 parallel news articles and corresponding summaries, all written by humans. The average lengths of articles are 1133.65 for English articles and 2035.33 for Chinese articles, and the average lengths of the summaries are 26.59 and 70.05, respectively. We train and evaluate an mBART-based cross-lingual abstractive summarization model using our dataset. The result shows that, compared with mono-lingual systems, the cross-lingual abstractive summarization system could also achieve solid performance.

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Emotion analysis and detection during COVID-19
Tiberiu Sosea | Chau Pham | Alexander Tekle | Cornelia Caragea | Junyi Jessy Li

Understanding emotions that people express during large-scale crises helps inform policy makers and first responders about the emotional states of the population as well as provide emotional support to those who need such support. We present CovidEmo, a dataset of ~3,000 English tweets labeled with emotions and temporally distributed across 18 months. Our analyses reveal the emotional toll caused by COVID-19, and changes of the social narrative and associated emotions over time. Motivated by the time-sensitive nature of crises and the cost of large-scale annotation efforts, we examine how well large pre-trained language models generalize across domains and timeline in the task of perceived emotion prediction in the context of COVID-19. Our analyses suggest that cross-domain information transfers occur, yet there are still significant gaps. We propose semi-supervised learning as a way to bridge this gap, obtaining significantly better performance using unlabeled data from the target domain.

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Cross-lingual Emotion Detection
Sabit Hassan | Shaden Shaar | Kareem Darwish

Emotion detection can provide us with a window into understanding human behavior. Due to the complex dynamics of human emotions, however, constructing annotated datasets to train automated models can be expensive. Thus, we explore the efficacy of cross-lingual approaches that would use data from a source language to build models for emotion detection in a target language. We compare three approaches, namely: i) using inherently multilingual models; ii) translating training data into the target language; and iii) using an automatically tagged parallel corpus. In our study, we consider English as the source language with Arabic and Spanish as target languages. We study the effectiveness of different classification models such as BERT and SVMs trained with different features. Our BERT-based monolingual models that are trained on target language data surpass state-of-the-art (SOTA) by 4% and 5% absolute Jaccard score for Arabic and Spanish respectively. Next, we show that using cross-lingual approaches with English data alone, we can achieve more than 90% and 80% relative effectiveness of the Arabic and Spanish BERT models respectively. Lastly, we use LIME to analyze the challenges of training cross-lingual models for different language pairs.

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DirectQuote: A Dataset for Direct Quotation Extraction and Attribution in News Articles
Yuanchi Zhang | Yang Liu

Quotation extraction and attribution are challenging tasks, aiming at determining the spans containing quotations and attributing each quotation to the original speaker. Applying this task to news data is highly related to fact-checking, media monitoring and news tracking. Direct quotations are more traceable and informative, and therefore of great significance among different types of quotations. Therefore, this paper introduces DirectQuote, a corpus containing 19,760 paragraphs and 10,279 direct quotations manually annotated from online news media. To the best of our knowledge, this is the largest and most complete corpus that focuses on direct quotations in news texts. We ensure that each speaker in the annotation can be linked to a specific named entity on Wikidata, benefiting various downstream tasks. In addition, for the first time, we propose several sequence labeling models as baseline methods to extract and attribute quotations simultaneously in an end-to-end manner.

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VaccineLies: A Natural Language Resource for Learning to Recognize Misinformation about the COVID-19 and HPV Vaccines
Maxwell Weinzierl | Sanda Harabagiu

Billions of COVID-19 vaccines have been administered, but many remain hesitant. Misinformation about the COVID-19 vaccines and other vaccines, propagating on social media, is believed to drive hesitancy towards vaccination. The ability to automatically recognize misinformation targeting vaccines on Twitter depends on the availability of data resources. In this paper we present VaccineLies, a large collection of tweets propagating misinformation about two vaccines: the COVID-19 vaccines and the Human Papillomavirus (HPV) vaccines. Misinformation targets are organized in vaccine-specific taxonomies, which reveal the misinformation themes and concerns. The ontological commitments of the misinformation taxonomies provide an understanding of which misinformation themes and concerns dominate the discourse about the two vaccines covered in VaccineLies. The organization into training, testing and development sets of VaccineLies invites the development of novel supervised methods for detecting misinformation on Twitter and identifying the stance towards it. Furthermore, VaccineLies can be a stepping stone for the development of datasets focusing on misinformation targeting additional vaccines.

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Tackling Irony Detection using Ensemble Classifiers
Christoph Turban | Udo Kruschwitz

Automatic approaches to irony detection have been of interest to the NLP community for a long time, yet, state-of-the-art approaches still fall way short of what one would consider a desirable performance. In part this is due to the inherent difficulty of the problem. However, in recent years ensembles of transformer-based approaches have emerged as a promising direction to push the state of the art forward in a wide range of NLP applications. A different, more recent, development is the automatic augmentation of training data. In this paper we will explore both these directions for the task of irony detection in social media. Using the common SemEval 2018 Task 3 benchmark collection we demonstrate that transformer models are well suited in ensemble classifiers for the task at hand. In the multi-class classification task we observe statistically significant improvements over strong baselines. For binary classification we achieve performance that is on par with state-of-the-art alternatives. The examined data augmentation strategies showed an effect, but are not decisive for good results.

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Automatic Construction of an Annotated Corpus with Implicit Aspects
Aye Aye Mar | Kiyoaki Shirai

Aspect-based sentiment analysis (ABSA) is a task that involves classifying the polarity of aspects of the products or services described in users’ reviews. Most previous work on ABSA has focused on explicit aspects, which appear as explicit words or phrases in the sentences of the review. However, users often express their opinions toward the aspects indirectly or implicitly, in which case the specific name of an aspect does not appear in the review. The current datasets used for ABSA are mainly annotated with explicit aspects. This paper proposes a novel method for constructing a corpus that is automatically annotated with implicit aspects. The main idea is that sentences containing explicit and implicit aspects share a similar context. First, labeled sentences with explicit aspects and unlabeled sentences that include implicit aspects are collected. Next, clustering is performed on these sentences so that similar sentences are merged into the same cluster. Finally, the explicit aspects are propagated to the unlabeled sentences in the same cluster, in order to construct a labeled dataset containing implicit aspects. The results of our experiments on mobile phone reviews show that our method of identifying the labels of implicit aspects achieves a maximum accuracy of 82%.

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A Multimodal Corpus for Emotion Recognition in Sarcasm
Anupama Ray | Shubham Mishra | Apoorva Nunna | Pushpak Bhattacharyya

While sentiment and emotion analysis have been studied extensively, the relationship between sarcasm and emotion has largely remained unexplored. A sarcastic expression may have a variety of underlying emotions. For example, “I love being ignored” belies sadness, while “my mobile is fabulous with a battery backup of only 15 minutes!” expresses frustration. Detecting the emotion behind a sarcastic expression is non-trivial yet an important task. We undertake the task of detecting the emotion in a sarcastic statement, which to the best of our knowledge, is hitherto unexplored. We start with the recently released multimodal sarcasm detection dataset (MUStARD) pre-annotated with 9 emotions. We identify and correct 343 incorrect emotion labels (out of 690). We double the size of the dataset, label it with emotions along with valence and arousal which are important indicators of emotional intensity. Finally, we label each sarcastic utterance with one of the four sarcasm types-Propositional, Embedded, Likeprefixed and Illocutionary, with the goal of advancing sarcasm detection research. Exhaustive experimentation with multimodal (text, audio, and video) fusion models establishes a benchmark for exact emotion recognition in sarcasm and outperforms the state-of-art sarcasm detection. We release the dataset enriched with various annotations and the code for research purposes: https://github.com/apoorva-nunna/MUStARD_Plus_Plus

Personal Narrative (PN) is the recollection of individuals’ life experiences, events, and thoughts along with the associated emotions in the form of a story. Compared to other genres such as social media texts or microblogs, where people write about experienced events or products, the spoken PNs are complex to analyze and understand. They are usually long and unstructured, involving multiple and related events, characters as well as thoughts and emotions associated with events, objects, and persons. In spoken PNs, emotions are conveyed by changing the speech signal characteristics as well as the lexical content of the narrative. In this work, we annotate a corpus of spoken personal narratives, with the emotion valence using discrete values. The PNs are segmented into speech segments, and the annotators annotate them in the discourse context, with values on a 5-point bipolar scale ranging from -2 to +2 (0 for neutral). In this way, we capture the unfolding of the PNs events and changes in the emotional state of the narrator. We perform an in-depth analysis of the inter-annotator agreement, the relation between the label distribution w.r.t. the stimulus (positive/negative) used for the elicitation of the narrative, and compare the segment-level annotations to a baseline continuous annotation. We find that the neutral score plays an important role in the agreement. We observe that it is easy to differentiate the positive from the negative valence while the confusion with the neutral label is high. Keywords: Personal Narratives, Emotion Annotation, Segment Level Annotation

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A Large-Scale Japanese Dataset for Aspect-based Sentiment Analysis
Yuki Nakayama | Koji Murakami | Gautam Kumar | Sudha Bhingardive | Ikuko Hardaway

There has been significant progress in the field of sentiment analysis. However, aspect-based sentiment analysis (ABSA) has not been explored in the Japanese language even though it has a huge scope in many natural language processing applications such as 1) tracking sentiment towards products, movies, politicians etc; 2) improving customer relation models. The main reason behind this is that there is no standard Japanese dataset available for ABSA task. In this paper, we present the first standard Japanese dataset for the hotel reviews domain. The proposed dataset contains 53,192 review sentences with seven aspect categories and two polarity labels. We perform experiments on this dataset using popular ABSA approaches and report error analysis. Our experiments show that contextual models such as BERT works very well for the ABSA task in the Japanese language and also show the need to focus on other NLP tasks for better performance through our error analysis.

We annotate 35,000 SNS posts with both the writer’s subjective sentiment polarity labels and the reader’s objective ones to construct a Japanese sentiment analysis dataset. Our dataset includes intensity labels (none, weak, medium, and strong) for each of the eight basic emotions by Plutchik (joy, sadness, anticipation, surprise, anger, fear, disgust, and trust) as well as sentiment polarity labels (strong positive, positive, neutral, negative, and strong negative). Previous studies on emotion analysis have studied the analysis of basic emotions and sentiment polarity independently. In other words, there are few corpora that are annotated with both basic emotions and sentiment polarity. Our dataset is the first large-scale corpus to annotate both of these emotion labels, and from both the writer’s and reader’s perspectives. In this paper, we analyze the relationship between basic emotion intensity and sentiment polarity on our dataset and report the results of benchmarking sentiment polarity classification.

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Complementary Learning of Aspect Terms for Aspect-based Sentiment Analysis
Han Qin | Yuanhe Tian | Fei Xia | Yan Song

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity towards a given aspect term in a sentence on the fine-grained level, which usually requires a good understanding of contextual information, especially appropriately distinguishing of a given aspect and its contexts, to achieve good performance. However, most existing ABSA models pay limited attention to the modeling of the given aspect terms and thus result in inferior results when a sentence contains multiple aspect terms with contradictory sentiment polarities. In this paper, we propose to improve ABSA by complementary learning of aspect terms, which serves as a supportive auxiliary task to enhance ABSA by explicitly recovering the aspect terms from each input sentence so as to better understand aspects and their contexts. Particularly, a discriminator is also introduced to further improve the learning process by appropriately balancing the impact of aspect recovery to sentiment prediction. Experimental results on five widely used English benchmark datasets for ABSA demonstrate the effectiveness of our approach, where state-of-the-art performance is observed on all datasets.

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Deep One-Class Hate Speech Detection Model
Saugata Bose | Dr. Guoxin Su

Hate speech detection for social media posts is considered as a binary classification problem in existing approaches, largely neglecting distinct attributes of hate speeches from other sentimental types such as “aggressive” and “racist”. As these sentimental types constitute a significant major portion of data, the classification performance is compromised. Moreover, those classifiers often do not generalize well across different datasets due to a relatively small number of hate-class samples. In this paper, we adopt a one-class perspective for hate speech detection, where the detection classifier is trained with hate-class samples only. Our model employs a BERT-BiLSTM module for feature extraction and a one-class SVM for classification. A comprehensive evaluation with four benchmarking datasets demonstrates the better performance of our model than existing approaches, as well as the advantage of training our model with a combination of the four datasets.

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Opinions in Interactions : New Annotations of the SEMAINE Database
Valentin Barriere | Slim Essid | Chloé Clavel

In this paper, we present the process we used in order to collect new annotations of opinions over the multimodal corpus SEMAINE composed of dyadic interactions. The dataset had already been annotated continuously in two affective dimensions related to the emotions: Valence and Arousal. We annotated the part of SEMAINE called Solid SAL composed of 79 interactions between a user and an operator playing the role of a virtual agent designed to engage a person in a sustained, emotionally colored conversation. We aligned the audio at the word level using the available high-quality manual transcriptions. The annotated dataset contains 5627 speech turns for a total of 73,944 words, corresponding to 6 hours 20 minutes of dyadic interactions. Each interaction has been labeled by three annotators at the speech turn level following a three-step process. This method allows us to obtain a precise annotation regarding the opinion of a speaker. We obtain thus a dataset dense in opinions, with more than 48% of the annotated speech turns containing at least one opinion. We then propose a new baseline for the detection of opinions in interactions improving slightly a state of the art model with RoBERTa embeddings. The obtained results on the database are promising with a F1-score at 0.72.

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Pars-ABSA: a Manually Annotated Aspect-based Sentiment Analysis Benchmark on Farsi Product Reviews
Taha Shangipour ataei | Kamyar Darvishi | Soroush Javdan | Behrouz Minaei-Bidgoli | Sauleh Eetemadi

Due to the increased availability of online reviews, sentiment analysis witnessed a thriving interest from researchers. Sentiment analysis is a computational treatment of sentiment used to extract and understand the opinions of authors. While many systems were built to predict the sentiment of a document or a sentence, many others provide the necessary detail on various aspects of the entity (i.e., aspect-based sentiment analysis). Most of the available data resources were tailored to English and the other popular European languages. Although Farsi is a language with more than 110 million speakers, to the best of our knowledge, there is a lack of proper public datasets on aspect-based sentiment analysis for Farsi. This paper provides a manually annotated Farsi dataset, Pars-ABSA, annotated and verified by three native Farsi speakers. The dataset consists of 5,114 positive, 3,061 negative and 1,827 neutral data samples from 5,602 unique reviews. Moreover, as a baseline, this paper reports the performance of some aspect-based sentiment analysis methods focusing on transfer learning on Pars-ABSA.

Social media platforms such as Twitter have evolved into a vast information sharing platform, allowing people from a variety of backgrounds and expertise to share their opinions on numerous events such as terrorism, narcotics and many other social issues. People sometimes misuse the power of social media for their agendas, such as illegal trades and negatively influencing others. Because of this, sentiment analysis has won the interest of a lot of researchers to widely analyze public opinion for social media monitoring. Several benchmark datasets for sentiment analysis across a range of domains have been made available, especially for high-resource languages. A few datasets are available for low-resource Indian languages like Hindi, such as movie reviews and product reviews, which do not address the current need for social media monitoring. In this paper, we address the challenges of sentiment analysis in Hindi and socially relevant domains by introducing a balanced corpus annotated with the sentiment classes, viz. positive, negative and neutral. To show the effective usage of the dataset, we build several deep learning based models and establish them as the baselines for further research in this direction.

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Sentiment Analysis of Homeric Text: The 1st Book of Iliad
John Pavlopoulos | Alexandros Xenos | Davide Picca

Sentiment analysis studies are focused more on online customer reviews or social media, and less on literary studies. The problem is greater for ancient languages, where the linguistic expression of sentiments may diverge from modern linguistic forms. This work presents the outcome of a sentiment annotation task of the first Book of Iliad, an ancient Greek poem. The annotators were provided with verses translated into modern Greek and they annotated the perceived emotions and sentiments verse by verse. By estimating the fraction of annotators that found a verse as belonging to a specific sentiment class, we model the poem’s perceived sentiment as a multi-variate time series. By experimenting with a state of the art deep learning masked language model, pre-trained on modern Greek and fine-tuned to estimate the sentiment of our data, we registered a mean squared error of 0.063. This low error indicates that sentiment estimators built on our dataset can potentially be used as mechanical annotators, hence facilitating the distant reading of Homeric text. Our dataset is released for public use.

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The Persian Dependency Treebank Made Universal
Pegah Safari | Mohammad Sadegh Rasooli | Amirsaeid Moloodi | Alireza Nourian

We describe an automatic method for converting the Persian Dependency Treebank (Rasooli et al., 2013) to Universal Dependencies. This treebank contains 29107 sentences. Our experiments along with manual linguistic analysis show that our data is more compatible with Universal Dependencies than the Uppsala Persian Universal Dependency Treebank (Seraji et al., 2016), larger in size and more diverse in vocabulary. Our data brings in labeled attachment F-score of 85.2 in supervised parsing. Also, our delexicalized Persian-to-English parser transfer experiments show that a parsing model trained on our data is ≈2% absolutely more accurate than that of Seraji et al. (2016) in terms of labeled attachment score.

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GujMORPH - A Dataset for Creating Gujarati Morphological Analyzer
Jatayu Baxi | Brijesh Bhatt

Computational morphology deals with the processing of a language at the word level. A morphological analyzer is a key linguistic word-level tool that returns all the constituent morphemes and their grammatical categories associated with a particular word form. For the highly inflectional and low resource languages, the creation of computational morphology-related tools is a challenging task due to the unavailability of underlying key resources. In this paper, we discuss the creation of an annotated morphological dataset- GujMORPH for the Gujarati - an indo-aryan language. For the creation of this dataset, we studied language grammar, word formation rules, and suffix attachments in depth. This dataset contains 16,527 unique inflected words along with their morphological segmentation and grammatical feature tagging information. It is a first of its kind dataset for the Gujarati language and can be used to develop morphological analyzer and generator models. The dataset is annotated in the standard Unimorph schema and evaluated on the baseline system. We also describe the tool used to annotate the data in the standard format. The dataset is released publicly along with the library. Using this library, the data can be obtained in a format that can be directly used to train any machine learning model.

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Informal Persian Universal Dependency Treebank
Roya Kabiri | Simin Karimi | Mihai Surdeanu

This paper presents the phonological, morphological, and syntactic distinctions between formal and informal Persian, showing that these two variants have fundamental differences that cannot be attributed solely to pronunciation discrepancies. Given that informal Persian exhibits particular characteristics, any computational model trained on formal Persian is unlikely to transfer well to informal Persian, necessitating the creation of dedicated treebanks for this variety. We thus detail the development of the open-source Informal Persian Universal Dependency Treebank, a new treebank annotated within the Universal Dependencies scheme. We then investigate the parsing of informal Persian by training two dependency parsers on existing formal treebanks and evaluating them on out-of-domain data, i.e. the development set of our informal treebank. Our results show that parsers experience a substantial performance drop when we move across the two domains, as they face more unknown tokens and structures and fail to generalize well. Furthermore, the dependency relations whose performance deteriorates the most represent the unique properties of the informal variant. The ultimate goal of this study that demonstrates a broader impact is to provide a stepping-stone to reveal the significance of informal variants of languages, which have been widely overlooked in natural language processing tools across languages.

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Automatic Correction of Syntactic Dependency Annotation Differences
Andrew Zupon | Andrew Carnie | Michael Hammond | Mihai Surdeanu

Annotation inconsistencies between data sets can cause problems for low-resource NLP, where noisy or inconsistent data cannot be easily replaced. We propose a method for automatically detecting annotation mismatches between dependency parsing corpora, along with three related methods for automatically converting the mismatches. All three methods rely on comparing unseen examples in a new corpus with similar examples in an existing corpus. These three methods include a simple lexical replacement using the most frequent tag of the example in the existing corpus, a GloVe embedding-based replacement that considers related examples, and a BERT-based replacement that uses contextualized embeddings to provide examples fine-tuned to our data. We evaluate these conversions by retraining two dependency parsers—Stanza and Parsing as Tagging (PaT)—on the converted and unconverted data. We find that applying our conversions yields significantly better performance in many cases. Some differences observed between the two parsers are observed. Stanza has a more complex architecture with a quadratic algorithm, taking longer to train, but it can generalize from less data. The PaT parser has a simpler architecture with a linear algorithm, speeding up training but requiring more training data to reach comparable or better performance.

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Building Large-Scale Japanese Pronunciation-Annotated Corpora for Reading Heteronymous Logograms
Fumikazu Sato | Naoki Yoshinaga | Masaru Kitsuregawa

Although screen readers enable visually impaired people to read written text via speech, the ambiguities in pronunciations of heteronyms cause wrong reading, which has a serious impact on the text understanding. Especially in Japanese, there are many common heteronyms expressed by logograms (Chinese characters or kanji) that have totally different pronunciations (and meanings). In this study, to improve the accuracy of pronunciation prediction, we construct two large-scale Japanese corpora that annotate kanji characters with their pronunciations. Using existing language resources on i) book titles compiled by the National Diet Library and ii) the books in a Japanese digital library called Aozora Bunko and their Braille translations, we develop two large-scale pronunciation-annotated corpora for training pronunciation prediction models. We first extract sentence-level alignments between the Aozora Bunko text and its pronunciation converted from the Braille data. We then perform dictionary-based pattern matching based on morphological dictionaries to find word-level pronunciation alignments. We have ultimately obtained the Book Title corpus with 336M characters (16.4M book titles) and the Aozora Bunko corpus with 52M characters (1.6M sentences). We analyzed pronunciation distributions for 203 common heteronyms, and trained a BERT-based pronunciation prediction model for 93 heteronyms, which achieved an average accuracy of 0.939.

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StyleKQC: A Style-Variant Paraphrase Corpus for Korean Questions and Commands
Won Ik Cho | Sangwhan Moon | Jongin Kim | Seokmin Kim | Nam Soo Kim

Paraphrasing is often performed with less concern for controlled style conversion. Especially for questions and commands, style-variant paraphrasing can be crucial in tone and manner, which also matters with industrial applications such as dialog systems. In this paper, we attack this issue with a corpus construction scheme that simultaneously considers the core content and style of directives, namely intent and formality, for the Korean language. Utilizing manually generated natural language queries on six daily topics, we expand the corpus to formal and informal sentences by human rewriting and transferring. We verify the validity and industrial applicability of our approach by checking the adequate classification and inference performance that fit with conventional fine-tuning approaches, at the same time proposing a supervised formality transfer task.

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Syntax-driven Approach for Semantic Role Labeling
Yuanhe Tian | Han Qin | Fei Xia | Yan Song

As an important task to analyze the semantic structure of a sentence, semantic role labeling (SRL) aims to locate the semantic role (e.g., agent) of noun phrases with respect to a given predicate and thus plays an important role in downstream tasks such as dialogue systems. To achieve a better performance in SRL, a model is always required to have a good understanding of the context information. Although one can use advanced text encoder (e.g., BERT) to capture the context information, extra resources are also required to further improve the model performance. Considering that there are correlations between the syntactic structure and the semantic structure of the sentence, many previous studies leverage auto-generated syntactic knowledge, especially the dependencies, to enhance the modeling of context information through graph-based architectures, where limited attention is paid to other types of auto-generated knowledge. In this paper, we propose map memories to enhance SRL by encoding different types of auto-generated syntactic knowledge (i.e., POS tags, syntactic constituencies, and word dependencies) obtained from off-the-shelf toolkits. Experimental results on two English benchmark datasets for span-style SRL (i.e., CoNLL-2005 and CoNLL-2012) demonstrate the effectiveness of our approach, which outperforms strong baselines and achieves state-of-the-art results on CoNLL-2005.

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HerBERT Based Language Model Detects Quantifiers and Their Semantic Properties in Polish
Marcin Woliński | Bartłomiej Nitoń | Witold Kieraś | Jakub Szymanik

The paper presents a tool for automatic marking up of quantifying expressions, their semantic features, and scopes. We explore the idea of using a BERT based neural model for the task (in this case HerBERT, a model trained specifically for Polish, is used). The tool is trained on a recent manually annotated Corpus of Polish Quantificational Expressions (Szymanik and Kieraś, 2022). We discuss how it performs against human annotation and present results of automatic annotation of 300 million sub-corpus of National Corpus of Polish. Our results show that language models can effectively recognise semantic category of quantification as well as identify key semantic properties of quantifiers, like monotonicity. Furthermore, the algorithm we have developed can be used for building semantically annotated quantifier corpora for other languages.

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Lexical Resource Mapping via Translations
Hongchang Bao | Bradley Hauer | Grzegorz Kondrak

Aligning lexical resources that associate words with concepts in multiple languages increases the total amount of semantic information that can be leveraged for various NLP tasks. We present a translation-based approach to mapping concepts across diverse resources. Our methods depend only on multilingual lexicalization information. When applied to align WordNet/BabelNet to CLICS and OmegaWiki, our methods achieve state-of-the-art accuracy, without any dependence on other sources of semantic knowledge. Since each word-concept pair corresponds to a unique sense of the word, we also demonstrate that the mapping task can be framed as word sense disambiguation. To facilitate future work, we release a set of high-precision WordNet-CLICS alignments, produced by combining three different mapping methods.

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Unsupervised Attention-based Sentence-Level Meta-Embeddings from Contextualised Language Models
Keigo Takahashi | Danushka Bollegala

A variety of contextualised language models have been proposed in the NLP community, which are trained on diverse corpora to produce numerous Neural Language Models (NLMs). However, different NLMs have reported different levels of performances in downstream NLP applications when used as text representations. We propose a sentence-level meta-embedding learning method that takes independently trained contextualised word embedding models and learns a sentence embedding that preserves the complementary strengths of the input source NLMs. Our proposed method is unsupervised and is not tied to a particular downstream task, which makes the learnt meta-embeddings in principle applicable to different tasks that require sentence representations. Specifically, we first project the token-level embeddings obtained by the individual NLMs and learn attention weights that indicate the contributions of source embeddings towards their token-level meta-embeddings. Next, we apply mean and max pooling to produce sentence-level meta-embeddings from token-level meta-embeddings. Experimental results on semantic textual similarity benchmarks show that our proposed unsupervised sentence-level meta-embedding method outperforms previously proposed sentence-level meta-embedding methods as well as a supervised baseline.

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Identification of Fine-Grained Location Mentions in Crisis Tweets
Sarthak Khanal | Maria Traskowsky | Doina Caragea

Identification of fine-grained location mentions in crisis tweets is central in transforming situational awareness information extracted from social media into actionable information. Most prior works have focused on identifying generic locations, without considering their specific types. To facilitate progress on the fine-grained location identification task, we assemble two tweet crisis datasets and manually annotate them with specific location types. The first dataset contains tweets from a mixed set of crisis events, while the second dataset contains tweets from the global COVID-19 pandemic. We investigate the performance of state-of-the-art deep learning models for sequence tagging on these datasets, in both in-domain and cross-domain settings.

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HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection
Francielle Vargas | Isabelle Carvalho | Fabiana Rodrigues de Góes | Thiago Pardo | Fabrício Benevenuto

Due to the severity of the social media offensive and hateful comments in Brazil, and the lack of research in Portuguese, this paper provides the first large-scale expert annotated corpus of Brazilian Instagram comments for hate speech and offensive language detection. The HateBR corpus was collected from the comment section of Brazilian politicians’ accounts on Instagram and manually annotated by specialists, reaching a high inter-annotator agreement. The corpus consists of 7,000 documents annotated according to three different layers: a binary classification (offensive versus non-offensive comments), offensiveness-level classification (highly, moderately, and slightly offensive), and nine hate speech groups (xenophobia, racism, homophobia, sexism, religious intolerance, partyism, apology for the dictatorship, antisemitism, and fatphobia). We also implemented baseline experiments for offensive language and hate speech detection and compared them with a literature baseline. Results show that the baseline experiments on our corpus outperform the current state-of-the-art for the Portuguese language.

Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without adequate treatment. Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention. Recent advances in pretrained contextualized language representations have promoted the development of several domainspecific pretrained models and facilitated several downstream applications. However, there are no existing pretrained language models for mental healthcare. This paper trains and release two pretrained masked language models, i.e., MentalBERT and MentalRoBERTa, to benefit machine learning for the mental healthcare research community. Besides, we evaluate our trained domain-specific models and several variants of pretrained language models on several mental disorder detection benchmarks and demonstrate that language representations pretrained in the target domain improve the performance of mental health detection tasks.

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Leveraging Hashtag Networks for Multimodal Popularity Prediction of Instagram Posts
Yu Yun Liao

With the increasing commercial and social importance of Instagram in recent years, more researchers begin to take multimodal approaches to predict popular content on Instagram. However, existing popularity prediction approaches often reduce hashtags to simple features such as hashtag length or number of hashtags in a post, ignoring the structural and textual information that entangles between hashtags. In this paper, we propose a multimodal framework using post captions, image, hashtag network, and topic model to predict popular influencer posts in Taiwan. Specifically, the hashtag network is constructed as a homogenous graph using the co-occurrence relationship between hashtags, and we extract its structural information with GraphSAGE and semantic information with BERTopic. Finally, the prediction process is defined as a binary classification task (popular/unpopular) using neural networks. Our results show that the proposed framework incorporating hashtag network outperforms all baselines and unimodal models, while information captured from the hashtag network and topic model appears to be complementary.

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Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis
Hang Jiang | Yining Hua | Doug Beeferman | Deb Roy

Social media data such as Twitter messages (“tweets”) pose a particular challenge to NLP systems because of their short, noisy, and colloquial nature. Tasks such as Named Entity Recognition (NER) and syntactic parsing require highly domain-matched training data for good performance. To date, there is no complete training corpus for both NER and syntactic analysis (e.g., part of speech tagging, dependency parsing) of tweets. While there are some publicly available annotated NLP datasets of tweets, they are only designed for individual tasks. In this study, we aim to create Tweebank-NER, an English NER corpus based on Tweebank V2 (TB2), train state-of-the-art (SOTA) Tweet NLP models on TB2, and release an NLP pipeline called Twitter-Stanza. We annotate named entities in TB2 using Amazon Mechanical Turk and measure the quality of our annotations. We train the Stanza pipeline on TB2 and compare with alternative NLP frameworks (e.g., FLAIR, spaCy) and transformer-based models. The Stanza tokenizer and lemmatizer achieve SOTA performance on TB2, while the Stanza NER tagger, part-of-speech (POS) tagger, and dependency parser achieve competitive performance against non-transformer models. The transformer-based models establish a strong baseline in Tweebank-NER and achieve the new SOTA performance in POS tagging and dependency parsing on TB2. We release the dataset and make both the Stanza pipeline and BERTweet-based models available “off-the-shelf” for use in future Tweet NLP research. Our source code, data, and pre-trained models are available at: https://github.com/social-machines/TweebankNLP.

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Did that happen? Predicting Social Media Posts that are Indicative of what happened in a scene: A case study of a TV show
Anietie Andy | Reno Kriz | Sharath Chandra Guntuku | Derry Tanti Wijaya | Chris Callison-Burch

While popular Television (TV) shows are airing, some users interested in these shows publish social media posts about the show. Analyzing social media posts related to a TV show can be beneficial for gaining insights about what happened during scenes of the show. This is a challenging task partly because a significant number of social media posts associated with a TV show or event may not clearly describe what happened during the event. In this work, we propose a method to predict social media posts (associated with scenes of a TV show) that are indicative of what transpired during the scenes of the show. We evaluate our method on social media (Twitter) posts associated with an episode of a popular TV show, Game of Thrones. We show that for each of the identified scenes, with high AUC’s, our method can predict posts that are indicative of what happened in a scene from those that are not-indicative. Based on Twitters policy, we will make the Tweeter ID’s of the Twitter posts used for this work publicly available.

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HashSet - A Dataset For Hashtag Segmentation
Prashant Kodali | Akshala Bhatnagar | Naman Ahuja | Manish Shrivastava | Ponnurangam Kumaraguru

Hashtag segmentation is the task of breaking a hashtag into its constituent tokens. Hashtags often encode the essence of user-generated posts, along with information like topic and sentiment, which are useful in downstream tasks. Hashtags prioritize brevity and are written in unique ways - transliterating and mixing languages, spelling variations, creative named entities. Benchmark datasets used for the hashtag segmentation task - STAN, BOUN - are small and extracted from a single set of tweets. However, datasets should reflect the variations in writing styles of hashtags and account for domain and language specificity, failing which the results will misrepresent model performance. We argue that model performance should be assessed on a wider variety of hashtags, and datasets should be carefully curated. To this end, we propose HashSet, a dataset comprising of: a) 1.9k manually annotated dataset; b) 3.3M loosely supervised dataset. HashSet dataset is sampled from a different set of tweets when compared to existing datasets and provides an alternate distribution of hashtags to build and validate hashtag segmentation models. We analyze the performance of SOTA models for Hashtag Segmentation, and show that the proposed dataset provides an alternate set of hashtags to train and assess models.

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Using Convolution Neural Network with BERT for Stance Detection in Vietnamese
Oanh Tran | Anh Cong Phung | Bach Xuan Ngo

Stance detection is the task of automatically eliciting stance information towards a specific claim made by a primary author. While most studies have been done for high-resource languages, this work is dedicated to a low-resource language, namely Vietnamese. In this paper, we propose an architecture using transformers to detect stances in Vietnamese claims. This architecture exploits BERT to extract contextual word embeddings instead of using traditional word2vec models. Then, these embeddings are fed into CNN networks to extract local features to train the stance detection model. We performed extensive comparison experiments to show the effectiveness of the proposed method on a public dataset1 Experimental results show that this proposed model outperforms the previous methods by a large margin. It yielded an accuracy score of 75.57% averaged on four labels. This sets a new SOTA result for future research on this interesting problem in Vietnamese.

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Annotation-Scheme Reconstruction for “Fake News” and Japanese Fake News Dataset
Taichi Murayama | Shohei Hisada | Makoto Uehara | Shoko Wakamiya | Eiji Aramaki

Fake news provokes many societal problems; therefore, there has been extensive research on fake news detection tasks to counter it. Many fake news datasets were constructed as resources to facilitate this task. Contemporary research focuses almost exclusively on the factuality aspect of the news. However, this aspect alone is insufficient to explain “fake news,” which is a complex phenomenon that involves a wide range of issues. To fully understand the nature of each instance of fake news, it is important to observe it from various perspectives, such as the intention of the false news disseminator, the harmfulness of the news to our society, and the target of the news. We propose a novel annotation scheme with fine-grained labeling based on detailed investigations of existing fake news datasets to capture these various aspects of fake news. Using the annotation scheme, we construct and publish the first Japanese fake news dataset. The annotation scheme is expected to provide an in-depth understanding of fake news. We plan to build datasets for both Japanese and other languages using our scheme. Our Japanese dataset is published at https://hkefka385.github.io/dataset/fakenews-japanese/.

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RoBERTuito: a pre-trained language model for social media text in Spanish
Juan Manuel Pérez | Damián Ariel Furman | Laura Alonso Alemany | Franco M. Luque

Since BERT appeared, Transformer language models and transfer learning have become state-of-the-art for natural language processing tasks. Recently, some works geared towards pre-training specially-crafted models for particular domains, such as scientific papers, medical documents, user-generated texts, among others. These domain-specific models have been shown to improve performance significantly in most tasks; however, for languages other than English, such models are not widely available. In this work, we present RoBERTuito, a pre-trained language model for user-generated text in Spanish, trained on over 500 million tweets. Experiments on a benchmark of tasks involving user-generated text showed that RoBERTuito outperformed other pre-trained language models in Spanish. In addition to this, our model has some cross-lingual abilities, achieving top results for English-Spanish tasks of the Linguistic Code-Switching Evaluation benchmark (LinCE) and also competitive performance against monolingual models in English Twitter tasks. To facilitate further research, we make RoBERTuito publicly available at the HuggingFace model hub together with the dataset used to pre-train it.

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Construction of Responsive Utterance Corpus for Attentive Listening Response Production
Koichiro Ito | Masaki Murata | Tomohiro Ohno | Shigeki Matsubara

In Japan, the number of single-person households, particularly among the elderly, is increasing. Consequently, opportunities for people to narrate are being reduced. To address this issue, conversational agents, e.g., communication robots and smart speakers, are expected to play the role of the listener. To realize these agents, this paper describes the collection of conversational responses by listeners that demonstrate attentive listening attitudes toward narrative speakers, and a method to annotate existing narrative speech with responsive utterances is proposed. To summarize, 148,962 responsive utterances by 11 listeners were collected in a narrative corpus comprising 13,234 utterance units. The collected responsive utterances were analyzed in terms of response frequency, diversity, coverage, and naturalness. These results demonstrated that diverse and natural responsive utterances were collected by the proposed method in an efficient and comprehensive manner. To demonstrate the practical use of the collected responsive utterances, an experiment was conducted, in which response generation timings were detected in narratives.

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Speak: A Toolkit Using Amazon Mechanical Turk to Collect and Validate Speech Audio Recordings
Christopher Song | David Harwath | Tuka Alhanai | James Glass

We present Speak, a toolkit that allows researchers to crowdsource speech audio recordings using Amazon Mechanical Turk (MTurk). Speak allows MTurk workers to submit speech recordings in response to a task prompt and stimulus (e.g. image, text excerpt, audio file) defined by researchers, a functionality that is not natively offered by MTurk at the time of writing this paper. Importantly, the toolkit employs numerous measures to ensure that speech recordings collected are of adequate quality, in order to avoid accepting unusable data and prevent abuse/fraud. Speak has demonstrated utility, having collected over 600,000 recordings to date. The toolkit is open-source and available for download.

Code-switching is a speech phenomenon occurring when a speaker switches language during a conversation. Despite the spontaneous nature of code-switching in conversational spoken language, most existing works collect code-switching data from read speech instead of spontaneous speech. ASCEND (A Spontaneous Chinese-English Dataset) is a high-quality Mandarin Chinese-English code-switching corpus built on spontaneous multi-turn conversational dialogue sources collected in Hong Kong. We report ASCEND’s design and procedure for collecting the speech data, including annotations. ASCEND consists of 10.62 hours of clean speech, collected from 23 bilingual speakers of Chinese and English. Furthermore, we conduct baseline experiments using pre-trained wav2vec 2.0 models, achieving a best performance of 22.69% character error rate and 27.05% mixed error rate.

This paper presents the results of an ongoing collaboration to develop an Arabic variety-independent romanization system that aims to homogenize and simplify the romanization of the Arabic script, and introduces an Arabic variety-independent WebMAUS service offering a free to use forced-alignment service fully integrated within the WebMAUS services. We present the rationale for developing such a system, highlighting the need for a detailed romanization system with graphemes corresponding to the phonemic short and long vowels/consonants in Arabic varieties. We describe how the acoustic model was created, followed by several hands-on recipes for applying the forced alignment webservice either online or programatically. Finally, we discuss some of the issues we faced during the development of the system.

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BembaSpeech: A Speech Recognition Corpus for the Bemba Language
Claytone Sikasote | Antonios Anastasopoulos

We present a preprocessed, ready-to-use automatic speech recognition corpus, BembaSpeech, consisting over 24 hours of read speech in the Bemba language, a written but low-resourced language spoken by over 30% of the population in Zambia. To assess its usefulness for training and testing ASR systems for Bemba, we explored different approaches; supervised pre-training (training from scratch), cross-lingual transfer learning from a monolingual English pre-trained model using DeepSpeech on the portion of the dataset and fine-tuning large scale self-supervised Wav2Vec2.0 based multilingual pre-trained models on the complete BembaSpeech corpus. From our experiments, the 1 billion XLS-R parameter model gives the best results. The model achieves a word error rate (WER) of 32.91%, results demonstrating that model capacity significantly improves performance and that multilingual pre-trained models transfers cross-lingual acoustic representation better than monolingual pre-trained English model on the BembaSpeech for the Bemba ASR. Lastly, results also show that the corpus can be used for building ASR systems for Bemba language.

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BehanceCC: A ChitChat Detection Dataset For Livestreaming Video Transcripts
Viet Lai | Amir Pouran Ben Veyseh | Franck Dernoncourt | Thien Nguyen

Livestreaming videos have become an effective broadcasting method for both video sharing and educational purposes. However, livestreaming videos contain a considerable amount of off-topic content (i.e., up to 50%) which introduces significant noises and data load to downstream applications. This paper presents BehanceCC, a new human-annotated benchmark dataset for off-topic detection (also called chitchat detection) in livestreaming video transcripts. In addition to describing the challenges of the dataset, our extensive experiments of various baselines reveal the complexity of chitchat detection for livestreaming videos and suggest potential future research directions for this task. The dataset will be made publicly available to foster research in this area.

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Adversarial Speech Generation and Natural Speech Recovery for Speech Content Protection
Sheng Li | Jiyi Li | Qianying Liu | Zhuo Gong

With the advent of the General Data Protection Regulation (GDPR) and increasing privacy concerns, the sharing of speech data is faced with significant challenges. Protecting the sensitive content of speech is the same important as the voiceprint. This paper proposes an effective speech content protection method by constructing a frame-by-frame adversarial speech generation system. We revisited the adversarial examples generating method in the recent machine learning field and selected the phonetic state sequence of sensitive speech for the adversarial examples generation. We build an adversarial speech collection. Moreover, based on the speech collection, we proposed a neural network-based frame-by-frame mapping method to recover the speech content by converting from the adversarial speech to the human speech. Experiment shows our proposed method can encode and recover any sensitive audio, and our method is easy to be conducted with publicly available resources of speech recognition technology.

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A new European Portuguese corpus for the study of Psychosis through speech analysis
Maria Forjó | Daniel Neto | Alberto Abad | HSofia Pinto | Joaquim Gago

Psychosis is a clinical syndrome characterized by the presence of symptoms such as hallucinations, thought disorder and disorganized speech. Several studies have used machine learning, combined with speech and natural language processing methods to aid in the diagnosis process of this disease. This paper describes the creation of the first European Portuguese corpus for the identification of the presence of speech characteristics of psychosis, which contains samples of 92 participants, 56 controls and 36 individuals diagnosed with psychosis and medicated. The corpus was used in a set of experiments that allowed identifying the most promising feature set to perform the classification: the combination of acoustic and speech metric features. Several classifiers were implemented to study which ones entailed the best performance depending on the task and feature set. The most promising results obtained for the entire corpus were achieved when identifying individuals with a Multi-Layer Perceptron classifier and reached an 87.5% accuracy. Focusing on the gender dependent results, the overall best results were 90.9% and 82.9% accuracy, for female and male subjects respectively. Lastly, the experiments performed lead us to conjecture that spontaneous speech presents more identifiable characteristics than read speech to differentiate healthy and patients diagnosed with psychosis.

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Investigating Inter- and Intra-speaker Voice Conversion using Audiobooks
Aghilas Sini | Damien Lolive | Nelly Barbot | Pierre Alain

Audiobook readers play with their voices to emphasize some text passages, highlight discourse changes or significant events, or in order to make listening easier and entertaining. A dialog is a central passage in audiobooks where the reader applies significant voice transformation, mainly prosodic modifications, to realize character properties and changes. However, these intra-speaker modifications are hard to reproduce with simple text-to-speech synthesis. The manner of vocalizing characters involved in a given story depends on the text style and differs from one speaker to another. In this work, this problem is investigated through the prism of voice conversion. We propose to explore modifying the narrator’s voice to fit the context of the story, such as the character who is speaking, using voice conversion. To this end, two complementary experiments are designed: the first one aims to assess the quality of our Phonetic PosteriorGrams (PPG)-based voice conversion system using parallel data. Subjective evaluations with naive raters are conducted to estimate the quality of the signal generated and the speaker similarity. The second experiment applies an intra-speaker voice conversion, considering narration passages and direct speech passages as two distinct speakers. Data are then nonparallel and the dissimilarity between character and narrator is subjectively measured.

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Multilingual Transfer Learning for Children Automatic Speech Recognition
Thomas Rolland | Alberto Abad | Catia Cucchiarini | Helmer Strik

Despite recent advances in automatic speech recognition (ASR), the recognition of children’s speech still remains a significant challenge. This is mainly due to the high acoustic variability and the limited amount of available training data. The latter problem is particularly evident in languages other than English, which are usually less-resourced. In the current paper, we address children ASR in a number of less-resourced languages by combining several small-sized children speech corpora from these languages. In particular, we address the following research question: Does a novel two-step training strategy in which multilingual learning is followed by language-specific transfer learning outperform conventional single language/task training for children speech, as well as multilingual and transfer learning alone? Based on previous experimental results with English, we hypothesize that multilingual learning provides a better generalization of the underlying characteristics of children’s speech. Our results provide a positive answer to our research question, by showing that using transfer learning on top of a multilingual model for an unseen language outperforms conventional single language-specific learning.

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BehanceQA: A New Dataset for Identifying Question-Answer Pairs in Video Transcripts
Amir Pouran Ben Veyseh | Viet Lai | Franck Dernoncourt | Thien Nguyen

Question-Answer (QA) is one of the effective methods for storing knowledge which can be used for future retrieval. As such, identifying mentions of questions and their answers in text is necessary for a knowledge construction and retrieval systems. In the literature, QA identification has been well studied in the NLP community. However, most of the prior works are restricted to formal written documents such as papers or websites. As such, Questions and Answers that are presented in informal/noisy documents have not been adequately studied. One of the domains that can significantly benefit from QA identification is the domain of livestreaming video transcripts that involve abundant QA pairs to provide valuable knowledge for future users and services. Since video transcripts are often transcribed automatically for scale, they are prone to errors. Combined with the informal nature of discussion in a video, prior QA identification systems might not be able to perform well in this domain. To enable comprehensive research in this domain, we present a large-scale QA identification dataset annotated by human over transcripts of 500 hours of streamed videos. We employ Behance.net to collect the videos and their automatically obtained transcripts. Furthermore, we conduct extensive analysis on the annotated dataset to understand the complexity of QA identification for livestreaming video transcripts. Our experiments show that the annotated dataset presents unique challenges for existing methods and more research is necessary to explore more effective methods. The dataset and the models developed in this work will be publicly released for future research.

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Bidirectional Skeleton-Based Isolated Sign Recognition using Graph Convolutional Networks
Konstantinos M. Dafnis | Evgenia Chroni | Carol Neidle | Dimitri Metaxas

To improve computer-based recognition from video of isolated signs from American Sign Language (ASL), we propose a new skeleton-based method that involves explicit detection of the start and end frames of signs, trained on the ASLLVD dataset; it uses linguistically relevant parameters based on the skeleton input. Our method employs a bidirectional learning approach within a Graph Convolutional Network (GCN) framework. We apply this method to the WLASL dataset, but with corrections to the gloss labeling to ensure consistency in the labels assigned to different signs; it is important to have a 1-1 correspondence between signs and text-based gloss labels. We achieve a success rate of 77.43% for top-1 and 94.54% for top-5 using this modified WLASL dataset. Our method, which does not require multi-modal data input, outperforms other state-of-the-art approaches on the same modified WLASL dataset, demonstrating the importance of both attention to the start and end frames of signs and the use of bidirectional data streams in the GCNs for isolated sign recognition.

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Deep learning-based end-to-end spoken language identification system for domain-mismatched scenario
Woohyun Kang | Md Jahangir Alam | Abderrahim Fathan

Domain mismatch is a critical issue when it comes to spoken language identification. To overcome the domain mismatch problem, we have applied several architectures and deep learning strategies which have shown good results in cross-domain speaker verification tasks to spoken language identification. Our systems were evaluated on the Oriental Language Recognition (OLR) Challenge 2021 Task 1 dataset, which provides a set of cross-domain language identification trials. Among our experimented systems, the best performance was achieved by using the mel frequency cepstral coefficient (MFCC) and pitch features as input and training the ECAPA-TDNN system with a flow-based regularization technique, which resulted in a Cavg of 0.0631 on the OLR 2021 progress set.

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Handwritten Character Generation using Y-Autoencoder for Character Recognition Model Training
Tomoki Kitagawa | Chee Siang Leow | Hiromitsu Nishizaki

It is well-known that the deep learning-based optical character recognition (OCR) system needs a large amount of data to train a high-performance character recognizer. However, it is costly to collect a large amount of realistic handwritten characters. This paper introduces a Y-Autoencoder (Y-AE)-based handwritten character generator to generate multiple Japanese Hiragana characters with a single image to increase the amount of data for training a handwritten character recognizer. The adaptive instance normalization (AdaIN) layer allows the generator to be trained and generate handwritten character images without paired-character image labels. The experiment shows that the Y-AE could generate Japanese character images then used to train the handwritten character recognizer, producing an F1-score improved from 0.8664 to 0.9281. We further analyzed the usefulness of the Y-AE-based generator with shape images, out-of-character (OOC) images, which have different character images styles in model training. The result showed that the generator could generate a handwritten image with a similar style to that of the input character.

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Attention-Focused Adversarial Training for Robust Temporal Reasoning
Lis Kanashiro Pereira

We propose an enhanced adversarial training algorithm for fine-tuning transformer-based language models (i.e., RoBERTa) and apply it to the temporal reasoning task. Current adversarial training approaches for NLP add the adversarial perturbation only to the embedding layer, ignoring the other layers of the model, which might limit the generalization power of adversarial training. Instead, our algorithm searches for the best combination of layers to add the adversarial perturbation. We add the adversarial perturbation to multiple hidden states or attention representations of the model layers. Adding the perturbation to the attention representations performed best in our experiments. Our model can improve performance on several temporal reasoning benchmarks, and establishes new state-of-the-art results.

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PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter
Kornraphop Kawintiranon | Lisa Singh

Transformer-based models have become the state-of-the-art for numerous natural language processing (NLP) tasks, especially for noisy data sets, including social media posts. For example, BERTweet, pre-trained RoBERTa on a large amount of Twitter data, has achieved state-of-the-art results on several Twitter NLP tasks. We argue that it is not only important to have general pre-trained models for a social media platform, but also domain-specific ones that better capture domain-specific language context. Domain-specific resources are not only important for NLP tasks associated with a specific domain, but they are also useful for understanding language differences across domains. One domain that receives a large amount of attention is politics, more specifically political elections. Towards that end, we release PoliBERTweet, a pre-trained language model trained from BERTweet on over 83M US 2020 election-related English tweets. While the construction of the resource is fairly straightforward, we believe that it can be used for many important downstream tasks involving language, including political misinformation analysis and election public opinion analysis. To show the value of this resource, we evaluate PoliBERTweet on different NLP tasks. The results show that our model outperforms general-purpose language models in domain-specific contexts, highlighting the value of domain-specific models for more detailed linguistic analysis. We also extend other existing language models with a sample of these data and show their value for presidential candidate stance detection, a context-specific task. We release PoliBERTweet and these other models to the community to advance interdisciplinary research related to Election 2020.

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Modeling the Impact of Syntactic Distance and Surprisal on Cross-Slavic Text Comprehension
Irina Stenger | Philip Georgis | Tania Avgustinova | Bernd Möbius | Dietrich Klakow

We focus on the syntactic variation and measure syntactic distances between nine Slavic languages (Belarusian, Bulgarian, Croatian, Czech, Polish, Slovak, Slovene, Russian, and Ukrainian) using symmetric measures of insertion, deletion and movement of syntactic units in the parallel sentences of the fable “The North Wind and the Sun”. Additionally, we investigate phonetic and orthographic asymmetries between selected languages by means of the information theoretical notion of surprisal. Syntactic distance and surprisal are, thus, considered as potential predictors of mutual intelligibility between related languages. In spoken and written cloze test experiments for Slavic native speakers, the presented predictors will be validated as to whether variations in syntax lead to a slower or impeded intercomprehension of Slavic texts.

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BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification
Vinura Dhananjaya | Piyumal Demotte | Surangika Ranathunga | Sanath Jayasena

This research provides the first comprehensive analysis of the performance of pre-trained language models for Sinhala text classification. We test on a set of different Sinhala text classification tasks and our analysis shows that out of the pre-trained multilingual models that include Sinhala (XLM-R, LaBSE, and LASER), XLM-R is the best model by far for Sinhala text classification. We also pre-train two RoBERTa-based monolingual Sinhala models, which are far superior to the existing pre-trained language models for Sinhala. We show that when fine-tuned, these pre-trained language models set a very strong baseline for Sinhala text classification and are robust in situations where labeled data is insufficient for fine-tuning. We further provide a set of recommendations for using pre-trained models for Sinhala text classification. We also introduce new annotated datasets useful for future research in Sinhala text classification and publicly release our pre-trained models.

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Pre-training and Evaluating Transformer-based Language Models for Icelandic
Jón Friðrik Daðason | Hrafn Loftsson

In this paper, we evaluate several Transformer-based language models for Icelandic on four downstream tasks: Part-of-Speech tagging, Named Entity Recognition. Dependency Parsing, and Automatic Text Summarization. We pre-train four types of monolingual ELECTRA and ConvBERT models and compare our results to a previously trained monolingual RoBERTa model and the multilingual mBERT model. We find that the Transformer models obtain better results, often by a large margin, compared to previous state-of-the-art models. Furthermore, our results indicate that pre-training larger language models results in a significant reduction in error rates in comparison to smaller models. Finally, our results show that the monolingual models for Icelandic outperform a comparably sized multilingual model.

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bib (full) Proceedings of the BUCC Workshop within LREC 2022

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Proceedings of the BUCC Workshop within LREC 2022
Reinhard Rapp | Pierre Zweigenbaum | Serge Sharoff

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Evaluating Monolingual and Crosslingual Embeddings on Datasets of Word Association Norms
Trina Kwong | Emmanuele Chersoni | Rong Xiang

In free word association tasks, human subjects are presented with a stimulus word and are then asked to name the first word (the response word) that comes up to their mind. Those associations, presumably learned on the basis of conceptual contiguity or similarity, have attracted for a long time the attention of researchers in linguistics and cognitive psychology, since they are considered as clues about the internal organization of the lexical knowledge in the semantic memory. Word associations data have also been used to assess the performance of Vector Space Models for English, but evaluations for other languages have been relatively rare so far. In this paper, we introduce word associations datasets for Italian, Spanish and Mandarin Chinese by extracting data from the Small World of Words project, and we propose two different tasks inspired by the previous literature. We tested both monolingual and crosslingual word embeddings on the new datasets, showing that they perform similarly in the evaluation tasks.

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About Evaluating Bilingual Lexicon Induction
Martin Laville | Emmanuel Morin | Phillippe Langlais

With numerous new methods proposed recently, the evaluation of Bilingual Lexicon Induction have been quite hazardous and inconsistent across works. Some studies proposed some guidance to sanitize this; yet, they are not necessarily followed by practitioners. In this study, we try to gather these different recommendations and add our owns, with the aim to propose an unified evaluation protocol. We further show that the easiness of a benchmark while being correlated to the proximity of the language pairs being considered, is even more conditioned on the graphical similarities within the test word pairs.

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Don’t Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings
Silvia Severini | Viktor Hangya | Masoud Jalili Sabet | Alexander Fraser | Hinrich Schütze

Bilingual Word Embeddings (BWEs) are one of the cornerstones of cross-lingual transfer of NLP models. They can be built using only monolingual corpora without supervision leading to numerous works focusing on unsupervised BWEs. However, most of the current approaches to build unsupervised BWEs do not compare their results with methods based on easy-to-access cross-lingual signals. In this paper, we argue that such signals should always be considered when developing unsupervised BWE methods. The two approaches we find most effective are: 1) using identical words as seed lexicons (which unsupervised approaches incorrectly assume are not available for orthographically distinct language pairs) and 2) combining such lexicons with pairs extracted by matching romanized versions of words with an edit distance threshold. We experiment on thirteen non-Latin languages (and English) and show that such cheap signals work well and that they outperform using more complex unsupervised methods on distant language pairs such as Chinese, Japanese, Kannada, Tamil, and Thai. In addition, they are even competitive with the use of high-quality lexicons in supervised approaches. Our results show that these training signals should not be neglected when building BWEs, even for distant languages.

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Building Domain-specific Corpora from the Web: the Case of European Digital Service Infrastructures
Rik van Noord | Cristian García-Romero | Miquel Esplà-Gomis | Leopoldo Pla Sempere | Antonio Toral

An important goal of the MaCoCu project is to improve EU-specific NLP systems that concern their Digital Service Infrastructures (DSIs). In this paper we aim at boosting the creation of such domain-specific NLP systems. To do so, we explore the feasibility of building an automatic classifier that allows to identify which segments in a generic (potentially parallel) corpus are relevant for a particular DSI. We create an evaluation data set by crawling DSI-specific web domains and then compare different strategies to build our DSI classifier for text in three languages: English, Spanish and Dutch. We use pre-trained (multilingual) language models to perform the classification, with zero-shot classification for Spanish and Dutch. The results are promising, as we are able to classify DSIs with between 70 and 80% accuracy, even without in-language training data. A manual annotation of the data revealed that we can also find DSI-specific data on crawled texts from general web domains with reasonable accuracy. We publicly release all data, predictions and code, as to allow future investigations in whether exploiting this DSI-specific data actually leads to improved performance on particular applications, such as machine translation.

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Multilingual Comparative Analysis of Deep-Learning Dependency Parsing Results Using Parallel Corpora
Diego Alves | Marko Tadić | Božo Bekavac

This article presents a comparative analysis of dependency parsing results for a set of 16 languages, coming from a large variety of linguistic families and genera, whose parallel corpora were used to train a deep-learning tool. Results are analyzed in comparison to an innovative way of classifying languages concerning the head directionality parameter used to perform a quantitative syntactic typological classification of languages. It has been shown that, despite using parallel corpora, there is a large discrepancy in terms of LAS results. The obtained results show that this heterogeneity is mainly due to differences in the syntactic structure of the selected languages, where Indo-European ones, especially Romance languages, have the best scores. It has been observed that the differences in the size of the representation of each language in the language model used by the deep-learning tool also play a major role in the dependency parsing efficacy. Other factors, such as the number of dependency parsing labels may also have an influence on results with more complex labeling systems such as the Polish language.

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CUNI Submission to the BUCC 2022 Shared Task on Bilingual Term Alignment
Borek Požár | Klára Tauchmanová | Kristýna Neumannová | Ivana Kvapilíková | Ondřej Bojar

We present our submission to the BUCC Shared Task on bilingual term alignment in comparable specialized corpora. We devised three approaches using static embeddings with post-hoc alignment, the Monoses pipeline for unsupervised phrase-based machine translation, and contextualized multilingual embeddings. We show that contextualized embeddings from pretrained multilingual models lead to similar results as static embeddings but further improvement can be achieved by task-specific fine-tuning. Retrieving term pairs from the running phrase tables of the Monoses systems can match this enhanced performance and leads to an average precision of 0.88 on the train set.

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Challenges of Building Domain-Specific Parallel Corpora from Public Administration Documents
Filip Klubička | Lorena Kasunić | Danijel Blazsetin | Petra Bago

PRINCIPLE was a Connecting Europe Facility (CEF)-funded project that focused on the identification, collection and processing of language resources (LRs) for four European under-resourced languages (Croatian, Icelandic, Irish and Norwegian) in order to improve translation quality of eTranslation, an online machine translation (MT) tool provided by the European Commission. The collected LRs were used for the development of neural MT engines in order to verify the quality of the resources. For all four languages, a total of 66 LRs were collected and made available on the ELRC-SHARE repository under various licenses. For Croatian, we have collected and published 20 LRs: 19 parallel corpora and 1 glossary. The majority of data is in the general domain (72 % of translation units), while the rest is in the eJustice (23 %), eHealth (3 %) and eProcurement (2 %) Digital Service Infrastructures (DSI) domains. The majority of the resources were for the Croatian-English language pair. The data was donated by six data contributors from the public as well as private sector. In this paper we present a subset of 13 Croatian LRs developed based on public administration documents, which are all made freely available, as well as challenges associated with the data collection, cleaning and processing.

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Setting Up Bilingual Comparable Corpora with Non-Contemporary Languages
Helena Bermudez Sabel | Francesca Dell’Oro | Cyrielle Montrichard | Corinne Rossari

This paper presents the project “Les corpora latins et français: une fabrique pour l’accès à la représentation des connaissances” (Latin and French Corpora: a Factory For Accessing Knowledge Representation) whose focus is the study of modality in both Latin and French by means of multi-genre, diachronic comparable corpora. The setting up of such corpora involves a number of conceptualisation challenges, in particular with regard to how to compare two asynchronous textual productions corresponding to different cultural frameworks. In this paper we outline the rationale of designing comparable corpora to explore our research questions and then focus on some of the issues that arise when comparing different diachronic spans of Latin and French. We also explain how these issues were dealt with, thus providing some grounds upon which other projects could build their methodology.

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Fusion of linguistic, neural and sentence-transformer features for improved term alignment
Andraz Repar | Senja Pollak | Matej Ulčar | Boshko Koloski

Crosslingual terminology alignment task has many practical applications. In this work, we propose an aligning method for the shared task of the 15th Workshop on Building and Using Comparable Corpora. Our method combines several different approaches into one cohesive machine learning model, based on SVM. From shared-task specific and external sources, we crafted four types of features: cognate-based, dictionary-based, embedding-based, and combined features, which combine aspects of the other three types. We added a post-processing re-scoring method, which reducess the effect of hubness, where some terms are nearest neighbours of many other terms. We achieved the average precision score of 0.833 on the English-French training set of the shared task.

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bib (full) Proceedings of the 4th Celtic Language Technology Workshop within LREC2022

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Proceedings of the 4th Celtic Language Technology Workshop within LREC2022
Theodorus Fransen | William Lamb | Delyth Prys

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Multilingual Abstract Meaning Representation for Celtic Languages
Johannes Heinecke | Anastasia Shimorina

Deep Semantic Parsing into Abstract Meaning Representation (AMR) graphs has reached a high quality with neural-based seq2seq approaches. However, the training corpus for AMR is only available for English. Several approaches to process other languages exist, but only for high resource languages. We present an approach to create a multilingual text-to-AMR model for three Celtic languages, Welsh (P-Celtic) and the closely related Irish and Scottish-Gaelic (Q-Celtic). The main success of this approach are underlying multilingual transformers like mT5. We finally show that machine translated test corpora unfairly improve the AMR evaluation for about 1 or 2 points (depending on the language).

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Diachronic Parsing of Pre-Standard Irish
Kevin Scannell

Irish underwent a major spelling standardization in the 1940’s and 1950’s, and as a result it can be challenging to apply language technologies designed for the modern language to older, “pre-standard” texts. Lemmatization, tagging, and parsing of these pre-standard texts play an important role in a number of applications, including the lexicographical work on Foclóir Stairiúil na Gaeilge, a historical dictionary of Irish covering the period from 1600 to the present. We have two main goals in this paper. First, we introduce a small benchmark corpus containing just over 3800 words, annotated according to the Universal Dependencies guidelines and covering a range of dialects and time periods since 1600. Second, we establish baselines for lemmatization, tagging, and dependency parsing on this corpus by experimenting with a variety of machine learning approaches.

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Creation of an Evaluation Corpus and Baseline Evaluation Scores for Welsh Text Summarisation
Mahmoud El-Haj | Ignatius Ezeani | Jonathan Morris | Dawn Knight

As part of the effort to increase the availability of Welsh digital technology, this paper introduces the first human vs metrics Welsh summarisation evaluation results and dataset, which we provide freely for research purposes to help advance the work on Welsh summarisation. The system summaries were created using an extractive graph-based Welsh summariser. The system summaries were evaluated by both human and a range of ROUGE metric variants (e.g. ROUGE 1, 2, L and SU4). The summaries and evaluation results will serve as benchmarks for the development of summarisers and evaluation metrics in other minority language contexts.

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CLILSTORE.EU - A Multilingual online CLIL platform
Caoimhín Ó Dónaill

CLILSTORE.EU is an open educational resource (OER) that was created by the Erasmus + funded CLIL Open Online Learning (COOL) project which ran from 2018-2021. The project consortium included teaching practitioners from the primary, secondary, tertiary and vocational sectors who each brought their influence to bear on the design and functionality of the OER and subsequently evaluated its development within the learning contexts of their respective sectors. CLILSTORE.EU serves as both an authoring and sharing platform where multimedia learning materials can be created and accessed. Its name comprises the acronym CLIL, owing to its particular suitablity as a tool to support the Content and Language Integrated Learning methodology (Marsh, D. (ed.), 2002). The main educational aims of the OER are to provide teachers with a relatively straightforward means of creating reusable, multimodal learning units that can be used within the classroom or via remote learning to underpin and scaffold the delivery of curricular content in any subject area, especially in contexts where learners are acquiring new knowledge through the medium of a second or additional language. The following account details recent development work on the OER’s functionality and usability and presents case studies showing how it can benefit Celtic languages.

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Evaluation of Three Welsh Language POS Taggers
Gruffudd Prys | Gareth Watkins

In this paper we describe our quantitative and qualitative evaluation of three Welsh language Part of Speech (POS) taggers. Following an introductory section, we explore some of the issues which face POS taggers, discuss the state of the art in English language tagging, and describe the three Welsh language POS taggers that will be evaluated in this paper, namely WNLT2, CyTag and TagTeg. In section 3 we describe the challenges involved in evaluating POS taggers which make use of different tagsets, and introduce our mapping of the taggers’ individual tagsets to an Intermediate Tagset used to facilitate their comparative evaluation. Section 4 introduces our benchmarking corpus as an important component of our methodology. In section 5 we describe how the inconsistencies in text tokenization between the different taggers present an issue when undertaking such evaluations, and discuss the method used to overcome this complication. Section 6 illustrates how we annotated the benchmark corpus, while section 7 describes the scoring method used. Section 8 provides an in-depth analysis of the results, and a summary of the work is presented in the conclusion found in section 9. Keywords: POS Tagger, Welsh, Evaluation, Machine Learning

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Iterated Dependencies in a Breton treebank and implications for a Categorial Dependency Grammar
Annie Foret | Denis Béchet | Valérie Bellynck

Categorial Dependency Grammars (CDG) are computational grammars for natural language processing, defining dependency structures. They can be viewed as a formal system, where types are attached to words, combining the classical categorial grammars’ elimination rules with valency pairing rules able to define discontinuous (non-projective) dependencies. Algorithms have been proposed to infer grammars in this class from treebanks, with respect to Mel’čuk principles. We consider this approach with experiments on Breton. We focus in particular on ”repeatable dependencies” (iterated) and their patterns. A dependency d is iterated in a dependency structure if some word in this structure governs several other words through dependency d. We illustrate this approach with data in the universal dependencies format and dependency patterns written in Grew (a graph rewriting tool dedicated to applications in natural Language Processing).

This paper describes ÉIST, automatic speech recogniser for Irish, developed as part of the ongoing ABAIR initiative, combining (1) acoustic models, (2) pronunciation lexicons and (3) language models into a hybrid system. A priority for now is a system that can deal with the multiple diverse native-speaker dialects. Consequently, (1) was built using predominately native-speaker speech, which included earlier recordings used for synthesis development as well as more diverse recordings obtained using the MíleGlór platform. The pronunciation variation across the dialects is a particular challenge in the development of (2) and is explored by testing both Trans-dialect and Multi-dialect letter-to-sound rules. Two approaches to language modelling (3) are used in the hybrid system, a simple n-gram model and recurrent neural network lattice rescoring, the latter garnering impressive performance improvements. The system is evaluated using a test set that is comprised of both native and non-native speakers, which allows for some inferences to be made on the performance of the system on both cohorts.

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Development and Evaluation of Speech Recognition for the Welsh Language
Dewi Jones

This paper reports on ongoing work on developing and evaluating speech recognition models for the Welsh language using data from the Common Voice project and two popular open development kits – HuggingFace wav2vec2 and coqui STT. Activities for ensuring the growth and improvement of the Welsh Common Voice dataset are described. Two applications have been developed – a voice assistant and an online transcription service that allow users and organisations to use the new models in a practical and useful context, but which have also helped source additional test data for better evaluation of recognition accuracy and establishing the optimal selection and configurations of models. Test results suggest that in transcription good accuracy can be achieved for read speech, but further data and research is required for improving recognition results of freely spoken formal and informal speech. Meanwhile a limited domain language model provides excellent accuracy for a voice assistant. All code, data and models produced from this work are freely available.

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Handwriting recognition for Scottish Gaelic
William Lamb | Beatrice Alex | Mark Sinclair

Like most other minority languages, Scottish Gaelic has limited tools and resources available for Natural Language Processing research and applications. These limitations restrict the potential of the language to participate in modern speech technology, while also restricting research in fields such as corpus linguistics and the Digital Humanities. At the same time, Gaelic has a long written history, is well-described linguistically, and is unusually well-supported in terms of potential NLP training data. For instance, archives such as the School of Scottish Studies hold thousands of digitised recordings of vernacular speech, many of which have been transcribed as paper-based, handwritten manuscripts. In this paper, we describe a project to digitise and recognise a corpus of handwritten narrative transcriptions, with the intention of re-purposing it to develop a Gaelic speech recognition system.

In this paper, we present the Irish language learning platform, An Sc ́eala ́ı, an intelligent Computer-Assisted Language Learning (iCALL) system which incorporates speech and language technologies in ways that promote the holistic development of the language skills - writing, listening, reading, and speaking. The technologies offer the advantage of extensive feedback in spoken and written form, enabling learners to improve their production. The system works equally as a classroom-based tool and as a standalone platform for the autonomous learner. Given the key role of education for the transmission of all the Celtic languages, it is vital that digital technologies be harnessed to maximise the effectiveness of language teaching/learning. An Scéalaí has been used by large numbers of learners and teachers and has received very positive feedback. It is built as a modular system which allows existing and newly emerging technologies to be readily integrated, even if those technologies are still in development phase. The architecture is largely language-independent, and as an open-source system, it is hoped that it can be usefully deployed in other Celtic languages.

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Cipher – Faoi Gheasa: A Game-with-a-Purpose for Irish
Elaine Uí Dhonnchadha | Monica Ward | Liang Xu

This paper describes Cipher – Faoi Gheasa, a ‘game with a purpose’ designed to support the learning of Irish in a fun and enjoyable way. The aim of the game is to promote language ‘noticing’ and to combine the benefits of reading with the enjoyment of computer game playing, in a pedagogically beneficial way. In this paper we discuss pedagogical challenges for Irish, the development of measures for the selection and ranking of reading materials, as well as initial results of game evaluation. Overall user feedback is positive and further testing and development is envisaged.

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Towards Coreference Resolution for Early Irish
Mark Darling | Marieke Meelen | David Willis

In this article, we present an outline of some of the issues involved in developing a semi-supervised procedure for coreference resolution for early Irish as part of a wider enterprise to create a parsed corpus of historical Irish with enriched annotation for information structure and anaphoric coreference. We outline the ways in which existing resources, notably the POMIC historical Irish corpus and the Cesax annotation algorithm, have had to be adapted, the first to provide suitable input for coreference resolution, the second to cope with specific aspects of early Irish grammar. We also outline features of a part-of-speech tagger that we have developed for early Irish as part of the first task and with a view to expanding the size of the future corpus.

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Use of Transformer-Based Models for Word-Level Transliteration of the Book of the Dean of Lismore
Edward Gow-Smith | Mark McConville | William Gillies | Jade Scott | Roibeard Ó Maolalaigh

The Book of the Dean of Lismore (BDL) is a 16th-century Scottish Gaelic manuscript written in a non-standard orthography. In this work, we outline the problem of transliterating the text of the BDL into a standardised orthography, and perform exploratory experiments using Transformer-based models for this task. In particular, we focus on the task of word-level transliteration, and achieve a character-level BLEU score of 54.15 with our best model, a BART architecture pre-trained on the text of Scottish Gaelic Wikipedia and then fine-tuned on around 2,000 word-level parallel examples. Our initial experiments give promising results, but we highlight the shortcomings of our model, and discuss directions for future work.

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Introducing the National Corpus of Irish Project
Mícheál Ó Meachair | Úna Bhreathnach | Gearóid Ó Cleircín

This paper introduces the National Corpus of Irish, an initiative to develop a large national corpus of written and spoken contemporary Irish as well as related specialised corpora. The newly-compiled corpora will be hosted at corpas.ie, in what will become a hub for corpus-based research on the Irish language. Users will be able to search the corpora and download data generated during the project from the corpas.ie website and appropriate third-party repositories. Corpus 1 will be a balanced general-purpose corpus containing c.155m words. Corpus 2 will be a written corpus consisting of c100m words. Corpus 3 will be a spoken corpus containing 6.5m words. Corpus 4 will be a monitor corpus with a target size of 1m words per year from 2000 onwards. Token, lemma, and n-gram frequency lists will be published at regular intervals on the project website, and language models will be published there and on other appropriate platforms during the course of the project. This paper focuses on the background and crucial scoping stage of the project, and examines user needs as identified in a survey of potential users.

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BU-TTS: An Open-Source, Bilingual Welsh-English, Text-to-Speech Corpus
Stephen Russell | Dewi Jones | Delyth Prys

This paper presents the design, collection and verification of a bilingual text-to-speech synthesis corpus for Welsh and English. The ever expanding voice collection currently contains almost 10 hours of recordings from a bilingual, phonetically balanced text corpus. The speakers consist of a professional voice actor and three amateur contributors, with male and female accents from north and south Wales. This corpus provides audio-text pairs for building and training high-quality bilingual Welsh-English neural based TTS systems. We describe the process by which we created a phonetically balanced prompt set and the challenges of attempting to collate such a dataset during the COVID-19 pandemic. Our initial findings in validating the corpus via the implementation of a state-of-the-art TTS models are presented. This corpus represents the first open-source Welsh language corpus large enough to capitalise on neural TTS architectures.

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Developing Automatic Speech Recognition for Scottish Gaelic
Lucy Evans | William Lamb | Mark Sinclair | Beatrice Alex

This paper discusses our efforts to develop a full automatic speech recognition (ASR) system for Scottish Gaelic, starting from a point of limited resource. Building ASR technology is important for documenting and revitalising endangered languages; it enables existing resources to be enhanced with automatic subtitles and transcriptions, improves accessibility for users, and, in turn, encourages continued use of the language. In this paper, we explain the many difficulties faced when collecting minority language data for speech recognition. A novel cross-lingual approach to the alignment of training data is used to overcome one such difficulty, and in this way we demonstrate how majority language resources can bootstrap the development of lower-resourced language technology. We use the Kaldi speech recognition toolkit to develop several Gaelic ASR systems, and report a final WER of 26.30%. This is a 9.50% improvement on our original model.

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Handwritten Text Recognition (HTR) for Irish-Language Folklore
Brian Ó Raghallaigh | Andrea Palandri | Críostóir Mac Cárthaigh

In this paper we present our method for digitising a large collection of handwritten Irish-language texts as part of a project to mine information from a large corpus of Irish and Scottish Gaelic folktales. The handwritten texts form part of the Main Manuscript Collection of the National Folklore Collection of Ireland and contain handwritten transcriptions of oral folklore collected in Ireland in the 20th century. With the goal of creating a large text corpus of the Irish-language folktales contained within this collection, our method involves scanning the pages of the physical volumes and digitising the text on these pages using Transkribus, a platform for the recognition of historical documents. Given the nature of the collection, the approach we have taken involves the creation of individual text recognition models for multiple collectors’ hands. Doing it this way was motivated by the fact that a relatively small number of collectors contributed the bulk of the material, while the differences between each collector in terms of style, layout and orthography were difficult to reconcile within a single handwriting model. We present our preliminary results along with a discussion on the viability of using crowdsourced correction to improve our HTR models.

This paper describes the prototype development of an Alternative and Augmentative Communication (AAC) system for the Irish language. This system allows users to communicate using the ABAIR synthetic voices, by selecting a series of words or images. Similar systems are widely available in English and are often used by autistic people, as well as by people with Cerebral Palsy, Alzheimer’s and Parkinson’s disease. A dual-pronged approach to development has been adopted: this involves (i) the initial short-term prototype development that targets the immediate needs of specific users, as well as considerations for (ii) the longer term development of a bilingual AAC system which will suit a broader range of users with varying linguistic backgrounds, age ranges and needs. This paper described the design considerations and the implementation steps in the current system. Given the substantial differences in linguistic structures in Irish and English, the development of a bilingual system raises many research questions and avenues for future development.

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bib (full) Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-10)

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Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-10)
Piotr Banski | Adrien Barbaresi | Simon Clematide | Marc Kupietz | Harald Lüngen

Following the successful creation of a national representative corpus of contemporary Romanian language, we turned our attention to the social media text, as present in micro-blogging platforms. In this paper, we present the current activities as well as the challenges faced when trying to apply existing tools (for both annotation and indexing) to a Romanian language micro-blogging corpus. These challenges are encountered at all annotation levels, including tokenization, and at the indexing stage. We consider that existing tools for Romanian language processing must be adapted to recognize features such as emoticons, emojis, hashtags, unusual abbreviations, elongated words (commonly used for emphasis in micro-blogging), multiple words joined together (within oroutside hashtags), and code-mixed text.

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Exhaustive Indexing of PubMed Records with Medical Subject Headings
Modest von Korff

With fourteen million publication records the PubMed database is one of the largest repositories in medical science. Analysing this database to relate biological targets to diseases is an important task in pharmaceutical research. We developed a software tool, MeSHTreeIndexer, for indexing the PubMed medical literature with disease terms. The disease terms were taken from the Medical Subject Heading (MeSH) Terms compiled by the National Institutes of Health (NIH) of the US. In a first semi-automatic step we identified about 5’900 terms as disease related. The MeSH terms contain so-called entry points that are synonymously used for the terms. We created an inverted index for these 5’900 MeSH terms and their 58’000 entry points. From the PubMed database fourteen million publication records were stored in Lucene. These publication records were tagged by the inverted MeSH term index. In this contribution we demonstrate that our approach provided a significant higher enrichment in MeSH terms than the indexing of the PubMed records by the NIH themselves. Manual control proved that our enrichment is meaningful. Our software was written in Java and is available as open source.

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UDeasy: a Tool for Querying Treebanks in CoNLL-U Format
Luca Brigada Villa

Many tools are available to query a dependency treebank, but they require the users to know a query language. In this paper I present UDeasy, an application whose main goal is to allow the users to easily query and extract patterns from a dependency treebank in CoNLL-U format.

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Matrix and Double-Array Representations for Efficient Finite State Tokenization
Nils Diewald

This paper presents an algorithm and implementation for efficient tokenization of space-delimited languages based on a deterministic finite state automaton. Two representations of the underlying data structure are presented and a model implementation for German is compared with state-of-the-art approaches. The presented solution is faster than other tools while maintaining comparable quality.

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Count-Based and Predictive Language Models for Exploring DeReKo
Peter Fankhauser | Marc Kupietz

We present the use of count-based and predictive language models for exploring language use in the German Reference Corpus DeReKo. For collocation analysis along the syntagmatic axis we employ traditional association measures based on co-occurrence counts as well as predictive association measures derived from the output weights of skipgram word embeddings. For inspecting the semantic neighbourhood of words along the paradigmatic axis we visualize the high dimensional word embeddings in two dimensions using t-stochastic neighbourhood embeddings. Together, these visualizations provide a complementary, explorative approach to analysing very large corpora in addition to corpus querying. Moreover, we discuss count-based and predictive models w.r.t. scalability and maintainability in very large corpora.

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“The word expired when that world awoke.” New Challenges for Research with Large Text Corpora and Corpus-Based Discourse Studies in Totalitarian Times
Hanno Biber

In the following poster proposal a report will be given on the prospects of a promising corpus project initiated by one of the large digital text corpora hosted by the Austrian Academy of Sciences. First, the resources of the AAC-Austrian Academy Corpus, that has been founded in 2001, which is one of the very valuable examples of digital diachronic text corpora suitable for corpus-based discourse studies and lexicography based upon historical sources, can be used as a basis for trying to answer new questions concerning the challenges for doing linguistic research with large digital text corpora in the context of studying totalitarian language use. The questions, as well as the chances and limits of such an approach, have very obvious actual references to the historic events unfolding today as well as a clearly historical dimension, precisely because the digital text sources that have been created to analyse the German language use of the Nazi-period from 1933 to 1945 can be understood as a model to deal with related questions of contemporary language use, particularly in the context of the new war of extermination of Russia in Ukraine of the year 2022 and how it is represented in contemporary media.

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bib (full) Proceedings of the First Computing Social Responsibility Workshop within the 13th Language Resources and Evaluation Conference

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Proceedings of the First Computing Social Responsibility Workshop within the 13th Language Resources and Evaluation Conference
Mingyu Wan | Chu-Ren Huang

Sustainability reporting has become an annual requirement in many countries and for certain types of companies. Sustainability reports inform stakeholders about companies’ commitment to sustainable development and their economic, social, and environmental sustainability practices. However, the fact that norms and standards allow a certain discretion to be adopted by drafting organizations makes such reports hardly comparable in terms of layout, disclosures, key performance indicators (KPIs), and so on. In this work, we present a system based on natural language processing and information extraction techniques to retrieve relevant information from sustainability reports, compliant with the Global Reporting Initiative Standards, written in Italian and English language. Specifically, the system is able to identify references to the various sustainability topics discussed by the reports: on which page of the document those references have been found, the context of each reference, and if it is mentioned positively or negatively. The output of the system has been then evaluated against a ground truth obtained through a manual annotation process on 134 reports. Experimental outcomes highlight the affordability of the approach for improving sustainability disclosures, accessibility, and transparency, thus empowering stakeholders to conduct further analysis and considerations.

We describe initial work into analysing the language used around environmental, social and governance (ESG) issues in UK company annual reports. We collect a dataset of annual reports from UK FTSE350 companies over the years 2012-2019; separately, we define a categorized list of core ESG terms (single words and multi-word expressions) by combining existing lists with manual annotation. We then show that this list can be used to analyse the changes in ESG language in the dataset over time, via a combination of language modelling and distributional modelling via contextual word embeddings. Initial findings show that while ESG discussion in annual reports is becoming significantly more likely over time, the increase varies with category and with individual terms, and that some terms show noticeable changes in usage.

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A Corpus-based Study of Corporate Image Represented in Corporate Social Responsibility Report: A Case Study of China Mobile and Vodafone
Xing Chen | Liang Xu

By examination of the high-frequency nouns, verbs, and keywords, the present study probes into the similarities and differences of corporate images represented in Corporate Social Responsibility (CSR) reports of China Mobile and Vodafone. The results suggest that: 1) both China Mobile and Vodafone prefer using some positive words, like improve, support and service to shape a positive, approachable and easy-going corporate image, and an image of prioritizing the environmental sustainability and the well-being of people; 2) CSR reports of China Mobile contain the keywords poverty and alleviation, which means China Mobile is pragmatic, collaborative and active to assume the responsibility for social events; 3) CSR reports of Vodafone contain keywords like privacy, women and global as well as some other countries, which shows Vodafone is enterprising, globalized and attentive to the development of women; 4) these differences might be related to the ideology and social culture of Chinese and British companies. This study may contribute to understanding the function of CSR report and offer helpful implications for broadening the research of corporate image.

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Framing Legitimacy in CSR: A Corpus of Chinese and American Petroleum Company CSR Reports and Preliminary Analysis
Jieyu Chen | Kathleen Ahrens | Chu-Ren Huang

We examine how Chinese and American oil companies use the gain- and loss-framed BUILDING source domain to legitimize their business in Corporate Social Responsibility (CSR) reports. Gain and loss frames can create legitimacy because they can ethically position an issue. We will focus on oil companies in China and the U.S. because different socio-cultural contexts in these two countries can potentially lead to different legitimation strategies in CSR reports, which can shed light on differences in Chinese and American CSR. All of the oil companies in our data are on the Fortune 500 list (2020). The results showed that Chinese oil companies used BUILDING metaphors more frequently than American oil companies. The most frequent keyword in Chinese CSRs “build” highlights environmental achievements in compliance with governments’ policies. American CSRs often used the metaphorical verb “support” to show their alignment with environmental policies and the interests of different stakeholders. The BUILDING source domain was used more often as gain frames in both Chinese and American CSR reports to show how oil companies create benefits for different stakeholders.

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MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain
Aleksandra Gabryszak | Philippe Thomas

In this paper we show how aspect-based sentiment analysis might help public transport companies to improve their social responsibility for accessible travel. We present MobASA: a novel German-language corpus of tweets annotated with their relevance for public transportation, and with sentiment towards aspects related to barrier-free travel. We identified and labeled topics important for passengers limited in their mobility due to disability, age, or when travelling with young children. The data can be used to identify hurdles and improve travel planning for vulnerable passengers, as well as to monitor a perception of transportation businesses regarding the social inclusion of all passengers. The data is publicly available under: https://github.com/DFKI-NLP/sim3s-corpus

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Detecting Violation of Human Rights via Social Media
Yash Pilankar | Rejwanul Haque | Mohammed Hasanuzzaman | Paul Stynes | Pramod Pathak

Social media is not just meant for entertainment, it provides platforms for sharing information, news, facts and events. In the digital age, activists and numerous users are seen to be vocal regarding human rights and their violations in social media. However, their voices do not often reach to the targeted audience and concerned human rights organization. In this work, we aimed at detecting factual posts in social media about violation of human rights in any part of the world. The end product of this research can be seen as an useful asset for different peacekeeping organizations who could exploit it to monitor real-time circumstances about any incident in relation to violation of human rights. We chose one of the popular micro-blogging websites, Twitter, for our investigation. We used supervised learning algorithms in order to build human rights violation identification (HRVI) models which are able to identify Tweets in relation to incidents of human right violation. For this, we had to manually create a data set, which is one of the contributions of this research. We found that our classification models that were trained on this gold-standard dataset performed excellently in classifying factual Tweets about human rights violation, achieving an accuracy of upto 93% on hold-out test set.

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Inclusion in CSR Reports: The Lens from a Data-Driven Machine Learning Model
Lu Lu | Jinghang Gu | Chu-Ren Huang

Inclusion, as one of the foundations in the diversity, equity, and inclusion initiative, concerns the degree of being treated as an ingroup member in a workplace. Despite of its importance in a corporate’s ecosystem, the inclusion strategies and its performance are not adequately addressed in corporate social responsibility (CSR) and CSR reporting. This study proposes a machine learning and big data-based model to examine inclusion through the use of stereotype content in actual language use. The distribution of the stereotype content in general corpora of a given society is utilized as a baseline, with which texts about corporate texts are compared. This study not only propose a model to identify and classify inclusion in language use, but also provides insights to measure and track progress by including inclusion in CSR reports as a strategy to build an inclusive corporate team.

Pharmaceutical text classification is an important area of research for commercial and research institutions working in the pharmaceutical domain. Addressing this task is challenging due to the need of expert verified labelled data which can be expensive and time consuming to obtain. Towards this end, we leverage predictive coding methods for the task as they have been shown to generalise well for sentence classification. Specifically, we utilise GAN-BERT architecture to classify pharmaceutical texts. To capture the domain specificity, we propose to utilise the BioBERT model as our BERT model in the GAN-BERT framework. We conduct extensive evaluation to show the efficacy of our approach over baselines on multiple metrics.

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bib (full) Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference

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Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference
Jonne Sälevä | Constantine Lignos

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SyntAct: A Synthesized Database of Basic Emotions
Felix Burkhardt | Florian Eyben | Björn Schuller

Speech emotion recognition is in the focus of research since several decades and has many applications. One problem is sparse data for supervised learning. One way to tackle this problem is the synthesis of data with emotion simulating speech synthesis approaches. We present a synthesized database of five basic emotions and neutral expression based on rule based manipulation for a diphone synthesizer which we release to the public. The database has been validated in several machine learning experiments as a training set to detect emotional expression from natural speech data. The scripts to generate such a database have been made open source and could be used to aid speech emotion recognition for a low resourced language, as MBROLA supports 35 languages

Research has shown the potential negative impact of social media usage on body image. Various platforms present numerous medial formats of possibly harmful content related to eating disorders. Different cultural backgrounds, represented, for example, by different languages, are participating in the discussion online. Therefore, this research aims to investigate eating disorder specific content in a multilingual and multimedia environment. We want to contribute to establishing a common ground for further automated approaches. Our first objective is to combine the two media formats, text and image, by classifying the posts from one social media platform (Reddit) and continuing the categorization in the second (Tumblr). Our second objective is the analysis of multilingualism. We worked qualitatively in an iterative valid categorization process, followed by a comparison of the portrayal of eating disorders on both platforms. Our final data sets contained 960 Reddit and 2 081 Tumblr posts. Our analysis revealed that Reddit users predominantly exchange content regarding disease and eating behaviour, while on Tumblr, the focus is on the portrayal of oneself and one’s body.

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Construction and Validation of a Japanese Honorific Corpus Based on Systemic Functional Linguistics
Muxuan Liu | Ichiro Kobayashi

In Japanese, there are different expressions used in speech depending on the speaker’s and listener’s social status, called honorifics. Unlike other languages, Japanese has many types of honorific expressions, and it is vital for machine translation and dialogue systems to handle the differences in meaning correctly. However, there is still no corpus that deals with honorific expressions based on social status. In this study, we developed an honorific corpus (KeiCO corpus) that includes social status information based on Systemic Functional Linguistics, which expresses language use in situations from the social group’s values and common understanding. As a general-purpose language resource, it filled in the Japanese honorific blanks. We expect the KeiCO corpus could be helpful for various tasks, such as improving the accuracy of machine translation, automatic evaluation, correction of Japanese composition and style transformation. We also verified the accuracy of our corpus by a BERT-based classification task.

In this paper, we present the first Entity Linking corpus for Icelandic. We describe our approach of using a multilingual entity linking model (mGENRE) in combination with Wikipedia API Search (WAPIS) to label our data and compare it to an approach using WAPIS only. We find that our combined method reaches 53.9% coverage on our corpus, compared to 30.9% using only WAPIS. We analyze our results and explain the value of using a multilingual system when working with Icelandic. Additionally, we analyze the data that remain unlabeled, identify patterns and discuss why they may be more difficult to annotate.

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Crawling Under-Resourced Languages - a Portal for Community-Contributed Corpus Collection
Erik Körner | Felix Helfer | Christopher Schröder | Thomas Eckart | Dirk Goldhahn

The “Web as corpus” paradigm opens opportunities for enhancing the current state of language resources for endangered and under-resourced languages. However, standard crawling strategies tend to overlook available resources of these languages in favor of already well-documented ones. Since 2016, the “Crawling Under-Resourced Languages” portal (CURL) has been contributing to bridging the gap between established crawling techniques and knowledge about relevant Web resources that is only available in the specific language communities. The aim of the CURL portal is to enlarge the amount of available text material for under-resourced languages thereby developing available datasets further and to use them as a basis for statistical evaluation and enrichment of already available resources. The application is currently provided and further developed as part of the thematic cluster “Non-Latin scripts and Under-resourced languages” in the German national research consortium Text+. In this context, its focus lies on the extraction of text material and statistical information for the data domain “Lexical resources”.

In this paper, we present a number of fine-grained resources for Natural Language Inference (NLI). In particular, we present a number of resources and validation methods for Greek NLI and a resource for precise NLI. First, we extend the Greek version of the FraCaS test suite to include examples where the inference is directly linked to the syntactic/morphological properties of Greek. The new resource contains an additional 428 examples, making it in total a dataset of 774 examples. Expert annotators have been used in order to create the additional resource, while extensive validation of the original Greek version of the FraCaS by non-expert and expert subjects is performed. Next, we continue the work initiated by (CITATION), according to which a subset of the RTE problems have been labeled for missing hypotheses and we present a dataset an order of magnitude larger, annotating the whole SuperGlUE/RTE dataset with missing hypotheses. Lastly, we provide a de-dropped version of the Greek XNLI dataset, where the pronouns that are missing due to the pro-drop nature of the language are inserted. We then run some models to see the effect of that insertion and report the results.

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Words.hk: A Comprehensive Cantonese Dictionary Dataset with Definitions, Translations and Transliterated Examples
Chaak-ming Lau | Grace Wing-yan Chan | Raymond Ka-wai Tse | Lilian Suet-ying Chan

This paper discusses the compilation of the words.hk Cantonese dictionary dataset, which was compiled through manual annotation over a period of 7 years. Cantonese is a low-resource language with limited tagged or manually checked resources, especially at the sentential level, and this dataset is an attempt to fill the gap. The dataset contains over 53,000 entries of Cantonese words, which comes with basic lexical information (Jyutping phonemic transcription, part-of-speech tags, usage tags), manually crafted definitions in Written Cantonese, English translations, and Cantonese examples with English translation and Jyutping transliterations. Special attention has been paid to handle character variants, so that unintended “character errors” (equivalent to typos in phonemic writing systems) are filtered out, and intra-speaker variants are handled. Fine details on word segmentation, character variant handling, definition crafting will be discussed. The dataset can be used in a wide range of natural language processing tasks, such as word segmentation, construction of semantic web and training of models for Cantonese transliteration.

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LiSTra Automatic Speech Translation: English to Lingala Case Study
Salomon Kabongo Kabenamualu | Vukosi Marivate | Herman Kamper

In recent years there has been great interest in addressing the data scarcity of African languages and providing baseline models for different Natural Language Processing tasks (Orife et al., 2020). Several initiatives (Nekoto et al., 2020) on the continent uses the Bible as a data source to provide proof of concept for some NLP tasks. In this work, we present the Lingala Speech Translation (LiSTra) dataset, release a full pipeline for the construction of such dataset in other languages, and report baselines using both the traditional cascade approach (Automatic Speech Recognition - Machine Translation), and a revolutionary transformer based End-2-End architecture (Liu et al., 2020) with a custom interactive attention that allows information sharing between the recognition decoder and the translation decoder.

In this paper, an approach for hate speech detection against women in the Arabic community on social media (e.g. Youtube) is proposed. In the literature, similar works have been presented for other languages such as English. However, to the best of our knowledge, not much work has been conducted in the Arabic language. A new hate speech corpus (Arabic_fr_en) is developed using three different annotators. For corpus validation, three different machine learning algorithms are used, including deep Convolutional Neural Network (CNN), long short-term memory (LSTM) network and Bi-directional LSTM (Bi-LSTM) network. Simulation results demonstrate the best performa

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Word-level Language Identification Using Subword Embeddings for Code-mixed Bangla-English Social Media Data
Aparna Dutta

This paper reports work on building a word-level language identification (LID) model for code-mixed Bangla-English social media data using subword embeddings, with an ultimate goal of using this LID module as the first step in a modular part-of-speech (POS) tagger in future research. This work reports preliminary results of a word-level LID model that uses a single bidirectional LSTM with subword embeddings trained on very limited code-mixed resources. At the time of writing, there are no previous reported results available in which subword embeddings are used for language identification with the Bangla-English code-mixed language pair. As part of the current work, a labeled resource for word-level language identification is also presented, by correcting 85.7% of labels from the 2016 ICON Whatsapp Bangla-English dataset. The trained model was evaluated on a test set of 4,015 tokens compiled from the 2015 and 2016 ICON datasets, and achieved a test accuracy of 93.61%.

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bib (full) Proceedings of the Workshop on Processing Language Variation: Digital Armenian (DigitAm) within the 13th Language Resources and Evaluation Conference

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Proceedings of the Workshop on Processing Language Variation: Digital Armenian (DigitAm) within the 13th Language Resources and Evaluation Conference
Victoria Khurshudyan | Nadi Tomeh | Damien Nouvel | Anaid Donabedian | Chahan Vidal-Gorene

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A Free/Open-Source Morphological Transducer for Western Armenian
Hossep Dolatian | Daniel Swanson | Jonathan Washington

We present a free/open-source morphological transducer for Western Armenian, an endangered and low-resource Indo-European language. The transducer has virtually complete coverage of the language’s inflectional morphology. We built the lexicon by scraping online dictionaries. As of submission, the transducer has a lexicon of 75K words. It has over 90% naive coverage on different Western Armenian corpora, and high precision.

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Dialects Identification of Armenian Language
Karen Avetisyan

The Armenian language has many dialects that differ from each other syntactically, morphologically, and phonetically. In this work, we implement and evaluate models that determine the dialect of a given passage of text. The proposed models are evaluated for the three major variations of the Armenian language: Eastern, Western, and Classical. Previously, there were no instruments of dialect identification in the Armenian language. The paper presents three approaches: a statistical which relies on a stop words dictionary, a modified statistical one with a dictionary of most frequently encountered words, and the third one that is based on Facebook’s fastText language identification neural network model. Two types of neural network models were trained, one with the usage of pre-trained word embeddings and the other without. Approaches were tested on sentence-level and document-level data. The results show that the neural network-based method works sufficiently better than the statistical ones, achieving almost 98% accuracy at the sentence level and nearly 100% at the document level.

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Analyse Automatique de l’Ancien Arménien. Évaluation d’une méthode hybride « dictionnaire » et « réseau de neurones » sur un Extrait de l’Adversus Haereses d’Irénée de Lyon
Bastien Kindt | Gabriel Kepeklian

The aim of this paper is to evaluate a lexical analysis (mainly lemmatization and POS-tagging) of a sample of the Ancient Armenian version of the Adversus Haereses by Irenaeus of Lyons (2nd c.) by using hybrid approach based on digital dictionaries on the one hand, and on Recurrent Neural Network (RNN) on the other hand. The quality of the results is checked by comparing data obtained by implementing these two methods with data manually checked. In the present case, 98,37% of the results are correct by using the first (lexical) approach, and 74,64% by using the second (RNN). But, in fact, both methods present advantages and disadvantages and argue for the hybrid method. The linguistic resources implemented here are jointly developed and tested by GREgORI and Calfa.

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Describing Language Variation in the Colophons of Armenian Manuscripts
Bastien Kindt | Emmanuel Van Elverdinghe

The colophons of Armenian manuscripts constitute a large textual corpus spanning a millennium of written culture. These texts are highly diverse and rich in terms of linguistic variation. This poses a challenge to NLP tools, especially considering the fact that linguistic resources designed or suited for Armenian are still scarce. In this paper, we deal with a sub-corpus of colophons written to commemorate the rescue of a manuscript and dating from 1286 to ca. 1450, a thematic group distinguished by a particularly high concentration of words exhibiting linguistic variation. The text is processed (lemmatization, POS-tagging, and inflectional tagging) using the tools of the GREgORI Project and evaluated. Through a selection of examples, we show how variation is dealt with at each linguistic level (phonology, orthography, flexion, vocabulary, syntax). Complex variation, at the level of tokens or lemmata, is considered as well. The results of this work are used to enrich and refine the linguistic resources of the GREgORI project, which in turn benefits the processing of other texts.

Eastern Armenian National Corpus (EANC) is a comprehensive corpus of Modern Eastern Armenian with about 110 million tokens, covering written and oral discourses from the mid-19th century to the present. The corpus is provided with morphological, semantic and metatext annotation, as well as English translations. EANC is open access and available at www.eanc.net.

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Towards a Unified ASR System for the Armenian Standards
Samuel Chakmakjian | Ilaine Wang

Armenian is a traditionally under-resourced language, which has seen a recent uptick in interest in the development of its tools and presence in the digital domain. Some of this recent interest has centred around the development of Automatic Speech Recognition (ASR) technologies. However, the language boasts two standard variants which diverge on multiple typological and structural levels. In this work, we examine some of the available bodies of data for ASR construction, present the challenges in the processing of these data and propose a methodology going forward.

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bib (full) Proceedings of the Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia within the 13th Language Resources and Evaluation Conference

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Proceedings of the Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia within the 13th Language Resources and Evaluation Conference
Atul Kr. Ojha | Sina Ahmadi | Chao-Hong Liu | John P. McCrae

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NLP Pipeline for Annotating (Endangered) Tibetan and Newar Varieties
Christian Faggionato | Nathan Hill | Marieke Meelen

In this paper we present our work-in-progress on a fully-implemented pipeline to create deeply-annotated corpora of a number of historical and contemporary Tibetan and Newar varieties. Our off-the-shelf tools allow researchers to create corpora with five different layers of annotation, ranging from morphosyntactic to information-structural annotation. We build on and optimise existing tools (in line with FAIR principles), as well as develop new ones, and show how they can be adapted to other Tibetan and Newar languages, most notably modern endangered languages that are both extremely low-resourced and under-researched.

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Towards an Ontology for Toponyms in Nepalese Historical Documents
Sabine Tittel

Nepalese historical legal documents contain a plethora of valuable information on the history of what is today Nepal. An empirical study based on such documents enables a deep understanding of religion and ritual, legal practice, rulership, and many other aspects of the society through time. The aim of the research project ‘Documents on the History of Religion and Law of Pre-modern Nepal’ is to make accessible a text corpus with 18 th to 20 th century documents both through cataloging and digital text editions, building a database called Documenta Nepalica. However, the lack of interoperability with other resources hampers its seamless integration into broader research contexts. To address this problem, we target the modeling of the Documenta Nepalica as Linked Data. This paper presents one module of this larger endeavour: It describes a proof of concept for an ontology for Nepalese toponyms that provides the means to classify toponyms attested in the documents and to model their entanglement with other toponyms, persons, events, and time. The ontology integrates and extends standard ontologies and increases interoperability through aligning the ontology individuals to the respective entries of geographic authority files such as GeoNames. Also, we establish a mapping of the individuals to DBpedia entities.

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Semiautomatic Speech Alignment for Under-Resourced Languages
Juho Leinonen | Niko Partanen | Sami Virpioja | Mikko Kurimo

Cross-language forced alignment is a solution for linguists who create speech corpora for very low-resource languages. However, cross-language is an additional challenge making a complex task, forced alignment, even more difficult. We study how linguists can impart domain expertise to the tasks to increase the performance of automatic forced aligners while keeping the time effort still lower than with manual forced alignment. First, we show that speech recognizers have a clear bias in starting the word later than a human annotator, which results in micro-pauses in the results that do not exist in manual alignments, and study which is the best way to automatically remove these silences. Second, we ask the linguists to simplify the task by splitting long interview audios into shorter lengths by providing some manually aligned segments and evaluating the results of this process. We also study how correlated source language performance is to target language performance, since often it is an easier task to find a better source model than to adapt to the target language.

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How to Digitize Completely: Interactive Geovizualization of a Sketch Map from the Kuzmina Archive
Elena Lazarenko | Aleksandr Riaposov

This paper discusses work in progress on the digitization of a sketch map of the Taz River basin – a region that is lacking highly detailed open-source cartography data. The original sketch is retrieved from the archive of Selkup materials gathered by Angelina Ivanovna Kuzmina in the 1960s and 1970s. The data quality and challenges that come with it are evaluated and a task-specific workflow is designed. The process of the turning a series of hand-drawn images with non-structured geographical and linguistic data into an interactive, geographically precise digital map is described both from linguistic and technical perspectives. Furthermore, the map objects in focus are differentiated based on the geographical type of the object and the etymology of the name. This provides an insight into the peculiarities of the linguistic history of the region and contributes to the cartography of the Uralic languages.

In this paper we show how word class based language modeling can support the integration of a small language in modern applications of speech technology. The methods described in this paper can be applied for any language. We demonstrate the methods on Upper Sorbian. The word classes model the semantic expressions of numerals, date and time of day. The implementation of the created grammars was realized in the form of finite-state-transducers (FSTs) and minimalists grammars (MGs). We practically demonstrate the usage of the FSTs in a simple smart-home speech application, that is able to set wake-up alarms and appointments expressed in a variety of spontaneous and natural sentences. While the created MGs are not integrated in an application for practical use yet, they provide evidence that MGs could potentially work more efficient than FSTs in built-on applications. In particular, MGs can work with a significantly smaller lexicon size, since their more complex structure lets them generate more expressions with less items, while still avoiding wrong expressions.

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Bringing Together Version Control and Quality Assurance of Language Data with LAMA
Aleksandr Riaposov | Elena Lazarenko | Timm Lehmberg

This contribution reports on work in process on project specific software and digital infrastructure components used along with corpus curation workflows in the the framework of the long-term language documentation project INEL. By bringing together scientists with different levels of technical affinity in a highly interdisciplinary working environment, the project is confronted with numerous workflow related issues. Many of them result from collaborative (remote-)work on digital corpora, which, among other things, include annotation, glossing but also quality- and consistency control. In this context several steps were taken to bridge the gap between usability and the requirements of complex data curation workflows. Components of the latter such as a versioning system and semi-automated data validators on one side meet the user demands for the simplicity and minimalism on the other side. Embodying a simple shell script in an interactive graphic user interface, we augment the efficacy of the data versioning and the integration of Java-based quality control and validation tools.

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Automatic Verb Classifier for Abui (AVC-abz)
Frantisek Kratochvil | George Saad | Jiří Vomlel | Václav Kratochvíl

We present an automatic verb classifier system that identifies inflectional classes in Abui (AVC-abz), a Papuan language of the Timor-Alor-Pantar family. The system combines manually annotated language data (the learning set) with the output of a morphological precision grammar (corpus data). The morphological precision grammar is trained on a fully glossed smaller corpus and applied to a larger corpus. Using the k-means algorithm, the system clusters inflectional classes discovered in the learning set. In the second step, Naive Bayes algorithm assigns the verbs found in the corpus data to the best-fitting cluster. AVC-abz serves to advance and refine the grammatical analysis of Abui as well as to monitor corpus coverage and its gradual improvement.

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Dialogue Act and Slot Recognition in Italian Complex Dialogues
Irene Sucameli | Michele De Quattro | Arash Eshghi | Alessandro Suglia | Maria Simi

Since the advent of Transformer-based, pretrained language models (LM) such as BERT, Natural Language Understanding (NLU) components in the form of Dialogue Act Recognition (DAR) and Slot Recognition (SR) for dialogue systems have become both more accurate and easier to create for specific application domains. Unsurprisingly however, much of this progress has been limited to the English language, due to the existence of very large datasets in both dialogue and written form, while only few corpora are available for lower resourced languages like Italian. In this paper, we present JILDA 2.0, an enhanced version of a Italian task-oriented dialogue dataset, using it to realise a Italian NLU baseline by evaluating three of the most recent pretrained LMs: Italian BERT, Multilingual BERT, and AlBERTo for the DAR and SR tasks. Thus, this paper not only presents an updated version of a dataset characterised by complex dialogues, but it also highlights the challenges that still remain in creating effective NLU components for lower resourced languages, constituting a first step in improving NLU for Italian dialogue.

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Digital Resources for the Shughni Language
Yury Makarov | Maksim Melenchenko | Dmitry Novokshanov

This paper describes the Shughni Documentation Project consisting of the Online Shughni Dictionary, morphological analyzer, orthography converter, and Shughni corpus. The online dictionary has not only basic functions such as finding words but also facilitates more complex tasks. Representing a lexeme as a network of database sections makes it possible to search in particular domains (e.g., in meanings only), and the system of labels facilitates conditional search queries. Apart from this, users can make search queries and view entries in different orthographies of the Shughni language and send feedback in case they spot mistakes. Editors can add, modify, or delete entries without programming skills via an intuitive interface. In future, such website architecture can be applied to creating a lexical database of Iranian languages. The morphological analyzer performs automatic analysis of Shughni texts, which is useful for linguistic research and documentation. Once the analysis is complete, homonymy resolution must be conducted so that the annotated texts are ready to be uploaded to the corpus. The analyzer makes use of the orthographic converter, which helps to tackle the problem of spelling variability in Shughni, a language with no standard literary tradition.

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German Dialect Identification and Mapping for Preservation and Recovery
Aynalem Tesfaye Misganaw | Sabine Roller

Many linguistic projects which focus on dialects do collection of audio data, analysis, and linguistic interpretation on the data. The outcomes of such projects are good language resources because dialects are among less-resources languages as most of them are oral traditions. Our project Dialektatlas Mittleres Westdeutschland (DMW) 1 focuses on the study of German language varieties through collection of audio data of words and phrases which are selected by linguistic experts based on the linguistic significance of the words (and phrases) to distinguish dialects among each other. We used a total of 7,814 audio snippets of the words and phrases of eight different dialects from middle west Germany. We employed a multilabel classification approach to address the problem of dialect mapping using Support Vector Machine (SVM) algorithm. The experimental result showed a promising accuracy of 87%.

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Exploring Transfer Learning for Urdu Speech Synthesis
Sahar Jamal | Sadaf Abdul Rauf | Quratulain Majid

Neural methods in Text to Speech synthesis (TTS) have demonstrated momentous advancement in terms of the naturalness and intelligibility of the synthesized speech. In this paper we present neural speech synthesis system for Urdu language, a low resource language. The main challenge faced for this study was the non-availability of any publicly available Urdu speech synthesis corpora. Urdu speech corpus was created using audio books and synthetic speech generation. To leverage the low resource scenario we adopted transfer learning for our experiments where knowledge extracted is further used to train the model using a relatively smaller Urdu training data set. The results from this model show satisfactory results, though a good margin for improvement exists and we are working to improve it further.

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Towards Bengali WordNet Enrichment using Knowledge Graph Completion Techniques
Sree Bhattacharyya | Abhik Jana

WordNet serves as a very essential knowledge source for various downstream Natural Language Processing (NLP) tasks. Since this is a human-curated resource, building such a resource is very cumbersome and time-consuming. Even though for languages like English, the existing WordNet is reasonably rich in terms of coverage, for resource-poor languages like Bengali, the WordNet is far from being reasonably sufficient in terms of coverage of vocabulary and relations between them. In this paper, we investigate the usefulness of some of the existing knowledge graph completion algorithms to enrich Bengali WordNet automatically. We explore three such techniques namely DistMult, ComplEx, and HolE, and analyze their effectiveness for adding more relations between existing nodes in the WordNet. We achieve maximum Hits@1 of 0.412 and Hits@10 of 0.703, which look very promising for low resource languages like Bengali.

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Enriching Hindi WordNet Using Knowledge Graph Completion Approach
Sushil Awale | Abhik Jana

Even though the use of WordNet in the Natural Language Processing domain is unquestionable, creating and maintaining WordNet is a cumbersome job and it is even difficult for low resource languages like Hindi. In this study, we aim to enrich the Hindi WordNet automatically by using state-of-the-art knowledge graph completion (KGC) approaches. We pose the automatic Hindi WordNet enrichment problem as a knowledge graph completion task and therefore we modify the WordNet structure to make it appropriate for applying KGC approaches. Second, we attempt five KGC approaches of three different genres and compare the performances for the task. Our study shows that ConvE is the best KGC methodology for this specific task compared to other KGC approaches.

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A Digital Swedish-Yiddish/Yiddish-Swedish Dictionary: A Web-Based Dictionary that is also Available Offline
Magnus Ahltorp | Jean Hessel | Gunnar Eriksson | Maria Skeppstedt | Rickard Domeij

Yiddish is one of the national minority languages of Sweden, and one of the languages for which the Swedish Institute for Language and Folklore is responsible for developing useful language resources. We here describe the web-based version of a Swedish-Yiddish/Yiddish-Swedish dictionary. The single search field of the web-based dictionary is used for incrementally searching all three components of the dictionary entries (the word in Swedish, the word in Yiddish with Hebrew characters and the transliteration in Latin script). When the user accesses the dictionary in an online mode, the dictionary is saved in the web browser, which makes it possible to also use the dictionary offline.

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An Online Dictionary for Dialects of North Frisian
Michael Wehar | Tanno Hüttenrauch

Language is an essential part of communication and culture. Documenting, digitizing, and preserving language is a meaningful pursuit. The first author of this work is a speaker of Söl’ring which is a dialect of the North Frisian language spoken on the island of Sylt in the North Frisia region of Germany. Söl’ring is estimated to have only hundreds of native speakers and very limited online language resources making it a prime candidate for language preservation initiatives. To help preserve Söl’ring and provide resources for Söl’ring speakers and learners, we built an online dictionary. Our dictionary, called friisk.org, provides translations for over 28,000 common German words to Söl’ring. In addition, our dictionary supports translations for Söl’ring to German, spell checking for Söl’ring, conjugations for common Söl’ring verbs, and an experimental transcriber from Söl’ring to IPA for pronunciations. Following the release of our online dictionary, we collaborated with neighboring communities to add limited support for additional North Frisian dialects including Fering, Halligen Frisian, Karrharder, Nordergoesharder, Öömrang, and Wiedingharder.

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Towards a Unified Tool for the Management of Data and Technologies in Field Linguistics and Computational Linguistics - LiFE
Siddharth Singh | Ritesh Kumar | Shyam Ratan | Sonal Sinha

The paper presents a new software - Linguistic Field Data Management and Analysis System - LiFE for endangered and low-resourced languages - an open-source, web-based linguistic data analysis and management application allowing systematic storage, management, usage and sharing of linguistic data collected from the field. The application enables users to store lexical items, sentences, paragraphs, audio-visual content including photographs, video clips, speech recordings, etc, with rich glossing and annotation. For field linguists, it provides facilities to generate interactive and print dictionaries; for NLP practitioners, it provides the data storage and representation in standard formats such as RDF, JSON and CSV. The tool provides a one-click interface to train NLP models for various tasks using the data stored in the system and then use it for assistance in further storage of the data (especially for the field linguists). At the same time, the tool also provides the facility of using the models trained outside of the tool for data storage, transcription, annotation and other tasks. The web-based application, allows for seamless collaboration among multiple persons and sharing the data, models, etc with each other.

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Universal Dependencies Treebank for Tatar: Incorporating Intra-Word Code-Switching Information
Chihiro Taguchi | Sei Iwata | Taro Watanabe

This paper introduces a new Universal Dependencies treebank for the Tatar language named NMCTT. A significant feature of the corpus is that it includes code-switching (CS) information at a morpheme level, given the fact that Tatar texts contain intra-word CS between Tatar and Russian. We first outline NMCTT with a focus on differences from other treebanks of Turkic languages. Then, to evaluate the merit of the CS annotation, this study concisely reports the results of a language identification task implemented with Conditional Random Fields that considers POS tag information, which is readily available in treebanks in the CoNLL-U format. Experimenting on NMCTT and the Turkish-German CS treebank (SAGT), we demonstrate that the proposed annotation scheme introduced in NMCTT can improve the performance of the subword-level language identification. This annotation scheme for CS is not only universally applicable to languages with CS, but also shows a possibility to employ morphosyntactic information for CS-related downstream tasks.

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Preparing an endangered language for the digital age: The Case of Judeo-Spanish
Alp Öktem | Rodolfo Zevallos | Yasmin Moslem | Özgür Güneş Öztürk | Karen Gerson Şarhon

We develop machine translation and speech synthesis systems to complement the efforts of revitalizing Judeo-Spanish, the exiled language of Sephardic Jews, which survived for centuries, but now faces the threat of extinction in the digital age. Building on resources created by the Sephardic community of Turkey and elsewhere, we create corpora and tools that would help preserve this language for future generations. For machine translation, we first develop a Spanish to Judeo-Spanish rule-based machine translation system, in order to generate large volumes of synthetic parallel data in the relevant language pairs: Turkish, English and Spanish. Then, we train baseline neural machine translation engines using this synthetic data and authentic parallel data created from translations by the Sephardic community. For text-to-speech synthesis, we present a 3.5-hour single speaker speech corpus for building a neural speech synthesis engine. Resources, model weights and online inference engines are shared publicly.

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bib (full) Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022

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Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022
Mahmoud El-Haj | Paul Rayson | Nadhem Zmandar

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FinRAD: Financial Readability Assessment Dataset - 13,000+ Definitions of Financial Terms for Measuring Readability
Sohom Ghosh | Shovon Sengupta | Sudip Naskar | Sunny Kumar Singh

In today’s world, the advancement and spread of the Internet and digitalization have resulted in most information being openly accessible. This holds true for financial services as well. Investors make data driven decisions by analysing publicly available information like annual reports of listed companies, details regarding asset allocation of mutual funds, etc. Many a time these financial documents contain unknown financial terms. In such cases, it becomes important to look at their definitions. However, not all definitions are equally readable. Readability largely depends on the structure, complexity and constituent terms that make up a definition. This brings in the need for automatically evaluating the readability of definitions of financial terms. This paper presents a dataset, FinRAD consisting of financial terms, their definitions and embeddings. In addition to standard readability scores (like “Flesch Reading Index (FRI)”, “Automated Readability Index (ARI)”, “SMOG Index Score (SIS)”,“Dale-Chall formula (DCF)”, etc.), it also contains the readability scores (AR) assigned based on sources from which the terms have been collected. We manually inspect a sample from it to ensure the quality of the assignment. Subsequently, we prove that the rule-based standard readability scores (like “Flesch Reading Index (FRI)”, “Automated Readability Index (ARI)”, “SMOG Index Score (SIS)”,“Dale-Chall formula (DCF)”, etc.) do not correlate well with the manually assigned binary readability scores of definitions of financial terms. Finally, we present a few neural baselines using transformer based architecture to automatically classify these definitions as readable or not. Pre-trained FinBERT model fine-tuned on FinRAD corpus performs the best (AU-ROC = 0.9927, F1 = 0.9610). This corpus can be downloaded from https://github.com/sohomghosh/FinRAD_Financial_Readability_Assessment_Dataset.

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Discovering Financial Hypernyms by Prompting Masked Language Models
Bo Peng | Emmanuele Chersoni | Yu-Yin Hsu | Chu-Ren Huang

With the rising popularity of Transformer-based language models, several studies have tried to exploit their masked language modeling capabilities to automatically extract relational linguistic knowledge, although this kind of research has rarely investigated semantic relations in specialized domains. The present study aims at testing a general-domain and a domain-adapted Transformer models on two datasets of financial term-hypernym pairs using the prompt methodology. Our results show that the differences of prompts impact critically on models’ performance, and that domain adaptation on financial text generally improves the capacity of the models to associate the target terms with the right hypernyms, although the more successful models are those retaining a general-domain vocabulary.

Ontologies are increasingly used for machine reasoning over the last few years. They can provide explanations of concepts or be used for concept classification if there exists a mapping from the desired labels to the relevant ontology. This paper presents a practical use of an ontology for the purpose of data set generalization in an oversampling setting, with the aim of improving classification models. We demonstrate our solution on a novel financial sentiment data set using the Financial Industry Business Ontology (FIBO). The results show that generalization-based data enrichment benefits simpler models in a general setting and more complex models such as BERT in low-data setting.

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Detecting Causes of Stock Price Rise and Decline by Machine Reading Comprehension with BERT
Gakuto Tsutsumi | Takehito Utsuro

In this paper, we focused on news reported when stock prices fluctuate significantly. The news reported when stock prices change is a very useful source of information on what factors cause stock prices to change. However, because it is manually produced, not all events that cause stock prices to change are necessarily reported. Thus, in order to provide investors with information on those causes of stock price changes, it is necessary to develop a system to collect information on events that could be closely related to the stock price changes of certain companies from the Internet. As the first step towards developing such a system, this paper takes an approach of employing a BERT-based machine reading comprehension model, which extracts causes of stock price rise and decline from news reports on stock price changes. In the evaluation, the approach of using the title of the article as the question of machine reading comprehension performs well. It is shown that the fine-tuned machine reading comprehension model successfully detects additional causes of stock price rise and decline other than those stated in the title of the article.

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XLNET-GRU Sentiment Regression Model for Cryptocurrency News in English and Malay
Nur Azmina Mohamad Zamani | Jasy Suet Yan Liew | Ahmad Muhyiddin Yusof

Contextual word embeddings such as the transformer language models are gaining popularity in text classification and analytics but have rarely been explored for sentiment analysis on cryptocurrency news particularly on languages other than English. Various state-of-the-art (SOTA) pre-trained language models have been introduced recently such as BERT, ALBERT, ELECTRA, RoBERTa, and XLNet for text representation. Hence, this study aims to investigate the performance of using Gated Recurrent Unit (GRU) with Generalized Autoregressive Pretraining for Language (XLNet) contextual word embedding for sentiment analysis on English and Malay cryptocurrency news (Bitcoin and Ethereum). We also compare the performance of our XLNet-GRU model against other SOTA pre-trained language models. Manually labelled corpora of English and Malay news are utilized to learn the context of text specifically in the cryptocurrency domain. Based on our experiments, we found that our XLNet-GRU sentiment regression model outperformed the lexicon-based baseline with mean adjusted R2 = 0.631 across Bitcoin and Ethereum for English and mean adjusted R2 = 0.514 for Malay.

This paper presents the results and findings of the Financial Narrative Summarisation Shared Task on summarising UK, Greek and Spanish annual reports. The shared task was organised as part of the Financial Narrative Processing 2022 Workshop (FNP 2022 Workshop). The Financial Narrative summarisation Shared Task (FNS-2022) has been running since 2020 as part of the Financial Narrative Processing (FNP) workshop series (El-Haj et al., 2022; El-Haj et al., 2021; El-Haj et al., 2020b; El-Haj et al., 2019c; El-Haj et al., 2018). The shared task included one main task which is the use of either abstractive or extractive automatic summarisers to summarise long documents in terms of UK, Greek and Spanish financial annual reports. This shared task is the third to target financial documents. The data for the shared task was created and collected from publicly available annual reports published by firms listed on the Stock Exchanges of UK, Greece and Spain. A total number of 14 systems from 7 different teams participated in the shared task.

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Multilingual Text Summarization on Financial Documents
Negar Foroutan | Angelika Romanou | Stéphane Massonnet | Rémi Lebret | Karl Aberer

This paper proposes a multilingual Automated Text Summarization (ATS) method targeting the Financial Narrative Summarization Task (FNS-2022). We developed two systems; the first uses a pre-trained abstractive summarization model that was fine-tuned on the downstream objective, the second approaches the problem as an extractive approach in which a similarity search is performed on the trained span representations. Both models aim to identify the beginning of the continuous narrative section of the document. The language models were fine-tuned on a financial document collection of three languages (English, Spanish, and Greek) and aim to identify the beginning of the summary narrative part of the document. The proposed systems achieve high performance in the given task, with the sequence-to-sequence variant ranked 1st on ROUGE-2 F1 score on the test set for each of the three languages.

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Extractive and Abstractive Summarization Methods for Financial Narrative Summarization in English, Spanish and Greek
Alejandro Vaca | Alba Segurado | David Betancur | Álvaro Barbero Jiménez

This paper describes the three summarization systems submitted to the Financial Narrative Summarization Shared Task (FNS-2022). We developed a task-specific extractive summarization method for the reports in English. It was based on a sequence classification task whose objective was to find the sentence where the summary begins. On the other hand, since the summaries for the reports in Spanish and Greek were not extractive, we used an abstractive strategy for each of the languages. In particular, we created a new Encoder-Decoder architecture in Spanish, MariMari, based on an existing Encoding-only model; we also trained multilingual Encoder-Decoder models for this task. Finally, the summaries for the reports in Greek were obtained with a translation-summary-translation system in which the reports were translated to English and summarised, and then the summaries were translated back to Greek.

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DiMSum: Distributed and Multilingual Summarization of Financial Narratives
Neelesh Shukla | Amit Vaid | Raghu Katikeri | Sangeeth Keeriyadath | Msp Raja

This paper was submitted for Financial Narrative Summarization (FNS) task in FNP-2022 workshop. The objective of the task was to generate not more than 1000 words summaries for the annual financial reports written in English, Spanish and Greek languages. The central idea of this paper is to demonstrate automatic ways of identifying key narrative sections and their contributions towards generating summaries of financial reports. We have observed a few limitations in the previous works: First, the complete report was being considered for summary generation instead of key narrative sections. Second, many of the works followed manual or heuristic-based techniques to identify narrative sections. Third, sentences from key narrative sections were abruptly dropped to limit the summary to the desired length. To overcome these shortcomings, we introduced a novel approach to automatically learn key narrative sections and their weighted contributions to the reports. Since the summaries may come from various parts of the reports, the summary generation process was distributed amongst the key narrative sections based on the weights identified, later combined to have an overall summary. We also showcased that our approach is adaptive to various report formats and languages.

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Transformer-based Models for Long Document Summarisation in Financial Domain
Urvashi Khanna | Samira Ghodratnama | Diego Moll ́a | Amin Beheshti

Summarisation of long financial documents is a challenging task due to the lack of large-scale datasets and the need for domain knowledge experts to create human-written summaries. Traditional summarisation approaches that generate a summary based on the content cannot produce summaries comparable to human-written ones and thus are rarely used in practice. In this work, we use the Longformer-Encoder-Decoder (LED) model to handle long financial reports. We describe our experiments and participating systems in the financial narrative summarisation shared task. Multi-stage fine-tuning helps the model generalise better on niche domains and avoids the problem of catastrophic forgetting. We further investigate the effect of the staged fine-tuning approach on the FNS dataset. Our systems achieved promising results in terms of ROUGE scores on the validation dataset.

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Financial Narrative Summarisation Using a Hybrid TF-IDF and Clustering Summariser: AO-Lancs System at FNS 2022
Mahmoud El-Haj | Andrew Ogden

This paper describes the HTAC system submitted to the Financial Narrative Summarization Shared Task (FNS-2022). A methodology implementing Financial narrative Processing (FNP) to summarise financial annual reports, named Hybrid TF-IDF and Clustering (HTAC). This involves a hybrid approach combining TF-IDF sentence ranking as an NLP tool with a state-of-the-art Clustering Machine learning model to produce short 1000-word summaries of long financial annual reports. These Annual Reports are a legal responsibility of public companies and are in excess of 50,000 words. The model extracts the crucial information from these documents, discarding the extraneous content, leaving only the crucial information in a shorter, non-redundant summary. Producing summaries that are more effective than summaries produced by two pre-existing generic summarisers.

This paper describes the FinTOC-2022 Shared Task on the structure extraction from financial documents, its participants results and their findings. This shared task was organized as part of The 4th Financial Narrative Processing Workshop (FNP 2022), held jointly at The 13th Edition of the Language Resources and Evaluation Conference (LREC 2022), Marseille, France (El-Haj et al., 2022). This shared task aimed to stimulate research in systems for extracting table-of-contents (TOC) from investment documents (such as financial prospectuses) by detecting the document titles and organizing them hierarchically into a TOC. For the forth edition of this shared task, three subtasks were presented to the participants: one with English documents, one with French documents and the other one with Spanish documents. This year, we proposed a different and revised dataset for English and French compared to the previous editions of FinTOC and a new dataset for Spanish documents was added. The task attracted 6 submissions for each language from 4 teams, and the most successful methods make use of textual, structural and visual features extracted from the documents and propose classification models for detecting titles and TOCs for all of the subtasks.

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ISPRAS@FinTOC-2022 Shared Task: Two-stage TOC Generation Model
Anastasiia Bogatenkova | Oksana Vladimirovna Belyaeva | Andrew Igorevich Perminov | Ilya Sergeevich Kozlov

This work is connected with participation in FinTOC-2022 Shared Task: “Financial Document Structure Extraction”. The competition contains two subtasks: title detection and TOC generation. We describe an approach for solving these tasks and propose the pipeline, consisting of extraction of document lines and existing TOC, feature matrix forming and classification. Classification model consists of two classifiers: the first binary classifier separates title lines from non-title, the second one determines the title level. In the title detection task, we got 0.900, 0.778 and 0.558 F1 measure, in the TOC generation task we got 63.1, 41.5 and 40.79 the harmonic mean of Inex F1 score and Inex level accuracy for English, French and Spanish documents respectively. With these results, our approach took first place among English and French submissions and second place among Spanish submissions. As a team, we took first place in the competition in English and French categories and second place in the competition in Spanish.

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swapUNIBA@FinTOC2022: Fine-tuning Pre-trained Document Image Analysis Model for Title Detection on the Financial Domain
Pierluigi Cassotti | Cataldo Musto | Marco DeGemmis | Georgios Lekkas | Giovanni Semeraro

In this paper, we introduce the results of our submitted system to the FinTOC 2022 task. We address the task using a two-stage process: first, we detect titles using Document Image Analysis, then we train a supervised model for the hierarchical level prediction. We perform Document Image Analysis using a pre-trained Faster R-CNN on the PublyaNet dataset. We fine-tuned the model on the FinTOC 2022 training set. We extract orthographic and layout features from detected titles and use them to train a Random Forest model to predict the title level. The proposed system ranked #1 on both Title Detection and the Table of Content extraction tasks for Spanish. The system ranked #3 on both the two subtasks for English and French.

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GREYC@FinTOC-2022: Handling Document Layout and Structure in Native PDF Bundle of Documents
Emmanuel Giguet | Nadine Lucas

n this paper, we present our contribution to the FinTOC-2022 Shared Task “Financial Document Structure Extraction”. We participated in the three tracks dedicated to English, French and Spanish document processing. Our main contribution consists in considering financial prospectus as a bundle of documents, i.e., a set of merged documents, each with their own layout and structure. Therefore, Document Layout and Structure Analysis (DLSA) first starts with the boundary detection of each document using general layout features. Then, the process applies inside each single document, taking advantage of the local properties. DLSA is achieved considering simultaneously text content, vectorial shapes and images embedded in the native PDF document. For the Title Detection task in English and French, we observed a significant improvement of the F-measures for Title Detection compared with those obtained during our previous participation.

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The Financial Causality Extraction Shared Task (FinCausal 2022)
Dominique Mariko | Hanna Abi-Akl | Kim Trottier | Mahmoud El-Haj

We present the FinCausal 2020 Shared Task on Causality Detection in Financial Documents and the associated FinCausal dataset, and discuss the participating systems and results. The task focuses on detecting if an object, an event or a chain of events is considered a cause for a prior event. This shared task focuses on determining causality associated with a quantified fact. An event is defined as the arising or emergence of a new object or context in regard to a previous situation. Therefore, the task will emphasise the detection of causality associated with transformation of financial objects embedded in quantified facts. A total number of 7 teams submitted system runs to the FinCausal task and contributed with a system description paper. FinCausal shared task is associated with the 4th Financial Narrative Processing Workshop (FNP 2022) (El-Haj et al., 2022) which is held at the The 13th Language Resources and Evaluation Conference (LREC 2022) in Marseille, France, on June 24, 2022.

Causal information extraction is an important task in natural language processing, particularly in finance domain. In this work, we develop several information extraction models using pre-trained transformer-based language models for identifying cause and effect text spans from financial documents. We use FinCausal 2021 and 2022 data sets to train span-based and sequence tagging models. Our ensemble of sequence tagging models based on the RoBERTa-Large pre-trained language model achieves an F1 score of 94.70 with Exact Match score of 85.85 and obtains the 1st place in the FinCausal 2022 competition.

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Multilingual Financial Documentation Summarization by Team_Tredence for FNS2022
Manish Pant | Ankush Chopra

This paper describes multi-lingual long document summarization systems submitted to the Financial Narrative Summarization Shared Task (FNS 2022 ) by Team-Tredence. We developed task-specific summarization methods for 3 languages – English, Spanish and Greek. The solution is divided into two parts, where a RoBERTa model was finetuned to identify/extract summarizing segments from English documents and T5 based models were used for summarizing Spanish and Greek documents. A purely extractive approach was applied to summarize English documents using data-specific heuristics. An mT5 model was fine-tuned to identify potential narrative sections for Greek and Spanish, followed by finetuning mT5 and T5(Spanish version) for abstractive summarization task. This system also features a novel approach for generating summarization training dataset using long document segmentation and the semantic similarity across segments. We also introduce an N-gram variability score to select sub-segments for generating more diverse and informative summaries from long documents.

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DCU-Lorcan at FinCausal 2022: Span-based Causality Extraction from Financial Documents using Pre-trained Language Models
Chenyang Lyu | Tianbo Ji | Quanwei Sun | Liting Zhou

In this paper, we describe our DCU-Lorcan system for the FinCausal 2022 shared task: span-based cause and effect extraction from financial documents. We frame the FinCausal 2022 causality extraction task as a span extraction/sequence labeling task, our submitted systems are based on the contextualized word representations produced by pre-trained language models and linear layers predicting the label for each word, followed by post-processing heuristics. In experiments, we employ pre-trained language models including DistilBERT, BERT and SpanBERT. Our best performed system achieves F-1, Recall, Precision and Exact Match scores of 92.76, 92.77, 92.76 and 68.60 respectively. Additionally, we conduct experiments investigating the effect of data size to the performance of causality extraction model and an error analysis investigating the outputs in predictions.

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LIPI at FinCausal 2022: Mining Causes and Effects from Financial Texts
Sohom Ghosh | Sudip Naskar

While reading financial documents, investors need to know the causes and their effects. This empowers them to make data-driven decisions. Thus, there is a need to develop an automated system for extracting causes and their effects from financial texts using Natural Language Processing. In this paper, we present the approach our team LIPI followed while participating in the FinCausal 2022 shared task. This approach is based on the winning solution of the first edition of FinCausal held in the year 2020.

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iLab at FinCausal 2022: Enhancing Causality Detection with an External Cause-Effect Knowledge Graph
Ziwei Xu | Rungsiman Nararatwong | Natthawut Kertkeidkachorn | Ryutaro Ichise

The application of span detection grows fast along with the increasing need of understanding the causes and effects of events, especially in the finance domain. However, once the syntactic clues are absent in the text, the model tends to reverse the cause and effect spans. To solve this problem, we introduce graph construction techniques to inject cause-effect graph knowledge for graph embedding. The graph features combining with BERT embedding, then are used to predict the cause effect spans. The results show our proposed graph builder method outperforms the other methods w/wo external knowledge.

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ExpertNeurons at FinCausal 2022 Task 2: Causality Extraction for Financial Documents
Joydeb Mondal | Nagaraj Bhat | Pramir Sarkar | Shahid Reza

In this paper describes the approach which we have built for causality extraction from the financial documents that we have submitted for FinCausal 2022 task 2. We proving a solution with intelligent pre-processing and post-processing to detect the number of cause and effect in a financial document and extract them. Our given approach achieved 90% as F1 score(weighted-average) for the official blind evaluation dataset.

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ATL at FinCausal 2022: Transformer Based Architecture for Automatic Causal Sentence Detection and Cause-Effect Extraction
Abir Naskar | Tirthankar Dasgupta | Sudeshna Jana | Lipika Dey

Automatic extraction of cause-effect relationships from natural language texts is a challenging open problem in Artificial Intelligence. Most of the early attempts at its solution used manually constructed linguistic and syntactic rules on restricted domain data sets. With the advent of big data, and the recent popularization of deep learning, the paradigm to tackle this problem has slowly shifted. In this work we proposed a transformer based architecture to automatically detect causal sentences from textual mentions and then identify the corresponding cause-effect relations. We describe our submission to the FinCausal 2022 shared task based on this method. Our model achieves a F1-score of 0.99 for the Task-1 and F1-score of 0.60 for Task-2 on the shared task data set on financial documents.

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MNLP at FinCausal2022: Nested NER with a Generative Model
Jooyeon Lee | Luan Huy Pham | Özlem Uzuner

This paper describes work performed for the FinCasual 2022 Shared Task “Financial Document Causality Detection” (FinCausal 2022). As the name implies, the task involves extraction of casual and consequential elements from financial text. Our approach focuses employing Nested NER using the Text-to-Text Transformer (T5) generative transformer models while applying different combinations of datasets and tagging methods. Our system reports accuracy of 79% in Exact Match comparison and F-measure score of 92% token level measurement.

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bib (full) Proceedings of the 9th Workshop on Games and Natural Language Processing within the 13th Language Resources and Evaluation Conference

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Proceedings of the 9th Workshop on Games and Natural Language Processing within the 13th Language Resources and Evaluation Conference
Chris Madge

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An Analysis of Abusive Language Data Collected through a Game with a Purpose
Federico Bonetti | Sara Tonelli

In this work we present an analysis of abusive language annotations collected through a 3D video game. With this approach, we are able to involve in the annotation teenagers, i.e. typical targets of cyberbullying, whose data are usually not available for research purposes. Using the game in the framework of educational activities to empower teenagers against online abuse we are able to obtain insights into how teenagers communicate, and what kind of messages they consider more offensive. While players produced interesting annotations and the distributions of classes between players and experts are similar, we obtained a significant number of mismatching judgements between experts and players.

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Applying Gamification Incentives in the Revita Language-learning System
Jue Hou | Ilmari Kylliäinen | Anisia Katinskaia | Giacomo Furlan | Roman Yangarber

We explore the importance of gamification features in a language-learning platform designed for intermediate-to-advanced learners. Our main thesis is: learning toward advanced levels requires a massive investment of time. If the learner engages in more practice sessions, and if the practice sessions are longer, we can expect the results to be better. This principle appears to be tautologically self-evident. Yet, keeping the learner engaged in general—and building gamification features in particular—requires substantial efforts on the part of developers. Our goal is to keep the learner engaged in long practice sessions over many months—rather than for the short-term. This creates a conflict: In academic research on language learning, resources are typically scarce, and gamification usually is not considered an essential priority for allocating resources. We argue in favor of giving serious consideration to gamification in the language-learning setting—as a means of enabling in-depth research. In this paper, we introduce several gamification incentives in the Revita language-learning platform. We discuss the problems in obtaining quantitative measures of the effectiveness of gamification features.

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Less Text, More Visuals: Evaluating the Onboarding Phase in a GWAP for NLP
Fatima Althani | Chris Madge | Massimo Poesio

Games-with-a-purpose find attracting players a challenge. To improve player recruitment, we explored two game design elements that can increase player engagement during the onboarding phase; a narrative and a tutorial. In a qualitative study with 12 players of linguistic and language learning games, we examined the effect of presentation format on players’ engagement. Our reflexive thematic analysis found that in the onboarding phase of a GWAP for NLP, presenting players with visuals is expected and pre- senting too much text overwhelms them. Furthermore, players found that the instructions they were presented with lacked linguistic context. Additionally, the tutorial and game interface required refinement as the feedback is unsupportive and the graphics were not clear.

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NLU for Game-based Learning in Real: Initial Evaluations
Eda Okur | Saurav Sahay | Lama Nachman

Intelligent systems designed for play-based interactions should be contextually aware of the users and their surroundings. Spoken Dialogue Systems (SDS) are critical for these interactive agents to carry out effective goal-oriented communication with users in real-time. For the real-world (i.e., in-the-wild) deployment of such conversational agents, improving the Natural Language Understanding (NLU) module of the goal-oriented SDS pipeline is crucial, especially with limited task-specific datasets. This study explores the potential benefits of a recently proposed transformer-based multi-task NLU architecture, mainly to perform Intent Recognition on small-size domain-specific educational game datasets. The evaluation datasets were collected from children practicing basic math concepts via play-based interactions in game-based learning settings. We investigate the NLU performances on the initial proof-of-concept game datasets versus the real-world deployment datasets and observe anticipated performance drops in-the-wild. We have shown that compared to the more straightforward baseline approaches, Dual Intent and Entity Transformer (DIET) architecture is robust enough to handle real-world data to a large extent for the Intent Recognition task on these domain-specific in-the-wild game datasets.

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How NLP Can Strengthen Digital Game Based Language Learning Resources for Less Resourced Languages
Monica Ward | Liang Xu | Elaine Uí Dhonnchadha

This paper provides an overview of the Cipher engine which enables the development of a Digital Educational Game (DEG) based on noticing ciphers or patterns in texts. The Cipher engine was used to develop the Cipher: Faoi Gheasa, a digital educational game for Irish, which incorporates NLP resources and is informed by Digital Game-Based Language Learning (DGBLL) and Computer-Assisted Language Learning (CALL) research. The paper outlines six phases where NLP has strengthened the Cipher: Faoi Gheasa game. It shows how the Cipher engine can be used to build a Cipher game for other languages, particularly low-resourced and endangered languages in which NLP resources are under-developed or few in number.

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The “Actors Challenge” Project: Collecting Data on Intonation Profiles via a Web Game
Natallia Chaiko | Sia Sepanta | Roberto Zamparelli

This paper describes ”Actors Challenge”, a soon-to-go-public web game where the players alternate in the double role of actors and judges of other players’ acted-out utterances, and in the process create an oral dataset of prosodic contours that can disambiguate textually identical utterances in different contexts. The game is undergoing alpha testing and should be deployed within a few months. We discuss the need, the core mechanism and the challenges ahead.

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Generating Descriptive and Rules-Adhering Spells for Dungeons & Dragons Fifth Edition
Pax Newman | Yudong Liu

We examine the task of generating unique content for the spell system of the tabletop roleplaying game Dungeons and Dragons Fifth Edition using several generative language models. Due to the descriptive nature of the game Dungeons and Dragons Fifth Edition, it presents a number of interesting avenues for generation and analysis of text. In particular, the “spell” system of the game has interesting and unique characteristics as it is primarily made up of high level and descriptive text but has many of the game’s main rules embedded with that text. Thus, we examine the capabilities of several models on the task of generating new content for this game, evaluating the performance through the use of both score-based methods and a survey on the best performing model to determine how the generated content conforms to the rules of the game and how well they might be used in the game.

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bib (full) Proceedings of Globalex Workshop on Linked Lexicography within the 13th Language Resources and Evaluation Conference

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Proceedings of Globalex Workshop on Linked Lexicography within the 13th Language Resources and Evaluation Conference
Ilan Kernerman | Simon Krek

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Towards the Profiling of Linked Lexicographic Resources
Lenka Bajcetic | Seung-bin Yim | Thierry Declerck

This paper presents Edie: ELEXIS DIctionary Evaluator. Edie is designed to create profiles for lexicographic resources accessible through the ELEXIS platform. These profiles can be used to evaluate and compare lexicographic resources, and in particular they can be used to identify potential data that could be linked.

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Towards the Linking of a Sign Language Ontology with Lexical Data
Thierry Declerck

We describe our current work for linking a new ontology for representing constitutive elements of Sign Languages with lexical data encoded within the OntoLex-Lemon framework. We first present very briefly the current state of the ontology, and show how transcriptions of signs can be represented in OntoLex-Lemon, in a minimalist manner, before addressing the challenges of linking the elements of the ontology to full lexical descriptions of the spoken languages.

Following presentations of frequency and attestations, and embeddings and distributional similarity, this paper introduces the third cornerstone of the emerging OntoLex module for Frequency, Attestation and Corpus-based Information, OntoLex-FrAC. We provide an RDF vocabulary for collocations, established as a consensus over contributions from five different institutions and numerous data sets, with the goal of eliciting feedback from reviewers, workshop audience and the scientific community in preparation of the final consolidation of the OntoLex-FrAC module, whose publication as a W3C community report is foreseen for the end of this year. The novel collocation component of OntoLex-FrAC is described in application to a lexicographic resource and corpus-based collocation scores available from the web, and finally, we demonstrate the capability and genericity of the model by showing how to retrieve and aggregate collocation information by means of SPARQL, and its export to a tabular format, so that it can be easily processed in downstream applications.

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TIAD 2022: The Fifth Translation Inference Across Dictionaries Shared Task
Jorge Gracia | Besim Kabashi | Ilan Kernerman

The objective of the Translation Inference Across Dictionaries (TIAD) series of shared tasks is to explore and compare methods and techniques that infer translations indirectly between language pairs, based on other bilingual/multilingual lexicographic resources. In this fifth edition, the participating systems were asked to generate new translations automatically among three languages - English, French, Portuguese - based on known indirect translations contained in the Apertium RDF graph. Such evaluation pairs have been the same during the four last TIAD editions. Since the fourth edition, however, a larger graph is used as a basis to produce the translations, namely Apertium RDF v2. The evaluation of the results was carried out by the organisers against manually compiled language pairs of K Dictionaries. For the second time in the TIAD series, some systems beat the proposed baselines. This paper gives an overall description of the shard task, the evaluation data and methodology, and the systems’ results.

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Creating Bilingual Dictionaries from Existing Ones by Means of Pivot-Oriented Translation Inference and Logistic Regression
Yves Bestgen

To produce new bilingual dictionaries from existing ones, an important task in the field of translation, a system based on a very classical supervised learning technique, with no other knowledge than the available bilingual dictionaries, is proposed. It performed very well in the Translation Inference Across Dictionaries (TIAD) shared task on the combined 2021 and 2022 editions. An analysis of the pros and cons suggests a series of avenues to further improve its effectiveness.

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Compiling a Highly Accurate Bilingual Lexicon by Combining Different Approaches
Steinþór Steingrímsson | Luke O’Brien | Finnur Ingimundarson | Hrafn Loftsson | Andy Way

Bilingual lexicons can be generated automatically using a wide variety of approaches. We perform a rigorous manual evaluation of four different methods: word alignments on different types of bilingual data, pivoting, machine translation and cross-lingual word embeddings. We investigate how the different setups perform using publicly available data for the English-Icelandic language pair, doing separate evaluations for each method, dataset and confidence class where it can be calculated. The results are validated by human experts, working with a random sample from all our experiments. By combining the most promising approaches and data sets, using confidence scores calculated from the data and the results of manually evaluating samples from our manual evaluation as indicators, we are able to induce lists of translations with a very high acceptance rate. We show how multiple different combinations generate lists with well over 90% acceptance rate, substantially exceeding the results for each individual approach, while still generating reasonably large candidate lists. All manually evaluated equivalence pairs are published in a new lexicon of over 232,000 pairs under an open license.

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A Category Theory Framework for Sense Systems
David Strohmaier | Gladys Tyen

Sense repositories are a key component of many NLP applications that require the identification of word senses. Many sense repositories exist: a large proportion is based on lexicographic resources such as WordNet and various dictionaries, but there are others which are the product of clustering algorithms and other automatic techniques. Over the years these repositories have been mapped to each other. However, there have been no attempts (until now) to provide any theoretical grounding for such mappings, causing inconsistencies and unintuitive results. The present paper draws on category theory to formalise assumptions about mapped repositories that are often left implicit, providing formal grounding for this type of language resource. The paper first gives an overview of the word sense disambiguation literature and four types of sense representations: dictionary definitions, clusters of senses, domain labels, and embedding vectors. These different sense representations make different assumptions about the relations and mappings between word senses. We then introduce notation to represent the mappings and repositories as a category, which we call a “sense system”. We represent a sense system as a small category S, where the object set of S, denoted by Ob(S), is a set of sense repositories; and the homomorphism set or hom-set of S, denoted by Hom(S), is a set of mappings between these repositories. On the basis of the sense system description, we propose, formalise, and motivate four basic and two guiding criteria for such sense systems. The four basic criteria are: 1) Correctness preservation: Mappings should preserve the correctness of sense labels in all contexts. Intuitively, if the correct sense for a word token is mapped to another sense, this sense should also be correct for that token. This criterion is endorsed by virtually all existing mappings, but the formalism presented in the paper makes this assumption explicit and allows us to distinguish it from other criteria. 2) Candidacy preservation: Mappings should preserve what we call “the lexical candidacy” of sense labels. Assume that a sense s is mapped to another sense s’ in a different repository. Candidacy preservation then requires that if s is a sense associated with word type w, then so is s’. This criterion is trivially fulfilled by clustering-based approaches, but is not typically explicitly stated for repositories, and we demonstrate how a violation might occur. Our formalisation allows us to specify the difference of this criterion to correctness preservation. As we argue, candidacy preservation allows us to straightforwardly and consistently compare granularity levels by counting the number of senses for each word type. 3) Uniqueness criterion: There should be at most one mapping from one repository to another. This criterion is also fulfilled by clustering-based approaches, but is often violated by repositories that use domain labels. We argue that adhering to the uniqueness criterion provides several benefits, including: a) being able to consistently convert between sets of labels and evaluation metrics, allowing researchers to work with data and models that use different sets of labels; b) ensuring that sense repositories would form a partial preorder, which would roughly correspond to the notion of granularity; and c) ensuring transitivity of mapped senses. 4) Connectivity: A sense system should be a connected category. The connectivity criterion on its own is not very informative, but it enables other criteria by extending their benefits to the rest of the sense system, such as allowing cross-checking between multiple repositories, allowing comparison of grain level, and label conversion. As we argue, connectivity should be considered a formal requirement helping to describe sense repositories and how they relate. We also offer two guiding criteria, which we consider aspirational rather than requirements that have to be strictly fulfilled for all purposes: 1) Non-contradiction: Mappings cannot exist between senses that semantically contradict each other. The non-contradiction criterion forbids mappings between senses whose (strict) implications contradict each other. We demonstrate how such a contradiction might occur, but acknowledge the difficulty in identifying such contradictions. As we argue, the reason to consider this a guiding rather than a strict criterion is that many sense repositories lack the semantic specificity that would allow researchers to identify these contradictions. 2) Inter-annotator agreement: Mappings should correspond to a partial preorder of inter-annotator agreement levels. It has been observed that, when annotating corpora with senses from a given sense repository, inter-annotator agreement tends to drop when the repository is more fine-grained. Therefore, if one repository is coarser-grained than another, one can expect agreement levels to be higher when annotating corpora with senses from the first repository. While this criterion will necessarily be subject to empirical variability (and does apply to sense repositories using non-interpretable representations such as embeddings), we argue that strong violations suggest that the sense distinctions of the coarse-grained sense repository are unnatural, i.e. not in accordance with human linguistic intuitions. Our list is by no means exhaustive, as there are other properties that may be desirable depending on the downstream application. Our category-theory based formalism will serve as the basis for describing any such further properties. However, we also envision that the criteria we have proposed will serve as guidelines for future sense repositories and mappings, in order to avoid the inconsistencies and counterintuitive results derived from existing mappings.

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Converting a Database of Complex German Word Formation for Linked Data
Petra Steiner

This work combines two lexical resources with morphological information on German word formation, CELEX for German and the latest release of GermaNet, for extracting and building complex word structures. This yields a database of over 100,000 German wordtrees. A definition for sequential morphological analyses leads to a Ontolex-Lemon type model. By using GermaNet sense information, the data can be linked to other semantic resources. An alignment to the CIDOC Conceptual Reference Model (CIDOC-CRM) is also provided. The scripts for the data generation are publicly available on GitHub.

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Resolving Inflectional Ambiguity of Macedonian Adjectives
Katerina Zdravkova

Macedonian adjectives are inflected for gender, number, definiteness and degree, with in average 47.98 inflections per headword. The inflection paradigm of qualificative adjectives is even richer, embracing 56.27 morphophonemic alterations. Depending on the word they were derived from, more than 600 Macedonian adjectives have an identical headword and two different word forms for each grammatical category. While non-verbal adjectives alter the root before adding the inflectional suffixes, suffixes of verbal adjectives are added directly to the root. In parallel with the morphological differences, both types of adjectives have a different translation, depending on the category of the words they have been derived from. Nouns that collocate with these adjectives are mutually disjunctive, enabling the resolution of inflectional ambiguity. They are organised as a lexical taxonomy, created using hierarchical divisive clustering. If embedded in the future spell-checking applications, this taxonomy will significantly reduce the risk of forming incorrect inflections, which frequently occur in the daily news and more often in the advertisements and social media.

MorphoLex is a study in which root, prefix and suffixes of words are analyzed. With MorphoLex, many words can be analyzed according to certain rules and a useful database can be created. Due to the fact that Turkish is an agglutinative language and the richness of its language structure, it offers different analyzes and results from previous studies in MorphoLex. In this study, we revealed the process of creating a database with 48,472 words and the results of the differences in language structure.

Wordnets have been popular tools for providing and representing semantic and lexical relations of languages. They are useful tools for various purposes in NLP studies. Many researches created WordNets for different languages. For Turkish, there are two WordNets, namely the Turkish WordNet of BalkaNet and KeNet. In this paper, we present new WordNets for Turkish each of which is based on one of the first 9 editions of the Turkish dictionary starting from the 1944 edition. These WordNets are historical in nature and make implications for Modern Turkish. They are developed by extending KeNet, which was created based on the 2005 and 2011 editions of the Turkish dictionary. In this paper, we explain the steps in creating these 9 new WordNets for Turkish, discuss the challenges in the process and report comparative results about the WordNets.

This paper aims to present WordNet and Wikipedia connection by linking synsets from Turkish WordNet KeNet with Wikipedia and thus, provide a better machine-readable dictionary to create an NLP model with rich data. For this purpose, manual mapping between two resources is realized and 11,478 synsets are linked to Wikipedia. In addition to this, automatic linking approaches are utilized to analyze possible connection suggestions. Baseline Approach and ElasticSearch Based Approach help identify the potential human annotation errors and analyze the effectiveness of these approaches in linking. Adopting both manual and automatic mapping provides us with an encompassing resource of WordNet and Wikipedia connections.

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Homonymy Information for English WordNet
Rowan Hall Maudslay | Simone Teufel

A widely acknowledged shortcoming of WordNet is that it lacks a distinction between word meanings which are systematically related (polysemy), and those which are coincidental (homonymy). Several previous works have attempted to fill this gap, by inferring this information using computational methods. We revisit this task, and exploit recent advances in language modelling to synthesise homonymy annotation for Princeton WordNet. Previous approaches treat the problem using clustering methods; by contrast, our method works by linking WordNet to the Oxford English Dictionary, which contains the information we need. To perform this alignment, we pair definitions based on their proximity in an embedding space produced by a Transformer model. Despite the simplicity of this approach, our best model attains an F1 of .97 on an evaluation set that we annotate. The outcome of our work is a high-quality homonymy annotation layer for Princeton WordNet, which we release.

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bib (full) Proceedings of the 18th Joint ACL - ISO Workshop on Interoperable Semantic Annotation within LREC2022

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Proceedings of the 18th Joint ACL - ISO Workshop on Interoperable Semantic Annotation within LREC2022
Harry Bunt

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What Is Going through Your Mind? Metacognitive Events Classification in Human-Agent Interactions
Hafiza Erum Manzoor | Volha Petukhova

For an agent, either human or artificial, to show intelligent interactive behaviour implies assessments of the reliability of own and others’ thoughts, feelings and beliefs. Agents capable of these robust evaluations are able to adequately interpret their own and others’ cognitive and emotional processes, anticipate future actions, and improve their decision-making and interactive performances across domains and contexts. Reliable instruments to assess interlocutors’ mindful capacities for monitoring and regulation - metacognition - in human-agent interaction in real-time and continuously are of crucial importance however challenging to design. The presented study reports Concurrent Think Aloud (CTA) experiments in order to access and evaluate metacognitive dispositions and attitudes of participants in human-agent interactions. A typology of metacognitive events related to the ‘verbalized’ monitoring, interpretation, reflection and regulation activities observed in a multimodal dialogue has been designed, and serves as a valid tool to identify relation between participants’ behaviour analysed in terms of ISO 24617-2 compliant dialogue acts and the corresponding metacognitive indicators.

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Assessment of Sales Negotiation Strategies with ISO 24617-2 Dialogue Act Annotations
Jutta Stock | Volha Petukhova | Dietrich Klakow

Call centres endeavour to achieve the highest possible level of transparency with regard to the factors influencing sales success. Existing approaches to the quality assessment of customer-agent sales negotiations do not enable in-depths analysis of sales behaviour. This study addresses this gap and presents a conceptual and operational framework applying the ISO 24617-2 dialogue act annotation scheme, a multidimensional taxonomy of interoperable semantic concepts. We hypothesise that the ISO 24617-2 dialogue act annotation framework adequately supports sales negotiation assessment in the domain of call centre conversations. Authentic call centre conversations are annotated and a range of extensions/modifications are proposed making the annotation scheme better fit this new domain. We concluded that ISO 24617-2 serves as a powerful instrument for the analysis and assessment of sales negotiation and strategies applied by a call centre agent.

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Guidelines and a Corpus for Extracting Biographical Events
Marco Antonio Stranisci | Enrico Mensa | Rossana Damiano | Daniele Radicioni | Ousmane Diakite

Despite biographies are widely spread within the Semantic Web, resources and approaches to automatically extract biographical events are limited. Such limitation reduces the amount of structured, machine-readable biographical information, especially about people belonging to underrepresented groups. Our work challenges this limitation by providing a set of guidelines for the semantic annotation of life events. The guidelines are designed to be interoperable with existing ISO-standards for semantic annotation: ISO-TimeML (SO-24617-1), and SemAF (ISO-24617-4). Guidelines were tested through an annotation task of Wikipedia biographies of underrepresented writers, namely authors born in non-Western countries, migrants, or belonging to ethnic minorities. 1,000 sentences were annotated by 4 annotators with an average Inter-Annotator Agreement of 0.825. The resulting corpus was mapped on OntoNotes. Such mapping allowed to to expand our corpus, showing that already existing resources may be exploited for the biographical event extraction task.

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Levels of Non-Fictionality in Fictional Texts
Florian Barth | Hanna Varachkina | Tillmann Dönicke | Luisa Gödeke

The annotation and automatic recognition of non-fictional discourse within a text is an important, yet unresolved task in literary research. While non-fictional passages can consist of several clauses or sentences, we argue that 1) an entity-level classification of fictionality and 2) the linking of Wikidata identifiers can be used to automatically identify (non-)fictional discourse. We query Wikidata and DBpedia for relevant information about a requested entity as well as the corresponding literary text to determine the entity’s fictionality status and assign a Wikidata identifier, if unequivocally possible. We evaluate our methods on an exemplary text from our diachronic literary corpus, where our methods classify 97% of persons and 62% of locations correctly as fictional or real. Furthermore, 75% of the resolved persons and 43% of the resolved locations are resolved correctly. In a quantitative experiment, we apply the entity-level fictionality tagger to our corpus and conclude that more non-fictional passages can be identified when information about real entities is available.

TIE-ML (Temporal Information Event Markup Language) first proposed by Cavar et al. (2021) provides a radically simplified temporal annotation schema for event sequencing and clause level temporal properties even in complex sentences. TIE-ML facilitates rapid annotation of essential tense features at the clause level by labeling simple or periphrastic tense properties, as well as scope relations between clauses, and temporal interpretation at the sentence level. This paper presents the first annotation samples and empirical results. The application of the TIE-ML strategy on the sentences in the Penn Treebank (Marcus et al., 1993) and other non-English language data is discussed in detail. The motivation, insights, and future directions for TIE-ML are discussed, too. The aim is to develop a more efficient annotation strategy and a formalism for clause-level tense and aspect labeling, event sequencing, and tense scope relations that boosts the productivity of tense and event-level corpus annotation. The central goal is to facilitate the production of large data sets for machine learning and quantitative linguistic studies of intra- and cross-linguistic semantic properties of temporal and event logic.

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Measuring Similarity by Linguistic Features rather than Frequency
Rodolfo Delmonte | Nicolò Busetto

In the use and creation of current Deep Learning Models the only number that is used for the overall computation is the frequency value associated with the current word form in the corpus, which is used to substitute it. Frequency values come in two forms: absolute and relative. Absolute frequency is used indirectly when selecting the vocabulary against which the word embeddings are created: the cutoff threshold is usually fixed at 30/50K entries of the most frequent words. Relative frequency comes in directly when computing word embeddings based on co-occurrence values of the tokens included in a window size 2/5 adjacent tokens. The latter values are then used to compute similarity, mostly based on cosine distance. In this paper we will evaluate the impact of these two frequency parameters on a small corpus of Italian sentences whose main features are two: presence of very rare words and of non-canonical structures. Rather than basing our evaluation on cosine measure alone, we propose a graded scale of scores which are linguistically motivated. The results computed on the basis of a perusal of BERT’s raw embeddings shows that the two parameters conspire to decide the level of predictability.

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Testing the Annotation Consistency of Hallidayan Transitivity Processes: A Multi-variable Structural Approach
Min Dong | Xiaoyan Liu | Alex Chengyu Fang

SFL seeks to explain identifiable, observable phenomena of language use in context through the application of a theoretical framework which models language as a functional, meaning making system (Halliday & Matthiessen 2004). Due to the lack of explicit annotation criteria and the divide between conceptual vs. syntactic criteria in practice, it has been a tough job to achieve consistency in the annotation of Hallidayn transitivity processes. The present study proposed that explicit structural and syntactic criteria should be adopted as a basis. Drawing on syntactic and grammatical features as judgement cues, we applied structurally oriented criteria for the annotation of the process categories and participant roles combining a set of interrelated syntactic variables and established the annotation criteria for contextualised circumstantial categories in structural as well as semantic terms. An experiment was carried out to test the usefulness of these annotation criteria, applying percent agreement and Cohen’s kappa as measurements of interrater reliability between the two annotators in each of the five pairs. The results verified our assumptions, albeit rather mildly, and, more significantly, offered some first empirical indications about the practical consistency of transitivity analysis in SFL. In the future work, the research team expect to draw on the insights and experience from some of the ISO standards devoted to semantic annotation such as dialogue acts (Bunt et al. 2012) and semantic roles (ISO-24617-4, 2014).

Reasoning about spatial information is fundamental in natural language to fully understand relationships between entities and/or between events. However, the complexity underlying such reasoning makes it hard to represent formally spatial information. Despite the growing interest on this topic, and the development of some frameworks, many problems persist regarding, for instance, the coverage of a wide variety of linguistic constructions and of languages. In this paper, we present a proposal of integrating ISO-Space into a ISO-based multilayer annotation scheme, designed to annotate news in European Portuguese. This scheme already enables annotation at three levels, temporal, referential and thematic, by combining postulates from ISO 24617-1, 4 and 9. Since the corpus comprises news articles, and spatial information is relevant within this kind of texts, a more detailed account of space was required. The main objective of this paper is to discuss the process of integrating ISO-Space with the existing layers of our annotation scheme, assessing the compatibility of the aforementioned parts of ISO 24617, and the problems posed by the harmonization of the four layers and by some specifications of ISO-Space.

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Do machines dream of artificial agreement?
Anna Lindahl

In this paper the (assumed) inconsistency between F1-scores and annotator agreement measures is discussed. This is exemplified in five corpora from the field of argumentation mining. High agreement is important in most annotation tasks and also often deemed important for an annotated dataset to be useful for machine learning. However, depending on the annotation task, achieving high agreement is not always easy. This is especially true in the field of argumentation mining, because argumentation can be complex as well as implicit. There are also many different models of argumentation, which can be seen in the increasing number of argumentation annotated corpora. Many of these reach moderate agreement but are still used in machine learning tasks, reaching high F1-score. In this paper we describe five corpora, in particular how they have been created and used, to see how they have handled disagreement. We find that agreement can be raised post-production, but that more discussion regarding evaluating and calculating agreement is needed. We conclude that standardisation of the models and the evaluation methods could help such discussions.

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CroaTPAS: A Survey-based Evaluation
Costanza Marini

The Croatian Typed Predicate Argument Structures resource is a Croatian/English bilingual digital dictionary of corpus-derived verb valency structures, whose argument slots have been annotated with Semantic Types labels following the CPA methodology. CroaTPAS is tailor-made to represent verb polysemy and currently contains 180 Croatian verbs for a total of 683 different verbs senses. In order to evaluate the resource both in terms of identified Croatian verb senses, as well as of the English descriptions explaining them, an online survey based on a multiple-choice sense disambiguation task was devised, pilot tested and distributed among respondents following a snowball sampling methodology. Answers from 30 respondents were collected and compared against a yardstick set of answers in line with CroaTPAS’s sense distinctions. Jaccard similarity index was used as a measure of agreement. Since the multiple-choice items respondents answered to were based on a representative selection of CroaTPAS verbs, they allowed for a generalization of the results to the whole of the resource.

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Simplifying Semantic Annotations of SMCalFlow
Joram Meron

SMCalFlow (Semantic Machines et al., 2020) is a large corpus of semantically detailed annotations of task-oriented natural dialogues. The annotations use a dataflow approach, in which the annotations are programs which represent user requests. Despite the availability, size and richness of this annotated corpus, it has seen only very limited use in dialogue systems research work, at least in part due to the difficulty in understanding and using the annotations. To address these difficulties, this paper suggests a simplification of the SMCalFlow annotations, as well as releases code needed to inspect the execution of the annotated dataflow programs, which should allow researchers of dialogue systems an easy entry point to experiment with various dataflow based implementations and annotations.

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Multilingual Reference Annotation: A Case between English and Mandarin Chinese
Ut Seong Sio | Luís Morgado da Costa

This paper presents the on-going effort to annotate a cross-lingual corpus on nominal referring expressions in English and Mandarin Chinese. The annotation includes referential forms and referential (information) statuses. We adopt the RefLex annotation scheme (Baumann and Riester, 2012) for the classification of referential statuses. The data focus of this paper is restricted to [the-X] phrases in English (where X stands for any nominal) and their translation equivalents in Mandarin Chinese. The original English and translated Mandarin versions of ‘The Adventure of the Dancing Men’ and ‘The Adventure of Speckled Band’ from the Sherlock Holmes series were annotated. It contains 1090 instances of [the-X] phrases in English. Our study uncovers the following: (i) bare nouns are the most common Mandarin translation for [the-X] phrases in English, followed by demonstrative phrases, with the exception that when the noun phrase refers to locations/places, in such cases, demonstrative phrases are almost never used; (ii) [the-X] phrases in English are more likely to be translated as demonstrative phrases in Mandarin if they have the referential status of ‘given’ (previously mentioned) or ‘given-displaced’(antecedent of an expression occurs earlier than the previous five clauses). In these Mandarin demonstrative phrases, the proximal demonstrative is more often used and it is almost exclusively used for ‘given’ while the distal demonstrative can be used for both ‘given’ and ‘given-displaced’.

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Graph Querying for Semantic Annotations
Maxime Amblard | Bruno Guillaume | Siyana Pavlova | Guy Perrier

This paper presents how the online tool Grew-match can be used to make queries and visualise data from existing semantically annotated corpora. A dedicated syntax is available to construct simple to complex queries and execute them against a corpus. Such queries give transverse views of the annotated data, this views can help for checking the consistency of annotations in one corpus or across several corpora. Grew-match can then be seen as an error mining tool: when inconsistencies are detected, it helps finding the sentences which should be fixed. Finally, Grew-match can also be used as a side tool to assist annotation task helping to find annotations examples in existing corpora to be compare to the data to be annotated.

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Intuitive and Formal Transparency in Annotation Schemes
Harry Bunt

This paper explores the application of the notion of ‘transparency’ to annotation schemes, understood as the properties that make it easy for potential users to see the scope of the scheme, the main concepts used in annotations, and the ways these concepts are interrelated. Based on an analysis of annotation schemes in the ISO Semantic Annotation Framework, it is argued that the way these schemes make use of ‘metamodels’ is not optimal, since these models are often not entirely clear and not directly related to the formal specification of the scheme. It is shown that by formalizing the relation between metamodels and annotations, by formalizing the relation between metamodels and annotations, both can benefit and can be made simpler, and the annotation scheme becomes intuitively more transparent.

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How much of UCCA can be predicted from AMR?
Siyana Pavlova | Maxime Amblard | Bruno Guillaume

In this paper, we consider two of the currently popular semantic frameworks: Abstract Meaning Representation (AMR) - a more abstract framework, and Universal Conceptual Cognitive Annotation (UCCA) - an anchored framework. We use a corpus-based approach to build two graph rewriting systems, a deterministic and a non-deterministic one, from the former to the latter framework. We present their evaluation and a number of ambiguities that we discovered while building our rules. Finally, we provide a discussion and some future work directions in relation to comparing semantic frameworks of different flavors.

Interoperability is a necessity for the resolution of complex tasks that require the interconnection of several NLP services. This article presents the approaches that were adopted in three scenarios to address the respective interoperability issues. The first scenario describes the creation of a common REST API for a specific platform, the second scenario presents the interconnection of several platforms via mapping of different representation formats and the third scenario shows the complexities of interoperability through semantic schema mapping or automatic translation.

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Annotating Japanese Numeral Expressions for a Logical and Pragmatic Inference Dataset
Kana Koyano | Hitomi Yanaka | Koji Mineshima | Daisuke Bekki

Numeral expressions in Japanese are characterized by the flexibility of quantifier positions and the variety of numeral suffixes. However, little work has been done to build annotated corpora focusing on these features and datasets for testing the understanding of Japanese numeral expressions. In this study, we build a corpus that annotates each numeral expression in an existing phrase structure-based Japanese treebank with its usage and numeral suffix types. We also construct an inference test set for numerical expressions based on this annotated corpus. In this test set, we particularly pay attention to inferences where the correct label differs between logical entailment and implicature and those contexts such as negations and conditionals where the entailment labels can be reversed. The baseline experiment with Japanese BERT models shows that our inference test set poses challenges for inference involving various types of numeral expressions.

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Annotating complex words to investigate the semantics of derivational processes
Rossella Varvara | Justine Salvadori | Richard Huyghe

In this paper, we present and test an annotation scheme designed to analyse the semantic properties of derived nouns in context. Aiming at a general semantic comparison of morphological processes, we use a descriptive model that seeks to capture semantic regularities among lexemes and affixes, rather than match occurrences to word sense inventories. We annotate two distinct features of target words: the ontological type of the entity they denote and their semantic relationship with the word they derive from. As illustrated through an annotation experiment on French corpus data, this procedure allows us to highlight semantic differences and similarities between affixes by investigating the number and frequency of their semantic functions, as well as the relation between affix polyfunctionality and lexical ambiguity.

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Annotating Propositional Attitude Verbs and their Arguments
Marta Ricchiardi | Elisabetta Jezek

This paper describes the results of an empirical study on attitude verbs and propositional attitude reports in Italian. Within the framework of a project aiming at acquiring argument structures for Italian verbs from corpora, we carried out a systematic annotation that aims at individuating which verbs are actually attitude verbs in Italian. The result is a list of 179 argument structures based on corpus-derived pattern of use for 126 verbs that behave as attitude verbs. The distribution of these verbs in the corpus suggests that not only the canonical that-clauses, i.e. subordinates introduced by the complementizerte che, but also direct speech, infinitives introduced by the complementizer di, and some nominals are good candidates to express propositional contents in propositional attitude reports. The annotation also enlightens some issues between semantics and ontology, concerning the relation between events and propositions.

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bib (full) Proceedings of the First Workshop on Language Technology and Resources for a Fair, Inclusive, and Safe Society within the 13th Language Resources and Evaluation Conference

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Proceedings of the First Workshop on Language Technology and Resources for a Fair, Inclusive, and Safe Society within the 13th Language Resources and Evaluation Conference
Kolawole Adebayo | Rohan Nanda | Kanishk Verma | Brian Davis

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Casteism in India, but Not Racism - a Study of Bias in Word Embeddings of Indian Languages
Senthil Kumar B | Pranav Tiwari | Aman Chandra Kumar | Aravindan Chandrabose

In this paper, we studied the gender bias in monolingual word embeddings of two Indian languages Hindi and Tamil. Tamil is one of the classical languages of India from the Dravidian language family. In Indian society and culture, instead of racism, a similar type of discrimination called casteism is against the subgroup of peoples representing lower class or Dalits. The word embeddings measurement to evaluate bias using the WEAT score reveals that the embeddings are biased with gender and casteism which is in line with the common stereotypical human biases.

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Objectifying Women? A Syntactic Bias in French and English Corpora.
Yanis da Cunha | Anne Abeillé

Gender biases in syntax have been documented for languages with grammatical gender for cases where mixed-gender coordination structures take masculine agreement, or with male-first preference in the ordering of pairs (Adam and Eve). On the basis of various annotated corpora spanning different genres (fiction, newspapers, speech and web), we show another syntactic gender bias: masculine pronouns are more often subjects than feminine pronouns, in both English and French. We find the same bias towards masculine subjects for French human nouns, which then refer to males and females. Comparing the subject of passive verbs and the object of active verbs, we show that this syntactic function bias is not reducible to a bias in semantic role assignment since it is also found with non-agentive subjects. For French fiction, we also found that the masculine syntactic function bias is larger in text written by male authors – female authors seem to be unbiased. We finally discuss two principles as possible explanations, ‘Like Me’ and ‘Easy first’, and examine the effect of the discourse tendency for men being agents and topics. We conclude by addressing the impact of such biases in language technologies.

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A Cancel Culture Corpus through the Lens of Natural Language Processing
Justus-Jonas Erker | Catalina Goanta | Gerasimos Spanakis

Cancel Culture as an Internet phenomenon has been previously explored from a social and legal science perspective. This paper demonstrates how Natural Language Processing tasks can be derived from this previous work, underlying techniques on how cancel culture can be measured, identified and evaluated. As part of this paper, we introduce a first cancel culture data set with of over 2.3 million tweets and a framework to enlarge it further. We provide a detailed analysis of this data set and propose a set of features, based on various models including sentiment analysis and emotion detection that can help characterizing cancel culture.

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Benchmarking Language Models for Cyberbullying Identification and Classification from Social-media Texts
Kanishk Verma | Tijana Milosevic | Keith Cortis | Brian Davis

Cyberbullying is bullying perpetrated via the medium of modern communication technologies like social media networks and gaming platforms. Unfortunately, most existing datasets focusing on cyberbullying detection or classification are i) limited in number ii) usually targeted to one specific online social networking (OSN) platform, or iii) often contain low-quality annotations. In this study, we fine-tune and benchmark state of the art neural transformers for the binary classification of cyberbullying in social media texts, which is of high value to Natural Language Processing (NLP) researchers and computational social scientists. Furthermore, this work represents the first step toward building neural language models for cross OSN platform cyberbullying classification to make them as OSN platform agnostic as possible.

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Identifying Hate Speech Using Neural Networks and Discourse Analysis Techniques
Zehra Melce Hüsünbeyi | Didar Akar | Arzucan Özgür

Discriminatory language, in particular hate speech, is a global problem posing a grave threat to democracy and human rights. Yet, it is not always easy to identify, as it is rarely explicit. In order to detect hate speech, we developed Hierarchical Attention Network (HAN) based and Bidirectional Encoder Representations from Transformer (BERT) based deep learning models to capture the changing discursive cues and understand the context around the discourse. In addition, we designed linguistic features using critical discourse analysis techniques and integrated them into these neural network models. We studied the compatibility of our model with the hate speech detection problem by comparing it with traditional machine learning models, as well as a Convolution Neural Network (CNN) based model, a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) based model which reached significant performance results for hate speech detection. Our results on a manually annotated corpus of print media in Turkish show that the proposed approach is effective for hate speech detection. We believe that the feature sets created for the Turkish language will encourage new studies in the quantitative analysis of hate speech.

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An Open Source Contractual Language Understanding Application Using Machine Learning
Afra Nawar | Mohammed Rakib | Salma Abdul Hai | Sanaulla Haq

Legal field is characterized by its exclusivity and non-transparency. Despite the frequency and relevance of legal dealings, legal documents like contracts remains elusive to non-legal professionals for the copious usage of legal jargon. There has been little advancement in making legal contracts more comprehensible. This paper presents how Machine Learning and NLP can be applied to solve this problem, further considering the challenges of applying ML to the high length of contract documents and training in a low resource environment. The largest open-source contract dataset so far, the Contract Understanding Atticus Dataset (CUAD) is utilized. Various pre-processing experiments and hyperparameter tuning have been carried out and we successfully managed to eclipse SOTA results presented for models in the CUAD dataset trained on RoBERTa-base. Our model, A-type-RoBERTa-base achieved an AUPR score of 46.6% compared to 42.6% on the original RoBERT-base. This model is utilized in our end to end contract understanding application which is able to take a contract and highlight the clauses a user is looking to find along with it’s descriptions to aid due diligence before signing. Alongside digital, i.e. searchable, contracts the system is capable of processing scanned, i.e. non-searchable, contracts using tesseract OCR. This application is aimed to not only make contract review a comprehensible process to non-legal professionals, but also to help lawyers and attorneys more efficiently review contracts.

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bib (full) Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022

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Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022
Sameer Pradhan | Sandra Kuebler

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Automatic Approach for Building Dataset of Citation Functions for COVID-19 Academic Papers
Setio Basuki | Masatoshi Tsuchiya

This paper develops a new dataset of citation functions of COVID-19-related academic papers. Because the preparation of new labels of citation functions and building a new dataset requires much human effort and is time-consuming, this paper uses our previous citation functions that were built for the Computer Science (CS) domain, which consists of five coarse-grained labels and 21 fine-grained labels. This paper uses the COVID-19 Open Research Dataset (CORD-19) and extracts 99.6k random citing sentences from 10.1k papers. These citing sentences are categorized using the classification models built from the CS domain. The manually check on 475 random samples resulted accuracies of 76.6% and 70.2% on coarse-grained labels and fine-grained labels, respectively. The evaluation reveals three findings. First, two fine-grained labels experienced meaning shift while retaining the same idea. Second, the COVID-19 domain is dominated by statements highlighting the importance, cruciality, usefulness, benefit, consideration, etc. of certain topics for making sensible argumentation. Third, discussing State of The Arts (SOTA) in terms of their outperforming previous works in the COVID-19 domain is less popular compared to the CS domain. Our results will be used for further dataset development by classifying citing sentences in all papers from CORD-19.

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The Development of a Comprehensive Spanish Dictionary for Phonetic and Lexical Tagging in Socio-phonetic Research (ESPADA)
Simon Gonzalez

Pronunciation dictionaries are an important component in the process of speech forced alignment. The accuracy of these dictionaries has a strong effect on the aligned speech data since they help the mapping between orthographic transcriptions and acoustic signals. In this paper, I present the creation of a comprehensive pronunciation dictionary in Spanish (ESPADA) that can be used in most of the dialect variants of Spanish data. Current dictionaries focus on specific regional variants, but with the flexible nature of our tool, it can be readily applied to capture the most common phonetic differences across major dialectal variants. We propose improvements to current pronunciation dictionaries as well as mapping other relevant annotations such as morphological and lexical information. In terms of size, it is currently the most complete dictionary with more than 628,000 entries, representing words from 16 countries. All entries come with their corresponding pronunciations, morphological and lexical tagging, and other relevant information for phonetic analysis: stress patterns, phonotactics, IPA transcriptions, and more. This aims to equip socio-phonetic researchers with a complete open-source tool that enhances dialectal research within socio-phonetic frameworks in the Spanish language.

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Extending the SSJ Universal Dependencies Treebank for Slovenian: Was It Worth It?
Kaja Dobrovoljc | Nikola Ljubešić

This paper presents the creation and evaluation of a new version of the reference SSJ Universal Dependencies Treebank for Slovenian, which has been substantially improved and extended to almost double the original size. The process was based on the initial revision and documentation of the language-specific UD annotation guidelines for Slovenian and the corresponding modification of the original SSJ annotations, followed by a two-stage annotation campaign, in which two new subsets have been added, the previously unreleased sentences from the ssj500k corpus and the Slovenian subset of the ELEXIS parallel corpus. The annotation campaign resulted in an extended version of the SSJ UD treebank with 5,435 newly added sentences comprising of 126,427 tokens. To evaluate the potential benefits of this data increase for Slovenian dependency parsing, we compared the performance of the classla-stanza dependency parser trained on the old and the new SSJ data when evaluated on the new SSJ test set and its subsets. Our results show an increase of LAS performance in general, especially for previously under-represented syntactic phenomena, such as lists, elliptical constructions and appositions, but also confirm the distinct nature of the two newly added subsets and the diversification of the SSJ treebank as a whole.

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Converting the Sinica Treebank of Mandarin Chinese to Universal Dependencies
Yu-Ming Hsieh | Yueh-Yin Shih | Wei-Yun Ma

This paper describes the conversion of the Sinica Treebank, one of the major Mandarin Chinese treebanks, to Universal Dependencies. The conversion is rule-based and the process involves POS tag mapping, head adjusting in line with the UD scheme and the dependency conversion. Linguistic insights into Mandarin Chinese alongwith the conversion are also discussed. The resulting corpus is the UD Chinese Sinica Treebank which contains more than fifty thousand tree structures according to the UD scheme. The dataset can be downloaded at https://github.com/ckiplab/ud.

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Desiderata for the Annotation of Information Structure in Complex Sentences
Hannah Booth

Many annotation schemes for information structure have been developed in recent years (Calhoun et al., 2005; Paggio, 2006; Goetze et al., 2007; Bohnet et al., 2013; Riester et al., 2018), in line with increased attention on the interaction between discourse and other linguistic dimensions (e.g. syntax, semantics, prosody). However, a crucial issue which existing schemes either gloss over, or propose only crude guidelines for, is how to annotate information structure in complex sentences. This unsatisfactory treatment is unsurprising given that theoretical work on information structure has traditionally neglected its status in dependent clauses. In this paper, I evaluate the status of pre-existing annotation schemes in relation to this vexed issue, and outline certain desiderata as a foundation for novel, more nuanced approaches, informed by state-of-the art theoretical insights (Erteschik-Shir, 2007; Bianchi and Frascarelli, 2010; Lahousse, 2010; Ebert et al., 2014; Matic et al., 2014; Lahousse, 2022). These desiderata relate both to annotation formats and the annotation process. The practical implications of these desiderata are illustrated via a test case using the Corpus of Historical Low German (Booth et al., 2020). The paper overall showcases the benefits which result from a free exchange between linguistic annotation models and theoretical research.

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The Sensitivity of Annotator Bias to Task Definitions in Argument Mining
Terne Sasha Thorn Jakobsen | Maria Barrett | Anders Søgaard | David Lassen

NLP models are dependent on the data they are trained on, including how this data is annotated. NLP research increasingly examines the social biases of models, but often in the light of their training data and specific social biases that can be identified in the text itself. In this paper, we present an annotation experiment that is the first to examine the extent to which social bias is sensitive to how data is annotated. We do so by collecting annotations of arguments in the same documents following four different guidelines and from four different demographic annotator backgrounds. We show that annotations exhibit widely different levels of group disparity depending on which guidelines annotators follow. The differences are not explained by task complexity, but rather by characteristics of these demographic groups, as previously identified by sociological studies. We release a dataset that is small in the number of instances but large in the number of annotations with demographic information, and our results encourage an increased awareness of annotator bias.

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NLP in Human Rights Research: Extracting Knowledge Graphs about Police and Army Units and Their Commanders
Daniel Bauer | Tom Longley | Yueen Ma | Tony Wilson

In this paper we explore the use of an NLP system to assist the work of Security Force Monitor (SFM). SFM creates data about the organizational structure, command personnel and operations of police, army and other security forces, which assists human rights researchers, journalists and litigators in their work to help identify and bring to account specific units and personnel alleged to have committed abuses of human rights and international criminal law. This paper presents an NLP system that extracts from English language news reports the names of security force units and the biographical details of their personnel, and infers the formal relationship between them. Published alongside this paper are the system’s code and training dataset. We find that the experimental NLP system performs the task at a fair to good level. Its performance is sufficient to justify further development into a live workflow that will give insight into whether its performance translates into savings in time and resource that would make it an effective technical intervention.

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Advantages of a Complex Multilayer Annotation Scheme: The Case of the Prague Dependency Treebank
Eva Hajicova | Marie Mikulová | Barbora Štěpánková | Jiří Mírovský

Recently, many corpora have been developed that contain multiple annotations of various linguistic phenomena, from morphological categories of words through the syntactic structure of sentences to discourse and coreference relations in texts. Discussions are ongoing on an appropriate annotation scheme for a large amount of diverse information. In our contribution we express our conviction that a multilayer annotation scheme offers to view the language system in its complexity and in the interaction of individual phenomena and that there are at least two aspects that support such a scheme: (i) A multilayer annotation scheme makes it possible to use the annotation of one layer to design the annotation of another layer(s) both conceptually and in a form of a pre-annotation procedure or annotation checking rules. (ii) A multilayer annotation scheme presents a reliable ground for corpus studies based on features across the layers. These aspects are demonstrated on the case of the Prague Dependency Treebank. Its multilayer annotation scheme withstood the test of time and serves well also for complex textual annotations, in which earlier morpho-syntactic annotations are advantageously used. In addition to a reference to the previous projects that utilise its annotation scheme, we present several current investigations.

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Introducing StarDust: A UD-based Dependency Annotation Tool
Arife B. Yenice | Neslihan Cesur | Aslı Kuzgun | Olcay Taner Yıldız

This paper aims to introduce StarDust, a new, open-source annotation tool designed for NLP studies. StarDust is designed specifically to be intuitive and simple for the annotators while also supporting the annotation of multiple languages with different morphological typologies, e.g. Turkish and English. This demonstration will mainly focus on our UD-based annotation tool for dependency syntax. Linked to a morphological analyzer, the tool can detect certain annotator mistakes and limit undesired dependency relations as well as offering annotators a quick and effective annotation process thanks to its new simple interface. Our tool can be downloaded from the Github.

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Annotation of Messages from Social Media for Influencer Detection
Kevin Deturck | Damien Nouvel | Namrata Patel | Frédérique Segond

To develop an influencer detection system, we designed an influence model based on the analysis of conversations in the “Change My View” debate forum. This led us to identify enunciative features (argumentation, emotion expression, view change, ...) related to influence between participants. In this paper, we present the annotation campaign we conducted to build up a reference corpus on these enunciative features. The annotation task was to identify in social media posts the text segments that corresponded to each enunciative feature. The posts to be annotated were extracted from two social media: the “Change My View” debate forum, with discussions on various topics, and Twitter, with posts from users identified as supporters of ISIS (Islamic State of Iraq and Syria). Over a thousand posts have been double or triple annotated throughout five annotation sessions gathering a total of 27 annotators. Some of the sessions involved the same annotators, which allowed us to analyse the evolution of their annotation work. Most of the sessions resulted in a reconciliation phase between the annotators, allowing for discussion and iterative improvement of the guidelines. We measured and analysed inter-annotator agreements over the course of the sessions, which allowed us to validate our iterative approach.

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Charon: A FrameNet Annotation Tool for Multimodal Corpora
Frederico Belcavello | Marcelo Viridiano | Ely Matos | Tiago Timponi Torrent

This paper presents Charon, a web tool for annotating multimodal corpora with FrameNet categories. Annotation can be made for corpora containing both static images and video sequences paired – or not – with text sequences. The pipeline features, besides the annotation interface, corpus import and pre-processing tools.

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Effect of Source Language on AMR Structure
Shira Wein | Wai Ching Leung | Yifu Mu | Nathan Schneider

The Abstract Meaning Representation (AMR) annotation schema was originally designed for English. But the formalism has since been adapted for annotation in a variety of languages. Meanwhile, cross-lingual parsers have been developed to derive English AMR representations for sentences from other languages—implicitly assuming that English AMR can approximate an interlingua. In this work, we investigate the similarity of AMR annotations in parallel data and how much the language matters in terms of the graph structure. We set out to quantify the effect of sentence language on the structure of the parsed AMR. As a case study, we take parallel AMR annotations from Mandarin Chinese and English AMRs, and replace all Chinese concepts with equivalent English tokens. We then compare the two graphs via the Smatch metric as a measure of structural similarity. We find that source language has a dramatic impact on AMR structure, with Smatch scores below 50% between English and Chinese graphs in our sample—an important reference point for interpreting Smatch scores in cross-lingual AMR parsing.

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Midas Loop: A Prioritized Human-in-the-Loop Annotation for Large Scale Multilayer Data
Luke Gessler | Lauren Levine | Amir Zeldes

Large scale annotation of rich multilayer corpus data is expensive and time consuming, motivating approaches that integrate high quality automatic tools with active learning in order to prioritize human labeling of hard cases. A related challenge in such scenarios is the concurrent management of automatically annotated data and human annotated data, particularly where different subsets of the data have been corrected for different types of annotation and with different levels of confidence. In this paper we present [REDACTED], a collaborative, version-controlled online annotation environment for multilayer corpus data which includes integrated provenance and confidence metadata for each piece of information at the document, sentence, token and annotation level. We present a case study on improving annotation quality in an existing multilayer parse bank of English called AMALGUM, focusing on active learning in corpus preprocessing, at the surprisingly challenging level of sentence segmentation. Our results show improvements to state-of-the-art sentence segmentation and a promising workflow for getting “silver” data to approach gold standard quality.

Conspiracy theories have found a new channel on the internet and spread by bringing together like-minded people, thus functioning as an echo chamber. The new 88-million word corpus Language of Conspiracy (LOCO) was created with the intention to provide a text collection to study how the language of conspiracy differs from mainstream language. We use this corpus to develop a robust annotation scheme that will allow us to distinguish between documents containing conspiracy language and documents that do not contain any conspiracy content or that propagate conspiracy theories via misinformation (which we explicitly disregard in our work). We find that focusing on indicators of a belief in a conspiracy combined with textual cues of conspiracy language allows us to reach a substantial agreement (based on Fleiss’ kappa and Krippendorff’s alpha). We also find that the automatic retrieval methods used to collect the corpus work well in finding mainstream documents, but include some documents in the conspiracy category that would not belong there based on our definition.

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Putting Context in SNACS: A 5-Way Classification of Adpositional Pragmatic Markers
Yang Janet Liu | Jena D. Hwang | Nathan Schneider | Vivek Srikumar

The SNACS framework provides a network of semantic labels called supersenses for annotating adpositional semantics in corpora. In this work, we consider English prepositions (and prepositional phrases) that are chiefly pragmatic, contributing extra-propositional contextual information such as speaker attitudes and discourse structure. We introduce a preliminary taxonomy of pragmatic meanings to supplement the semantic SNACS supersenses, with guidelines for the annotation of coherence connectives, commentary markers, and topic and focus markers. We also examine annotation disagreements, delve into the trickiest boundary cases, and offer a discussion of future improvements.

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Building a Biomedical Full-Text Part-of-Speech Corpus Semi-Automatically
Nicholas Elder | Robert E. Mercer | Sudipta Singha Roy

This paper presents a method for semi-automatically building a corpus of full-text English-language biomedical articles annotated with part-of-speech tags. The outcomes are a semi-automatic procedure to create a large silver standard corpus of 5 million sentences drawn from a large corpus of full-text biomedical articles annotated for part-of-speech, and a robust, easy-to-use software tool that assists the investigation of differences in two tagged datasets. The method to build the corpus uses two part-of-speech taggers designed to tag biomedical abstracts followed by a human dispute settlement when the two taggers differ on the tagging of a token. The dispute resolution aspect is facilitated by the software tool which organizes and presents the disputed tags. The corpus and all of the software that has been implemented for this study are made publicly available.

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Human Schema Curation via Causal Association Rule Mining
Noah Weber | Anton Belyy | Nils Holzenberger | Rachel Rudinger | Benjamin Van Durme

Event schemas are structured knowledge sources defining typical real-world scenarios (e.g., going to an airport). We present a framework for efficient human-in-the-loop construction of a schema library, based on a novel script induction system and a well-crafted interface that allows non-experts to “program” complex event structures. Associated with this work we release a schema library: a machine readable resource of 232 detailed event schemas, each of which describe a distinct typical scenario in terms of its relevant sub-event structure (what happens in the scenario), participants (who plays a role in the scenario), fine-grained typing of each participant, and the implied relational constraints between them. We make our schema library and the SchemaBlocks interface available online.

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A Cognitive Approach to Annotating Causal Constructions in a Cross-Genre Corpus
Angela Cao | Gregor Williamson | Jinho D. Choi

We present a scheme for annotating causal language in various genres of text. Our annotation scheme is built on the popular categories of cause, enable, and prevent. These vague categories have many edge cases in natural language, and as such can prove difficult for annotators to consistently identify in practice. We introduce a decision based annotation method for handling these edge cases. We demonstrate that, by utilizing this method, annotators are able to achieve inter-annotator agreement which is comparable to that of previous studies. Furthermore, our method performs equally well across genres, highlighting the robustness of our annotation scheme. Finally, we observe notable variation in usage and frequency of causal language across different genres.

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Automatic Enrichment of Abstract Meaning Representations
Yuxin Ji | Gregor Williamson | Jinho D. Choi

Abstract Meaning Representation (AMR) is a semantic graph framework which inadequately represent a number of important semantic features including number, (in)definiteness, quantifiers, and intensional contexts. Several proposals have been made to improve the representational adequacy of AMR by enriching its graph structure. However, these modifications are rarely added to existing AMR corpora due to the labor costs associated with manual annotation. In this paper, we develop an automated annotation tool which algorithmically enriches AMR graphs to better represent number, (in)definite articles, quantificational determiners, and intensional arguments. We compare our automatically produced annotations to gold-standard manual annotations and show that our automatic annotator achieves impressive results. All code for this paper, including our automatic annotation tool, is made publicly available.

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GRAIL—Generalized Representation and Aggregation of Information Layers
Sameer Pradhan | Mark Liberman

This paper identifies novel characteristics necessary to successfully represent multiple streams of natural language information from speech and text simultaneously, and proposes a multi-tiered system that implements these characteristics centered around a declarative configuration. The system facilitates easy incremental extension by allowing the creation of composable workflows of loosely coupled extensions, or plugins, allowing simple intial systems to be extended to accomodate rich representations while maintaining high data integrity. Key to this is leveraging established tools and technologies. We demonstrate using a small example.

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bib (full) Proceedings of the Workshop on Ethical and Legal Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Data In Language Resources within the 13th Language Resources and Evaluation Conference

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Proceedings of the Workshop on Ethical and Legal Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Data In Language Resources within the 13th Language Resources and Evaluation Conference
Ingo Siegert | Mickael Rigault | Victoria Arranz

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Keynote Speech - Major developments in the legal framework concerning language resources
Pawel Kamocki

Introductory Talk for the Workshop on Legal and Ethical Issues in Human Language Technologies, LREC 2022, Marseille, 24 June 2022

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Sentiment Analysis and Topic Modeling for Public Perceptions of Air Travel: COVID Issues and Policy Amendments
Avery Field | Aparna Varde | Pankaj Lal

Among many industries, air travel is impacted by the COVID pandemic. Airlines and airports rely on public sector information to enforce guidelines for ensuring health and safety of travelers. Such guidelines can be policy amendments or laws during the pandemic. In response to the inception of COVID preventive policies, travelers have exercised freedom of expression via the avenue of online reviews. This avenue facilitates voicing public concern while anonymizing / concealing user identity as needed. It is important to assess opinions on policy amendments to ensure transparency and openness, while also preserving confidentiality and ethics. Hence, this study leverages data science to analyze, with identity protection, the online reviews of airlines and airports since 2017, considering impacts of COVID issues and relevant policy amendments since 2020. Supervised learning with VADER sentiment analysis is deployed to predict changes in opinion from 2017 to date. Unsupervised learning with LDA topic modeling is employed to discover air travelers’ major areas of concern before and after the pandemic. This study reveals that COVID policies have worsened public perceptions of air travel and aroused notable new concerns, affecting economics, environment and health.

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Data Protection, Privacy and US Regulation
Denise DiPersio

This paper examines the state of data protection and privacy in the United States. There is no comprehensive federal data protection or data privacy law despite bipartisan and popular support. There are several data protection bills pending in the 2022 session of the US Congress, five of which are examined in Section 2 below. Although it is not likely that any will be enacted, the growing number reflects the concerns of citizens and lawmakers about the power of big data. Recent actions against data abuses, including data breaches, litigation and settlements, are reviewed in Section 3 of this paper. These reflect the real harm caused when personal data is misused. Section 4 contains a brief US copyright law update on the fair use exemption, highlighting a recent court decision and indications of a re-thinking of the fair use analysis. In Section 5, some observations are made on the role of privacy in data protection regulation. It is argued that privacy should be considered from the start of the data collection and technology development process. Enhanced awareness of ethical issues, including privacy, through university-level data science programs will also lay the groundwork for best practices throughout the data and development cycles.

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Pseudonymisation of Speech Data as an Alternative Approach to GDPR Compliance
Pawel Kamocki | Ingo Siegert

The debate on the use of personal data in language resources usually focuses — and rightfully so — on anonymisation. However, this very same debate usually ends quickly with the conclusion that proper anonymisation would necessarily cause loss of linguistically valuable information. This paper discusses an alternative approach — pseudonymisation. While pseudonymisation does not solve all the problems (inasmuch as pseudonymised data are still to be regarded as personal data and therefore their processing should still comply with the GDPR principles), it does provide a significant relief, especially — but not only — for those who process personal data for research purposes. This paper describes pseudonymisation as a measure to safeguard rights and interests of data subjects under the GDPR (with a special focus on the right to be informed). It also provides a concrete example of pseudonymisation carried out within a research project at the Institute of Information Technology and Communications of the Otto von Guericke University Magdeburg.

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Categorizing legal features in a metadata-oriented task: defining the conditions of use
Mickaël Rigault | Victoria Arranz | Valérie Mapelli | Penny Labropoulou | Stelios Piperidis

In recent times, more attention has been brought by the Human Language Technology (HLT) community to the legal framework for making available and reusing Language Resources (LR) and tools. Licensing is now an issue that is foreseen in most research projects and that is essential to provide legal certainty for repositories when distributing resources. Some repositories such as Zenodo or Quantum Stat do not offer the possibility to search for resources by licenses which can turn the searching for relevant resources a very complex task. Other repositories such as Hugging Face propose a search feature by license which may make it difficult to figure out what use can be made of such resources. During the European Language Grid (ELG) project, we moved a step forward to link metadata with the terms and conditions of use. In this paper, we document the process we undertook to categorize legal features of licenses listed in the SPDX license list and widely used in the HLT community as well as those licenses used within the ELG platform

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About Migration Flows and Sentiment Analysis on Twitter data: Building the Bridge between Technical and Legal Approaches to Data Protection
Thilo Gottschalk | Francesca Pichierri

Sentiment analysis has always been an important driver of political decisions and campaigns across all fields. Novel technologies allow automatizing analysis of sentiments on a big scale and hence provide allegedly more accurate outcomes. With user numbers in the billions and their increasingly important role in societal discussions, social media platforms become a glaring data source for these types of analysis. Due to its public availability, the relative ease of access and the sheer amount of available data, the Twitter API has become a particularly important source to researchers and data analysts alike. Despite the evident value of these data sources, the analysis of such data comes with legal, ethical and societal risks that should be taken into consideration when analysing data from Twitter. This paper describes these risks along the technical processing pipeline and proposes related mitigation measures.

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Transparency and Explainability of a Machine Learning Model in the Context of Human Resource Management
Sebastien Delecraz | Loukman Eltarr | Olivier Oullier

We introduce how the proprietary machine learning algorithms developed by Gojob, an HR Tech company, to match candidates to a job offer are as transparent and explainable as possible to users (i.e., our recruiters) and our clients (e.g. companies looking to fill jobs). We detail how our matching algorithm (which identifies the best candidates for a job offer) controls the fairness of its outcome. We have described the steps we have taken to ensure that the decisions made by our mathematical models not only inform but improve the performance of our recruiters.

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Public Interactions with Voice Assistant – Discussion of Different One-Shot Solutions to Preserve Speaker Privacy
Ingo Siegert | Yamini Sinha | Gino Winkelmann | Oliver Jokisch | Andreas Wendemuth

In recent years, the use of voice assistants has rapidly grown. Hereby, above all, the user’s speech data is stored and processed on a cloud platform, being the decisive factor for a good performance in speech processing and understanding. Although usually, they can be found in private households, a lot of business cases are also employed using voice assistants for public places, be it as an information service, a tour guide, or a booking system. As long as the systems are used in private spaces, it could be argued that the usage is voluntary and that the user itself is responsible for what is processed by the voice assistant system. When leaving the private space, the voluntary use is not the case anymore, as users may be made aware that their voice is processed in the cloud and background voices can be unintendedly recorded and processed as well. Thus, the usage of voice assistants in public environments raises a lot of privacy concerns. In this contribution, we discuss possible anonymization solutions to hide the speakers’ identity, thus allowing a safe cloud processing of speech data. Thereby, we promote the public use of voice assistants.

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Keynote Speech - Voice anonymization and the GDPR
Brij Mohan Lal Srivastava

Talk for the Workshop on Legal and Ethical Issues in Human Language Technologies, LREC 2022, Marseille, 24 June 2022

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Cross-Clinic De-Identification of Swedish Electronic Health Records: Nuances and Caveats
Olle Bridal | Thomas Vakili | Marina Santini

Privacy preservation of sensitive information is one of the main concerns in clinical text mining. Due to the inherent privacy risks of handling clinical data, the clinical corpora used to create the clinical Named Entity Recognition (NER) models underlying clinical de-identification systems cannot be shared. This situation implies that clinical NER models are trained and tested on data originating from the same institution since it is rarely possible to evaluate them on data belonging to a different organization. These restrictions on sharing make it very difficult to assess whether a clinical NER model has overfitted the data or if it has learned any undetected biases. This paper presents the results of the first-ever cross-institution evaluation of a Swedish de-identification system on Swedish clinical data. Alongside the encouraging results, we discuss differences and similarities across EHR naming conventions and NER tagsets.

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Generating Realistic Synthetic Curricula Vitae for Machine Learning Applications under Differential Privacy
Andrea Bruera | Francesco Alda | Francesco Di Cerbo

Applications involving machine learning in Human Resources (HR, the management of human talent in order to accomplish organizational goals) must respect the privacy of the individuals whose data is being used. This is a difficult aim, given the extremely personal nature of text data handled by HR departments, such as Curricula Vitae (CVs).

This paper presents the outcomes of the MAPA project, a set of annotated corpora for 24 languages of the European Union and an open-source customisable toolkit able to detect and substitute sensitive information in text documents from any domain, using state-of-the art, deep learning-based named entity recognition techniques. In the context of the project, the toolkit has been developed and tested on administrative, legal and medical documents, obtaining state-of-the-art results. As a result of the project, 24 dataset packages have been released and the de-identification toolkit is available as open source.

The days of large amorphous corpora collected with armies of Web crawlers and stored indefinitely are, or should be, coming to an end. There is a wealth of hidden linguistic information that is increasingly difficult to access, hidden in personal data that would be unethical and technically challenging to collect using traditional methods such as Web crawling and mass surveillance of online discussion spaces. Advances in privacy regulations such as GDPR and changes in the public perception of privacy bring into question the problematic ethical dimension of extracting information from unaware if not unwilling participants. Modern corpora need to adapt, be focused on testing specific hypotheses, and be respectful of the privacy of the people who generated its data. Our work focuses on using a distributed participatory approach and continuous informed consent to solve these issues, by allowing participants to voluntarily contribute their own censored personal data at a granular level. We evaluate our approach in a three-pronged manner, testing the accuracy of measurement of statistical measures of language with respect to standard corpus linguistics tools, evaluating the usability of our application with a participant involvement panel, and using the tool for a case study on health communication.

In this paper the authors detail the various legal and ethical issues faced during the ATCO2 project. This project is aimed at developing tools to automatically collect and transcribe air traffic conversations, especially conversations between pilots and air controls towers. In this paper the authors will develop issues related to intellectual property, public data, privacy, and general ethics issues related to the collection of air-traffic control speech.

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It is not Dance, is Data: Gearing Ethical Circulation of Intangible Cultural Heritage practices in the Digital Space
Jorge Yánez | Amel Fraisse

The documentation, protection and dissemination of Intangible Cultural Heritage (ICH) in the digital age pose significant theoretical, technological and legal challenges. Through a multidisciplinary lens, this paper presents new approaches for collecting, documenting, encrypting and protecting ICH-related data for more ethical circulation. Human-movement recognition technologies such as motion capture, allows for the recording, extraction and reproduction of human movement with unprecedented precision. The once indistinguishable or hard-to-trace reproduction of dance steps between their creators and unauthorized third parties becomes patent through the transmission of embodied knowledge, but in the form of data. This new battlefield prompted by digital technologies only adds to the disputes within the creative industries, in terms of authorship, ownership and commodification of body language. For the sake of this paper, we are aiming to disentangle the various layers present in the process of digitisation of the dancing body, to identify its by-products as well as the possible arising ownership rights that might entail. ”Who owns what?”, the basic premise of intellectual property law, is transposed, in this case, onto the various types of data generated when intangible cultural heritage, in the form of dance, is digitised through motion capture and encrypted with blockchain technologies.

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bib (full) Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages

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Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages
Rachele Sprugnoli | Marco Passarotti

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Identifying Cleartext in Historical Ciphers
Maria-Elena Gambardella | Beata Megyesi | Eva Pettersson

In historical encrypted sources we can find encrypted text sequences, also called ciphertext, as well as non-encrypted cleartexts written in a known language. While most of the cryptanalysis focuses on the decryption of ciphertext, cleartext is often overlooked although it can give us important clues about the historical interpretation and contextualisation of the manuscript. In this paper, we investigate to what extent we can automatically distinguish cleartext from ciphertext in historical ciphers and to what extent we are able to identify its language. The problem is challenging as cleartext sequences in ciphers are often short, up to a few words, in different languages due to historical code-switching. To identify the sequences and the language(s), we chose a rule-based approach and run 7 different models using historical language models on various ciphertexts.

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Detecting Diachronic Syntactic Developments in Presence of Bias Terms
Oliver Hellwig | Sven Sellmer

Corpus-based studies of diachronic syntactic changes are typically guided by the results of previous qualitative research. When such results are missing or, as is the case for Vedic Sanskrit, are restricted to small parts of a transmitted corpus, an exploratory framework that detects such changes in a data-driven fashion can substantially support the research process. In this paper, we introduce a customized version of the infinite relational model that groups syntactic constituents based on their structural similarities and their diachronic distributions. We propose a simple way to control for register and intellectual affiliation, and discuss our findings for four syntactic structures in Vedic texts.

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Accurate Dependency Parsing and Tagging of Latin
Sebastian Nehrdich | Oliver Hellwig

Having access to high-quality grammatical annotations is important for downstream tasks in NLP as well as for corpus-based research. In this paper, we describe experiments with the Latin BERT word embeddings that were recently be made available by Bamman and Burns (2020). We show that these embeddings produce competitive results in the low-level task morpho-syntactic tagging. In addition, we describe a graph-based dependency parser that is trained with these embeddings and that clearly outperforms various baselines.

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Annotating “Absolute” Preverbs in the Homeric and Vedic Treebanks
Luca Brigada Villa | Erica Biagetti | Chiara Zanchi

Indo-European preverbs are uninflected morphemes attaching to verbs and modifying their meaning. In Early Vedic and Homeric Greek, these morphemes held ambiguous morphosyntactic status raising issues for syntactic annotation. This paper focuses on the annotation of preverbs in so-called “absolute” position in two Universal Dependencies treebanks. This issue is related to the broader topic of how to annotate ellipsis in Universal Dependencies. After discussing some of the current annotations, we propose a new scheme that better accounts for the variety of absolute constructions.

This article presents a word-sense annotation for the Corpus of Historical Japanese: a mashed-up Japanese lexicon based on the ‘Word List by Semantic Principles’ (WLSP). The WLSP is a large-scale Japanese thesaurus that includes 98,241 entries with syntactic and hierarchical semantic categories. The historical WLSP is also compiled for the words in ancient Japanese. We utilized a morpheme-word sense alignment table to extract all possible word sense candidates for each word appearing in the target corpus. Then, we manually disambiguated the word senses for 647,751 words in the texts from the 10th century to 1910.

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The IKUVINA Treebank
Mathieu Dehouck

In this paper, we introduce the first dependency treebank for the Umbrian language (an extinct Indo-European language from the Italic family, once spoken in modern day Italy). We present the source of the corpus : a set of seven bronze tablets describing religious ceremonies, written using two different scripts, unearthed in Umbria in the XVth century. The corpus itself has already been studied extensively by specialists of old Italic and classical Indo-European languages. So we discuss a number of challenges that we encountered as we annotated the corpus following Universal Dependencies’ guidelines from existing textual analyses.

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Machine Translation of 16Th Century Letters from Latin to German
Lukas Fischer | Patricia Scheurer | Raphael Schwitter | Martin Volk

This paper outlines our work in collecting training data for and developing a Latin–German Neural Machine Translation (NMT) system, for translating 16th century letters. While Latin–German is a low-resource language pair in terms of NMT, the domain of 16th century epistolary Latin is even more limited in this regard. Through our efforts in data collection and data generation, we are able to train a NMT model that provides good translations for short to medium sentences, and outperforms GoogleTranslate overall. We focus on the correspondence of the Swiss reformer Heinrich Bullinger, but our parallel corpus and our NMT system will be of use for many other texts of the time.

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A Treebank-based Approach to the Supprema Constructio in Dante’s Latin Works
Flavio Massimiliano Cecchini | Giulia Pedonese

This paper aims to apply a corpus-driven approach to Dante Alighieri’s Latin works using UDante, a treebank based on Dante Search and part of the Universal Dependencies project. We present a method based on the notion of barycentre applied to a dependency tree as a way to calculate the “syntactic balance” of a sentence. Its application to Dante’s Latin works shows its potential in analysing the style of an author, and contributes to the interpretation of the supprema constructio mentioned in DVE II vi 7 as a well balanced syntactic pattern modeled on Latin literary writing.

Available language technology is hardly applicable to scarcely attested ancient languages, yet their digital semantic representation, though challenging, is an asset for the purpose of sharing and preserving existing cultural knowledge. In the context of a project on the languages and cultures of ancient Italy, we took up this challenge. The paper thus describes the development of a user friendly web platform, EpiLexO, for the creation and editing of an integrated system of language resources for ancient fragmentary languages centered on the lexicon, in compliance with current digital humanities and Linked Open Data principles. EpiLexo allows for the editing of lexica with all relevant cross-references: for their linking to their testimonies, as well as to bibliographic information and other (external) resources and common vocabularies. The focus of the current implementation is on the languages of ancient Italy, in particular Oscan, Faliscan, Celtic and Venetic; however, the technological solutions are designed to be general enough to be potentially applicable to different scenarios.

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BERToldo, the Historical BERT for Italian
Alessio Palmero Aprosio | Stefano Menini | Sara Tonelli

Recent works in historical language processing have shown that transformer-based models can be successfully created using historical corpora, and that using them for analysing and classifying data from the past can be beneficial compared to standard transformer models. This has led to the creation of BERT-like models for different languages trained with digital repositories from the past. In this work we introduce the Italian version of historical BERT, which we call BERToldo. We evaluate the model on the task of PoS-tagging Dante Alighieri’s works, considering not only the tagger performance but also the model size and the time needed to train it. We also address the problem of duplicated data, which is rather common for languages with a limited availability of historical corpora. We show that deduplication reduces training time without affecting performance. The model and its smaller versions are all made available to the research community.

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In Search of the Flocks: How to Perform Onomasiological Queries in an Ancient Greek Corpus?
Alek Keersmaekers | Toon Van Hal

This paper explores the possibilities of onomasiologically querying corpus data of Ancient Greek. The significance of the onomasiological approach has been highlighted in recent studies, yet the possibilities of performing ‘word-finding’ investigations into corpus data have not been dealt with in depth. The case study chosen focuses on collective nouns denoting animate groups (such as flocks of people, herds of cattle). By relying on a large automatically annotated corpus of Ancient Greek and on token-based vector information, a longlist of collective nouns was compiled through morpho-syntactic extraction and successive clustering procedures. After reducing this longlist to a shortlist, the results obtained are evaluated. In general, we find that πλῆθος can be considered to be the default collective noun of both humans and animals, becoming especially prominent during the Hellenistic period. In addition, specific tendencies in the use of collective nouns are discerned for specific semantic classes (e.g. gods and insects) and over time. Throughout the paper, special attention is paid to methodological issues related to onomasiologically searching.

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Contextual Unsupervised Clustering of Signs for Ancient Writing Systems
Michele Corazza | Fabio Tamburini | Miguel Valério | Silvia Ferrara

The application of machine learning techniques to ancient writing systems is a relatively new idea, and it poses interesting challenges for researchers. One particularly challenging aspect is the scarcity of data for these scripts, which contrasts with the large amounts of data usually available when applying neural models to computational linguistics and other fields. For this reason, any method that attempts to work on ancient scripts needs to be ad-hoc and consider paleographic aspects, in addition to computational ones. Considering the peculiar characteristics of the script that we used is therefore be a crucial part of our work, as any solution needs to consider the particular nature of the writing system that it is applied to. In this work we propose a preliminary evaluation of a novel unsupervised clustering method on Cypro-Greek syllabary, a writing system from Cyprus. This evaluation shows that our method improves clustering performance using information about the attested sequences of signs in combination with an unsupervised model for images, with the future goal of applying the methodology to undeciphered writing systems from a related and typologically similar script.

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Towards the Creation of a Diachronic Corpus for Italian: A Case Study on the GDLI Quotations
Manuel Favaro | Elisa Guadagnini | Eva Sassolini | Marco Biffi | Simonetta Montemagni

In this paper we describe some experiments related to a corpus derived from an authoritative historical Italian dictionary, namely the Grande dizionario della lingua italiana (‘Great Dictionary of Italian Language’, in short GDLI). Thanks to the digitization and structuring of this dictionary, we have been able to set up the first nucleus of a diachronic annotated corpus that selects—according to specific criteria, and distinguishing between prose and poetry—some of the quotations that within the entries illustrate the different definitions and sub-definitions. In fact, the GDLI presents a huge collection of quotations covering the entire history of the Italian language and thus ranging from the Middle Ages to the present day. The corpus was enriched with linguistic annotation and used to train and evaluate NLP models for POS tagging and lemmatization, with promising results.

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Automatic Translation Alignment for Ancient Greek and Latin
Tariq Yousef | Chiara Palladino | David J. Wright | Monica Berti

This paper presents the results of automatic translation alignment experiments on a corpus of texts in Ancient Greek translated into Latin. We used a state-of-the-art alignment workflow based on a contextualized multilingual language model that is fine-tuned on the alignment task for Ancient Greek and Latin. The performance of the alignment model is evaluated on an alignment gold standard consisting of 100 parallel fragments aligned manually by two domain experts, with a 90.5% Inter-Annotator-Agreement (IAA). An interactive online interface is provided to enable users to explore the aligned fragments collection and examine the alignment model’s output.

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Handling Stress in Finite-State Morphological Analyzers for Ancient Greek and Ancient Hebrew
Daniel Swanson | Francis Tyers

Modeling stress placement has historically been a challenge for computational morphological analysis, especially in finite-state systems because lexically conditioned stress cannot be modeled using only rewrite rules on the phonological form of a word. However, these phenomena can be modeled fairly easily if the lexicon’s internal representation is allowed to contain more information than the pure phonological form. In this paper we describe the stress systems of Ancient Greek and Ancient Hebrew and we present two prototype finite-state morphological analyzers, one for each language, which successfully implement these stress systems by inserting a small number of control characters into the phonological form, thus conclusively refuting the claim that finite-state systems are not powerful enough to model such stress systems and arguing in favor of the continued relevance of finite-state systems as an appropriate tool for modeling the morphology of historical languages.

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From Inscription to Semi-automatic Annotation of Maya Hieroglyphic Texts
Cristina Vertan | Christian Prager

The Maya script is the only readable autochthonous writing system of the Americas and consists of more than 1000 word signs and syllables. It is only partially deciphered and is the subject of the project “Text Database and Dictionary of the Classic Maya” . Texts are recorded in TEI XML and on the basis of a digital sign and graph catalog, which are stored in the TextGrid virtual repository. Due to the state of decipherment, it is not possible to record hieroglyphic texts directly in phonemically transliterated values. The texts are therefore documented numerically using numeric sign codes based on Eric Thompson’s catalog of the Maya script. The workflow for converting numerical transliteration into textual form involves several steps, with variable solutions possible at each step. For this purpose, the authors have developed ALMAH “Annotator for the Linguistic Analysis of Maya Hieroglyphs”. The tool is a client application and allows semi-automatic generation of phonemic transliteration from numerical transliteration and enables multi-step linguistic annotation. Alternative readings can be entered, and two or more decipherment proposals can be processed in parallel. ALMAH is implemented in JAVA, is based on a graph-data model, and has a user-friendly interface.

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Multilingual Named Entity Recognition for Medieval Charters Using Stacked Embeddings and Bert-based Models.
Sergio Torres Aguilar

In recent years the availability of medieval charter texts has increased thanks to advances in OCR and HTR techniques. But the lack of models that automatically structure the textual output continues to hinder the extraction of large-scale lectures from these historical sources that are among the most important for medieval studies. This paper presents the process of annotating and modelling a corpus to automatically detect named entities in medieval charters in Latin, French and Spanish and address the problem of multilingual writing practices in the Late Middle Ages. It introduces a new annotated multilingual corpus and presents a training pipeline using two approaches: (1) a method using contextual and static embeddings coupled to a Bi-LSTM-CRF classifier; (2) a fine-tuning method using the pre-trained multilingual BERT and RoBERTa models. The experiments described here are based on a corpus encompassing about 2.3M words (7576 charters) coming from five charter collections ranging from the 10th to the 15th centuries. The evaluation proves that both multilingual classifiers based on general purpose models and those specifically designed achieve high-performance results and do not show performance drop compared to their monolingual counterparts. This paper describes the corpus and the annotation guideline, and discusses the issues related to the linguistic of the charters, the multilingual writing practices, so as to interpret the results within a larger historical perspective.

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Linguistic Annotation of Neo-Latin Mathematical Texts: A Pilot-Study to Improve the Automatic Parsing of the Archimedes Latinus
Margherita Fantoli | Miryam de Lhoneux

This paper describes the process of syntactically parsing the Latin translation by Jacopo da San Cassiano of the Greek mathematical work The Spirals of Archimedes. The Universal Dependencies formalism is adopted. First, we introduce the historical and linguistic importance of Jacopo da San Cassiano’s translation. Subsequently, we describe the deep Biaffine parser used for this pilot study. In particular, we motivate the choice of using the technique of treebank embeddings in light of the characteristics of mathematical texts. The paper then details the process of creation of training and test data, by highlighting the most compelling linguistic features of the text and the choices implemented in the current version of the treebank. Finally, the results of the parsing are discussed in comparison to a baseline and the most prominent errors are discussed. Overall, the paper shows the added value of creating specific training data, and of using targeted strategies (as treebank embeddings) to exploit existing annotated corpora while preserving the features of one specific text when performing syntactic parsing.

This paper presents the results of the First Ancient Chinese Word Segmentation and POS Tagging Bakeoff (EvaHan), which was held at the Second Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) 2022, in the context of the 13th Edition of the Language Resources and Evaluation Conference (LREC 2022). We give the motivation for having an international shared contest, as well as the data and tracks. The contest is consisted of two modalities, closed and open. In the closed modality, the participants are only allowed to use the training data, obtained the highest F1 score of 96.03% and 92.05% in word segmentation and POS tagging. In the open modality, the participants can use whatever resource they have, with the highest F1 score of 96.34% and 92.56% in word segmentation and POS tagging. The scores on the blind test dataset decrease around 3 points, which shows that the out-of-vocabulary words still are the bottleneck for lexical analyzers.

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Automatic Word Segmentation and Part-of-Speech Tagging of Ancient Chinese Based on BERT Model
Yu Chang | Peng Zhu | Chaoping Wang | Chaofan Wang

In recent years, new deep learning methods and pre-training language models have been emerging in the field of natural language processing (NLP). These methods and models can greatly improve the accuracy of automatic word segmentation and part-of-speech tagging in the field of ancient Chinese research. In these models, the BERT model has made amazing achievements in the top-level test of machine reading comprehension SQuAD-1.1. In addition, it also showed better results than other models in 11 different NLP tests. In this paper, SIKU-RoBERTa pre-training language model based on the high-quality full-text corpus of SiKuQuanShu have been adopted, and part corpus of ZuoZhuan that has been word segmented and part-of-speech tagged is used as training sets to build a deep network model based on BERT for word segmentation and POS tagging experiments. In addition, we also use other classical NLP network models for comparative experiments. The results show that using SIKU-RoBERTa pre-training language model, the overall prediction accuracy of word segmentation and part-of-speech tagging of this model can reach 93.87% and 88.97%, with excellent overall performance.

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Ancient Chinese Word Segmentation and Part-of-Speech Tagging Using Data Augmentation
Yanzhi Tian | Yuhang Guo

We attended the EvaHan2022 ancient Chinese word segmentation and Part-of-Speech (POS) tagging evaluation. We regard the Chinese word segmentation and POS tagging as sequence tagging tasks. Our system is based on a BERT-BiLSTM-CRF model which is trained on the data provided by the EvaHan2022 evaluation. Besides, we also employ data augmentation techniques to enhance the performance of our model. On the Test A and Test B of the evaluation, the F1 scores of our system achieve 94.73% and 90.93% for the word segmentation, 89.19% and 83.48% for the POS tagging.

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BERT 4EVER@EvaHan 2022: Ancient Chinese Word Segmentation and Part-of-Speech Tagging Based on Adversarial Learning and Continual Pre-training
Hailin Zhang | Ziyu Yang | Yingwen Fu | Ruoyao Ding

With the development of artificial intelligence (AI) and digital humanities, ancient Chinese resources and language technology have also developed and grown, which have become an increasingly important part to the study of historiography and traditional Chinese culture. In order to promote the research on automatic analysis technology of ancient Chinese, we conduct various experiments on ancient Chinese word segmentation and part-of-speech (POS) tagging tasks for the EvaHan 2022 shared task. We model the word segmentation and POS tagging tasks jointly as a sequence tagging problem. In addition, we perform a series of training strategies based on the provided ancient Chinese pre-trained model to enhance the model performance. Concretely, we employ several augmentation strategies, including continual pre-training, adversarial training, and ensemble learning to alleviate the limited amount of training data and the imbalance between POS labels. Extensive experiments demonstrate that our proposed models achieve considerable performance on ancient Chinese word segmentation and POS tagging tasks. Keywords: ancient Chinese, word segmentation, part-of-speech tagging, adversarial learning, continuing pre-training

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Construction of Segmentation and Part of Speech Annotation Model in Ancient Chinese
Longjie Jiang | Qinyu C. Chang | Huyin H. Xie | Zhuying Z. Xia

Among the four civilizations in the world with the longest history, only Chinese civilization has been inherited and never interrupted for 5000 years. An important factor is that the Chinese nation has the fine tradition of sorting out classics. Recording history with words, inheriting culture through continuous collation of indigenous accounts, and maintaining the spread of Chinese civilization. In this competition, the siku-roberta model was introduced into the part-of-speech tagging task of ancient Chinese by using the Zuozhuan data set, and good prediction results were obtained.

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Simple Tagging System with RoBERTa for Ancient Chinese
Binghao Tang | Boda Lin | Si Li

This paper describes the system submitted for the EvaHan 2022 Shared Task on word segmentation and part-of-speech tagging for Ancient Chinese. Our system is based on the pre-trained language model SIKU-RoBERTa and the simple tagging layers. Our system significantly outperforms the official baselines in the released test sets and shows the effectiveness.

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The Uncertainty-based Retrieval Framework for Ancient Chinese CWS and POS
Pengyu Wang | Zhichen Ren

Automatic analysis for modern Chinese has greatly improved the accuracy of text mining in related fields, but the study of ancient Chinese is still relatively rare. Ancient text division and lexical annotation are important parts of classical literature comprehension, and previous studies have tried to construct auxiliary dictionary and other fused knowledge to improve the performance. In this paper, we propose a framework for ancient Chinese Word Segmentation and Part-of-Speech Tagging that makes a twofold effort: on the one hand, we try to capture the wordhood semantics; on the other hand, we re-predict the uncertain samples of baseline model by introducing external knowledge. The performance of our architecture outperforms pre-trained BERT with CRF and existing tools such as Jiayan.

Automatic word segmentation and part-of-speech tagging of ancient books can help relevant researchers to study ancient texts. In recent years, pre-trained language models have achieved significant improvements on text processing tasks. SikuRoberta is a pre-trained language model specially designed for automatic analysis of ancient Chinese texts. Although SikuRoberta significantly boosts performance on WSG and POS tasks on ancient Chinese texts, the lack of labeled data still limits the performance of the model. In this paper, to alleviate the problem of insufficient training data, We define hybrid tags to integrate WSG and POS tasks and design Roberta-CRF model to predict tags for each Chinese characters. Moreover, We generate synthetic labeled data based on the LSTM language model. To further mine knowledge in SikuRoberta, we generate the synthetic unlabeled data based on the Masked LM. Experiments show that the performance of the model is improved with the synthetic data, indicating that the effectiveness of the data augmentation methods.

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A Joint Framework for Ancient Chinese WS and POS Tagging Based on Adversarial Ensemble Learning
Shuxun Yang

Ancient Chinese word segmentation and part-of-speech tagging tasks are crucial to facilitate the study of ancient Chinese and the dissemination of traditional Chinese culture. Current methods face problems such as lack of large-scale labeled data, individual task error propagation, and lack of robustness and generalization of models. Therefore, we propose a joint framework for ancient Chinese WS and POS tagging based on adversarial ensemble learning, called AENet. On the basis of pre-training and fine-tuning, AENet uses a joint tagging approach of WS and POS tagging and treats it as a joint sequence tagging task. Meanwhile, AENet incorporates adversarial training and ensemble learning, which effectively improves the model recognition efficiency while enhancing the robustness and generalization of the model. Our experiments demonstrate that AENet improves the F1 score of word segmentation by 4.48% and the score of part-of-speech tagging by 2.29% on test dataset compared with the baseline, which shows high performance and strong generalization.

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Glyph Features Matter: A Multimodal Solution for EvaHan in LT4HALA2022
Wei Xinyuan | Liu Weihao | Qing Zong | Zhang Shaoqing | Baotian Hu

We participate in the LT4HALA2022 shared task EvaHan. This task has two subtasks. Subtask 1 is word segmentation, and subtask 2 is part-of-speech tagging. Each subtask consists of two tracks, a close track that can only use the data and models provided by the organizer, and an open track without restrictions. We employ three pre-trained models, two of which are open-source pre-trained models for ancient Chinese (Siku-Roberta and roberta-classical-chinese), and one is our pre-trained GlyphBERT combined with glyph features. Our methods include data augmentation, data pre-processing, model pretraining, downstream fine-tuning, k-fold cross validation and model ensemble. We achieve competitive P, R, and F1 scores on both our own validation set and the final public test set.

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Overview of the EvaLatin 2022 Evaluation Campaign
Rachele Sprugnoli | Marco Passarotti | Flavio Massimiliano Cecchini | Margherita Fantoli | Giovanni Moretti

This paper describes the organization and the results of the second edition of EvaLatin, the campaign for the evaluation of Natural Language Processing tools for Latin. The three shared tasks proposed in EvaLatin 2022, i.,e.,Lemmatization, Part-of-Speech Tagging and Features Identification, are aimed to foster research in the field of language technologies for Classical languages. The shared dataset consists of texts mainly taken from the LASLA corpus. More specifically, the training set includes only prose texts of the Classical period, whereas the test set is organized in three sub-tasks: a Classical sub-task on a prose text of an author not included in the training data, a Cross-genre sub-task on poetic and scientific texts, and a Cross-time sub-task on a text of the 15th century. The results obtained by the participants for each task and sub-task are presented and discussed.

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An ELECTRA Model for Latin Token Tagging Tasks
Wouter Mercelis | Alek Keersmaekers

This report describes the KU Leuven / Brepols-CTLO submission to EvaLatin 2022. We present the results of our current small Latin ELECTRA model, which will be expanded to a larger model in the future. For the lemmatization task, we combine a neural token-tagging approach with the in-house rule-based lemma lists from Brepols’ ReFlex software. The results are decent, but suffer from inconsistencies between Brepols’ and EvaLatin’s definitions of a lemma. For POS-tagging, the results come up just short from the first place in this competition, mainly struggling with proper nouns. For morphological tagging, there is much more room for improvement. Here, the constraints added to our Multiclass Multilabel model were often not tight enough, causing missing morphological features. We will further investigate why the combination of the different morphological features, which perform fine on their own, leads to issues.

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Transformer-based Part-of-Speech Tagging and Lemmatization for Latin
Krzysztof Wróbel | Krzysztof Nowak

The paper presents a submission to the EvaLatin 2022 shared task. Our system places first for lemmatization, part-of-speech and morphological tagging in both closed and open modalities. The results for cross-genre and cross-time sub-tasks show that the system handles the diachronic and diastratic variation of Latin. The architecture employs state-of-the-art transformer models. For part-of-speech and morphological tagging, we use XLM-RoBERTa large, while for lemmatization a ByT5 small model was employed. The paper features a thorough discussion of part-of-speech and lemmatization errors which shows how the system performance may be improved for Classical, Medieval and Neo-Latin texts.

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bib (full) Proceedings of the 18th Workshop on Multiword Expressions @LREC2022

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Proceedings of the 18th Workshop on Multiword Expressions @LREC2022
Archna Bhatia | Paul Cook | Shiva Taslimipoor | Marcos Garcia | Carlos Ramisch

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Figurative Language in Noun Compound Models across Target Properties, Domains and Time
Sabine Schulte im Walde

A variety of distributional and multi-modal computational approaches has been suggested for modelling the degrees of compositionality across types of multiword expressions and languages. As the starting point of my talk, I will present standard variants of computational models that have been proven successful in predicting the compositionality of German and English noun compounds. The main part of the talk will then be concerned with investigating the general reliability of these standard models and discussing implications for gold-standard datasets: I will demonstrate how prediction results vary (i) across representations, (ii) across empirical target properties, (iii) across compound types, (iv) across levels of abstractness, and (v) for general- vs. domain-specific language. Finally, I will present a preliminary quantitative study on diachronic changes of noun compound meanings and compositionality over time.

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Multiword Expressions and the Low-Resource Scenario from the Perspective of a Local Oral Culture
Steven Bird

Research on multiword expressions and on under-resourced languages often begins with problematisation. The existence of non-compositional meaning, or the paucity of conventional language resources, are treated as problems to be solved. This perspective is associated with the view of Language as a lexico-grammatical code, and of NLP as a conventional sequence of computational tasks. In this talk, I share from my experience in an Australian Aboriginal community, where people tend to see language as an expression of identity and of ‘connection to country’. Here, my early attempts to collect language data were thwarted. There was no obvious role for tasks like speech recognition, parsing, or translation. Instead, working under the authority of local elders, I pivoted to language processing tasks that were more in keeping with local interests and aspirations. I describe these tasks and suggest some new ways of framing the work of NLP, and I explore implications for work on multiword expressions and on under-resourced languages.

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A General Framework for Detecting Metaphorical Collocations
Marija Brkić Bakarić | Lucia Načinović Prskalo | Maja Popović

This paper aims at identifying a specific set of collocations known under the term metaphorical collocations. In this type of collocations, a semantic shift has taken place in one of the components. Since the appropriate gold standard needs to be compiled prior to any serious endeavour to extract metaphorical collocations automatically, this paper first presents the steps taken to compile it, and then establishes appropriate evaluation framework. The process of compiling the gold standard is illustrated on one of the most frequent Croatian nouns, which resulted in the preliminary relation significance set. With the aim to investigate the possibility of facilitating the process, frequency, logDice, relation, and pretrained word embeddings are used as features in the classification task conducted on the logDice-based word sketch relation lists. Preliminary results are presented.

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Improving Grammatical Error Correction for Multiword Expressions
Shiva Taslimipoor | Christopher Bryant | Zheng Yuan

Grammatical error correction (GEC) is the task of automatically correcting errors in text. It has mainly been developed to assist language learning, but can also be applied to native text. This paper reports on preliminary work in improving GEC for multiword expression (MWE) error correction. We propose two systems which incorporate MWE information in two different ways: one is a multi-encoder decoder system which encodes MWE tags in a second encoder, and the other is a BART pre-trained transformer-based system that encodes MWE representations using special tokens. We show improvements in correcting specific types of verbal MWEs based on a modified version of a standard GEC evaluation approach.

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An Analysis of Attention in German Verbal Idiom Disambiguation
Rafael Ehren | Laura Kallmeyer | Timm Lichte

In this paper we examine a BiLSTM architecture for disambiguating verbal potentially idiomatic expressions (PIEs) as to whether they are used in a literal or an idiomatic reading with respect to explainability of its decisions. Concretely, we extend the BiLSTM with an additional attention mechanism and track the elements that get the highest attention. The goal is to better understand which parts of an input sentence are particularly discriminative for the classifier’s decision, based on the assumption that these elements receive a higher attention than others. In particular, we investigate POS tags and dependency relations to PIE verbs for the tokens with the maximal attention. It turns out that the elements with maximal attention are oftentimes nouns that are the subjects of the PIE verb. For longer sentences however (i.e., sentences containing, among others, more modifiers), the highest attention word often stands in a modifying relation to the PIE components. This is particularly frequent for PIEs classified as literal. Our study shows that an attention mechanism can contribute to the explainability of classification decisions that depend on specific cues in the sentential context, as it is the case for PIE disambiguation.

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Support Verb Constructions across the Ocean Sea
Jorge Baptista | Nuno Mamede | Sónia Reis

This paper analyses the support (or light) verb constructions (SVC) in a publicly available, manually annotated corpus of multiword expressions (MWE) in Brazilian Portuguese. The paper highlights several issues in the linguistic definitions therein adopted for these types of MWE, and reports the results from applying STRING, a rule-based parsing system, originally developed for European Portuguese, to this corpus from Brazilian Portuguese. The goal is two-fold: to improve the linguistic definition of SVC in the annotation task, as well as to gauge the major difficulties found when transposing linguistic resources between these two varieties of the same language.

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A Matrix-Based Heuristic Algorithm for Extracting Multiword Expressions from a Corpus
Orhan Bilgin

This paper describes an algorithm for automatically extracting multiword expressions (MWEs) from a corpus. The algorithm is node-based, i.e. extracts MWEs that contain the item specified by the user, using a fixed window-size around the node. The main idea is to detect the frequency anomalies that occur at the starting and ending points of an ngram that constitutes a MWE. This is achieved by locally comparing matrices of observed frequencies to matrices of expected frequencies, and determining, for each individual input, one or more sub-sequences that have the highest probability of being a MWE. Top-performing sub-sequences are then combined in a score-aggregation and ranking stage, thus producing a single list of score-ranked MWE candidates, without having to indiscriminately generate all possible sub-sequences of the input strings. The knowledge-poor and computationally efficient algorithm attempts to solve certain recurring problems in MWE extraction, such as the inability to deal with MWEs of arbitrary length, the repetitive counting of nested ngrams, and excessive sensitivity to frequency. Evaluation results show that the best-performing version generates top-50 precision values between 0.71 and 0.88 on Turkish and English data, and performs better than the baseline method even at n=1000.

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Multi-word Lexical Units Recognition in WordNet
Marek Maziarz | Ewa Rudnicka | Łukasz Grabowski

WordNet is a state-of-the-art lexical resource used in many tasks in Natural Language Processing, also in multi-word expression (MWE) recognition. However, not all MWEs recorded in WordNet could be indisputably called lexicalised. Some of them are semantically compositional and show no signs of idiosyncrasy. This state of affairs affects all evaluation measures that use the list of all WordNet MWEs as a gold standard. We propose a method of distinguishing between lexicalised and non-lexicalised word combinations in WordNet, taking into account lexicality features, such as semantic compositionality, MWE length and translational criterion. Both a rule-based approach and a ridge logistic regression are applied, beating a random baseline in precision of singling out lexicalised MWEs, as well as in recall of ruling out cases of non-lexicalised MWEs.

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Automatic Detection of Difficulty of French Medical Sequences in Context
Anaïs Koptient | Natalia Grabar

Medical documents use technical terms (single or multi-word expressions) with very specific semantics. Patients may find it difficult to understand these terms, which may lower their understanding of medical information. Before the simplification step of such terms, it is important to detect difficult to understand syntactic groups in medical documents as they may correspond to or contain technical terms. We address this question through categorization: we have to predict difficult to understand syntactic groups within syntactically analyzed medical documents. We use different models for this task: one built with only internal features (linguistic features), one built with only external features (contextual features), and one built with both sets of features. Our results show an f-measure over 0.8. Use of contextual (external) features and of annotations from all annotators impact the results positively. Ablation tests indicate that frequencies in large corpora and lexicon are relevant for this task.

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Annotating “Particles” in Multiword Expressions in te reo Māori for a Part-of-Speech Tagger
Aoife Finn | Suzanne Duncan | Peter-Lucas Jones | Gianna Leoni | Keoni Mahelona

This paper discusses the development of a Part-of-Speech tagger for te reo Māori, which is the Indigenous language of Aotearoa, also known as New Zealand. Te reo Māori is a particularly analytical and polysemic language. A word class called “particles” is introduced, they are small multi-functional words with many meanings, for example ē, ai, noa, rawa, mai, anō and koa. These “particles” are reflective of the analytical and polysemous nature of te reo Māori. They frequently occur both singularly and also in multiword expressions, including time adverbial phrases. The paper illustrates the challenges that they presented to part-of-speech tagging. It also discusses how we overcome these challenges in a way that is appropriate for te reo Māori, given its status an Indigenous language and history of colonisation. This includes a discussion of the importance of accurately reflecting the conceptualization of te reo Māori. And how this involved making no linguistic presumptions, and of eliciting faithful judgements from speakers, in a way that is uninfluenced by linguistic terminology.

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Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High German
Felix Schneider | Sven Sickert | Phillip Brandes | Sophie Marshall | Joachim Denzler

In this work, we present a novel unsupervised method for adjective-noun metaphor detection on low resource languages. We propose two new approaches: First, a way of artificially generating metaphor training examples and second, a novel way to find metaphors relying only on word embeddings. The latter enables application for low resource languages. Our method is based on a transformation of word embedding vectors into another vector space, in which the distance between the adjective word vector and the noun word vector represents the metaphoricity of the word pair. We train this method in a zero-shot pseudo-supervised manner by generating artificial metaphor examples and show that our approach can be used to generate a metaphor dataset with low annotation cost. It can then be used to finetune the system in a few-shot manner. In our experiments we show the capabilities of the method in its unsupervised and in its supervised version. Additionally, we test it against a comparable unsupervised baseline method and a supervised variation of it.

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Automatic Bilingual Phrase Dictionary Construction from GIZA++ Output
Albina Khusainova | Vitaly Romanov | Adil Khan

Modern encoder-decoder based neural machine translation (NMT) models are normally trained on parallel sentences. Hence, they give best results when translating full sentences rather than sentence parts. Thereby, the task of translating commonly used phrases, which often arises for language learners, is not addressed by NMT models. While for high-resourced language pairs human-built phrase dictionaries exist, less-resourced pairs do not have them. We suggest an approach for building such dictionary automatically based on the GIZA++ output and show that it works significantly better than translating phrases with a sentences-trained NMT system.

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A BERT’s Eye View: Identification of Irish Multiword Expressions Using Pre-trained Language Models
Abigail Walsh | Teresa Lynn | Jennifer Foster

This paper reports on the investigation of using pre-trained language models for the identification of Irish verbal multiword expressions (vMWEs), comparing the results with the systems submitted for the PARSEME shared task edition 1.2. We compare the use of a monolingual BERT model for Irish (gaBERT) with multilingual BERT (mBERT), fine-tuned to perform MWE identification, presenting a series of experiments to explore the impact of hyperparameter tuning and dataset optimisation steps on these models. We compare the results of our optimised systems to those achieved by other systems submitted to the shared task, and present some best practices for minority languages addressing this task.

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Enhancing the PARSEME Turkish Corpus of Verbal Multiword Expressions
Yagmur Ozturk | Najet Hadj Mohamed | Adam Lion-Bouton | Agata Savary

The PARSEME (Parsing and Multiword Expressions) project proposes multilingual corpora annotated for multiword expressions (MWEs). In this case study, we focus on the Turkish corpus of PARSEME. Turkish is an agglutinative language and shows high inflection and derivation in word forms. This can cause some issues in terms of automatic morphosyntactic annotation. We provide an overview of the problems observed in the morphosyntactic annotation of the Turkish PARSEME corpus. These issues are mostly observed on the lemmas, which is important for the approximation of a type of an MWE. We propose modifications of the original corpus with some enhancements on the lemmas and parts of speech. The enhancements are then evaluated with an identification system from the PARSEME Shared Task 1.2 to detect MWEs, namely Seen2Seen. Results show increase in the F-measure for MWE identification, emphasizing the necessity of robust morphosyntactic annotation for MWE processing, especially for languages that show high surface variability.

Deep neural models, in particular Transformer-based pre-trained language models, require a significant amount of data to train. This need for data tends to lead to problems when dealing with idiomatic multiword expressions (MWEs), which are inherently less frequent in natural text. As such, this work explores sample efficient methods of idiomaticity detection. In particular we study the impact of Pattern Exploit Training (PET), a few-shot method of classification, and BERTRAM, an efficient method of creating contextual embeddings, on the task of idiomaticity detection. In addition, to further explore generalisability, we focus on the identification of MWEs not present in the training data. Our experiments show that while these methods improve performance on English, they are much less effective on Portuguese and Galician, leading to an overall performance about on par with vanilla mBERT. Regardless, we believe sample efficient methods for both identifying and representing potentially idiomatic MWEs are very encouraging and hold significant potential for future exploration.

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mwetoolkit-lib: Adaptation of the mwetoolkit as a Python Library and an Application to MWE-based Document Clustering
Fernando Zagatti | Paulo Augusto de Lima Medeiros | Esther da Cunha Soares | Lucas Nildaimon dos Santos Silva | Carlos Ramisch | Livy Real

This paper introduces the mwetoolkit-lib, an adaptation of the mwetoolkit as a python library. The original toolkit performs the extraction and identification of multiword expressions (MWEs) in large text bases through the command line. One of the contributions of our work is the adaptation of the MWE extraction pipeline from the mwetoolkit, allowing its usage in python development environments and integration in larger pipelines. The other contribution is the execution of a pilot experiment aiming to show the impact of MWE discovery in data professionals’ work. This experiment found that the addition of MWE knowledge to the Term Frequency-Inverse Document Frequency (TF-IDF) vectorization altered the word relevance order, improving the linguistic quality of the clusters returned by k-means method.

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Handling Idioms in Symbolic Multilingual Natural Language Generation
Michaelle Dubé | François Lareau

While idioms are usually very rigid in their expression, they sometimes allow a certain level of freedom in their usage, with modifiers or complements splitting them or being syntactically attached to internal nodes rather than to the root (e.g., “take something with a big grain of salt”). This means that they cannot always be handled as ready-made strings in rule-based natural language generation systems. Having access to the internal syntactic structure of an idiom allows for more subtle processing. We propose a way to enumerate all possible language-independent n-node trees and to map particular idioms of a language onto these generic syntactic patterns. Using this method, we integrate the idioms from the LN-fr into GenDR, a multilingual realizer. Our implementation covers nearly 98% of LN-fr’s idioms with high precision, and can easily be extended or ported to other languages.

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bib (full) Proceedings of the 2nd Workshop on Novel Incentives in Data Collection from People: models, implementations, challenges and results within LREC 2022

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Proceedings of the 2nd Workshop on Novel Incentives in Data Collection from People: models, implementations, challenges and results within LREC 2022
Chris Callison-Burch | Christopher Cieri | James Fiumara | Mark Liberman

This paper provides an overview and update on the Linguistic Data Consortium’s (LDC) NIEUW (Novel Incentives and Workflows) project supported by the National Science Foundation and part of LDC’s larger goal of improving the cost, variety, scale, and quality of language resources available for education, research, and technology development. NIEUW leverages the power of novel incentives to elicit linguistic data and annotations from a wide variety of contributors including citizen scientists, game players, and language students and professionals. In order to align appropriate incentives with the various contributors, LDC has created three distinct web portals to bring together researchers and other language professionals with participants best suited to their project needs. These portals include LanguageARC designed for citizen scientists, Machina Pro Linguistica designed for students and language professionals, and LingoBoingo designed for game players. The design, interface, and underlying tools for each web portal were developed to appeal to the different incentives and motivations of their respective target audiences.

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Use of a Citizen Science Platform for the Creation of a Language Resource to Study Bias in Language Models for French: A Case Study
Karën Fort | Aurélie Névéol | Yoann Dupont | Julien Bezançon

There is a growing interest in the evaluation of bias, fairness and social impact of Natural Language Processing models and tools. However, little resources are available for this task in languages other than English. Translation of resources originally developed for English is a promising research direction. However, there is also a need for complementing translated resources by newly sourced resources in the original languages and social contexts studied. In order to collect a language resource for the study of biases in Language Models for French, we decided to resort to citizen science. We created three tasks on the LanguageARC citizen science platform to assist with the translation of an existing resource from English into French as well as the collection of complementary resources in native French. We successfully collected data for all three tasks from a total of 102 volunteer participants. Participants from different parts of the world contributed and we noted that although calls sent to mailing lists had a positive impact on participation, some participants pointed barriers to contributions due to the collection platform.

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Fearless Steps APOLLO: Advanced Naturalistic Corpora Development
John H.L. Hansen | Aditya Joglekar | Szu-Jui Chen | Meena Chandra Shekar | Chelzy Belitz

In this study, we present the Fearless Steps APOLLO Community Resource, a collection of audio and corresponding meta-data diarized from the NASA Apollo Missions. Massive naturalistic speech data which is time-synchronized, without any human subject privacy constraints is very rare and difficult to organize, collect, and deploy. The Apollo Missions Audio is the largest collection of multi-speaker multi-channel data, where over 600 personnel are communicating over multiple missions to achieve strategic space exploration goals. A total of 12 manned missions over a six-year period produced extensive 30-track 1-inch analog tapes containing over 150,000 hours of audio. This presents the wider research community a unique opportunity to extract multi-modal knowledge in speech science, team cohesion and group dynamics, and historical archive preservation. We aim to make this entire resource and supporting speech technology meta-data creation publicly available as a Community Resource for the development of speech and behavioral science. Here we present the development of this community resource, our outreach efforts, and technological developments resulting from this data. We finally discuss the planned future directions for this community resource.

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Creating Mexican Spanish Language Resources through the Social Service Program
Carlos Daniel Hernandez Mena | Ivan Vladimir Meza Ruiz

This work presents the path toward the creation of eight Spoken Language Resources under the umbrella of the Mexican Social Service national program. This program asks undergraduate students to donate time and work for the benefit of their society as a requirement to receive their degree. The program has thousands of options for the students who enroll. We show how we created a program which has resulted in the creation of open language resources which now are freely available in different repositories. We estimate that this exercise is equivalent to a budget of more than half a million US dollars. However, since the program is based on retribution from the students to their communities there has not been a necessity of a financial budget.

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Fictionary-Based Games for Language Resource Creation
Steinunn Rut Friðriksdóttir | Hafsteinn Einarsson

In this paper, we present a novel approach to data collection for natural language processing (NLP), linguistic research and lexicographic work. Using the parlor game Fictionary as a framework, data can be crowd-sourced in a gamified manner, which carries the potential of faster, cheaper and better data when compared to traditional methods due to the engaging and competitive nature of the game. To improve data quality, the game includes a built-in review process where players review each other’s data and evaluate its quality. The paper proposes several games that can be used within this framework, and explains the value of the data generated by their use. These proposals include games that collect named entities along with their corresponding type tags, question-answer pairs, translation pairs and neologism, to name only a few. We are currently working on a digital platform that will host these games in Icelandic but wish to open the discussion around this topic and encourage other researchers to explore their own versions of the proposed games, all of which are language-independent.

This paper describes our use of mixed incentives and the citizen science portal LanguageARC to prepare, collect and quality control a large corpus of object namings for the purpose of providing speech data to document the under-represented Guanzhong dialect of Chinese spoken in the Shaanxi province in the environs of Xi’an.

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Crowdsourced Participants’ Accuracy at Identifying the Social Class of Speakers from South East England
Amanda Cole

Five participants, each located in distinct locations (USA, Canada, South Africa, Scotland and (South East) England), identified the self-determined social class of a corpus of 227 speakers (born 1986–2001; from South East England) based on 10-second passage readings. This pilot study demonstrates the potential for using crowdsourcing to collect sociolinguistic data, specifically using LanguageARC, especially when geographic spread of participants is desirable but not easily possible using traditional fieldwork methods. Results show that, firstly, accuracy at identifying social class is relatively low when compared to other factors, including when the same speech stimuli were used (e.g., ethnicity: Cole 2020). Secondly, participants identified speakers’ social class significantly better than chance for a three-class distinction (working, middle, upper) but not for a six-class distinction. Thirdly, despite some differences in performance, the participant located in South East England did not perform significantly better than other participants, suggesting that the participant’s presumed greater familiarity with sociolinguistic variation in the region may not have been advantageous. Finally, there is a distinction to be made between participants’ ability to pinpoint a speaker’s exact social class membership and their ability to identify the speaker’s relative class position. This paper discusses the role of social identification tasks in illuminating how speech is categorised and interpreted.

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About the Applicability of Combining Implicit Crowdsourcing and Language Learning for the Collection of NLP Datasets
Verena Lyding | Lionel Nicolas | Alexander König

In this article, we present a recent trend of approaches, hereafter referred to as Collect4NLP, and discuss its applicability. Collect4NLP-based approaches collect inputs from language learners through learning exercises and aggregate the collected data to derive linguistic knowledge of expert quality. The primary purpose of these approaches is to improve NLP resources, however sincere concern with the needs of learners is crucial for making Collect4NLP work. We discuss the applicability of Collect4NLP approaches in relation to two perspectives. On the one hand, we compare Collect4NLP approaches to the two crowdsourcing trends currently most prevalent in NLP, namely Crowdsourcing Platforms (CPs) and Games-With-A-Purpose (GWAPs), and identify strengths and weaknesses of each trend. By doing so we aim to highlight particularities of each trend and to identify in which kind of settings one trend should be favored over the other two. On the other hand, we analyze the applicability of Collect4NLP approaches to the production of different types of NLP resources. We first list the types of NLP resources most used within its community and second propose a set of blueprints for mapping these resources to well-established language learning exercises as found in standard language learning textbooks.

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The Influence of Intrinsic and Extrinsic Motivation on the Creation of Language Resources in a Citizen Linguistics Project about Lexicography
Barbara Heinisch

In the field of citizen linguistics, various initiatives are aimed at the creation of language resources by members of the public. To recruit and retain these participants different incentives informed by different motivations, extrinsic and intrinsic ones, play a role at different project stages. Illustrated by a project in the field of lexicography which draws on the extrinsic and/or intrinsic motivation of participants, the complexity of providing the ‘right’ incentives is addressed. This complexity does not only surface when considering cultural differences and the heterogeneity of the motivations participants might have but also through the changing motivations over time. Here, identifying target groups may help to guide recruitment, retention and dissemination activities. In addition, continuous adaptations may be required during the course of the project to strike a balance between necessary and feasible incentives.

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bib (full) Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022

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Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022
Gavin Abercrombie | Valerio Basile | Sara Tonelli | Verena Rieser | Alexandra Uma

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Disagreement Space in Argument Analysis
Annette Hautli-Janisz | Ella Schad | Chris Reed

For a highly subjective task such as recognising speaker intention and argumentation, the traditional way of generating gold standards is to aggregate a number of labels into a single one. However, this seriously neglects the underlying richness that characterises discourse and argumentation and is also, in some cases, straightforwardly impossible. In this paper, we present QT30nonaggr, the first corpus of non-aggregated argument annotation, which will be openly available upon publication. QT30nonaggr encompasses 10% of QT30, the largest corpus of dialogical argumentation and analysed broadcast political debate currently available with 30 episodes of BBC’s ‘Question Time’ from 2020 and 2021. Based on a systematic and detailed investigation of annotation judgements across all steps of the annotation process, we structure the disagreement space with a taxonomy of the types of label disagreements in argument annotation, identifying the categories of annotation errors, fuzziness and ambiguity.

Recent studies have shown that for subjective annotation tasks, the demographics, lived experiences, and identity of annotators can have a large impact on how items are labeled. We expand on this work, hypothesizing that gender may correlate with differences in annotations for a number of NLP benchmarks, including those that are fairly subjective (e.g., affect in text) and those that are typically considered to be objective (e.g., natural language inference). We develop a robust framework to test for differences in annotation across genders for four benchmark datasets. While our results largely show a lack of statistically significant differences in annotation by males and females for these tasks, the framework can be used to analyze differences in annotation between various other demographic groups in future work. Finally, we note that most datasets are collected without annotator demographics and released only in aggregate form; we call on the community to consider annotator demographics as data is collected, and to release dis-aggregated data to allow for further work analyzing variability among annotators.

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Predicting Literary Quality How Perspectivist Should We Be?
Yuri Bizzoni | Ida Marie Lassen | Telma Peura | Mads Rosendahl Thomsen | Kristoffer Nielbo

Approaches in literary quality tend to belong to two main grounds: one sees quality as completely subjective, relying on the idiosyncratic nature of individual perspectives on the apperception of beauty; the other is ground-truth inspired, and attempts to find one or two values that predict something like an objective quality: the number of copies sold, for example, or the winning of a prestigious prize. While the first school usually does not try to predict quality at all, the second relies on a single majority vote in one form or another. In this article we discuss the advantages and limitations of these schools of thought and describe a different approach to reader’s quality judgments, which moves away from raw majority vote, but does try to create intermediate classes or groups of annotators. Drawing on previous works we describe the benefits and drawbacks of building similar annotation classes. Finally we share early results from a large corpus of literary reviews for an insight into which classes of readers might make most sense when dealing with the appreciation of literary quality.

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Bias Discovery within Human Raters: A Case Study of the Jigsaw Dataset
Marta Marchiori Manerba | Riccardo Guidotti | Lucia Passaro | Salvatore Ruggieri

Understanding and quantifying the bias introduced by human annotation of data is a crucial problem for trustworthy supervised learning. Recently, a perspectivist trend has emerged in the NLP community, focusing on the inadequacy of previous aggregation schemes, which suppose the existence of single ground truth. This assumption is particularly problematic for sensitive tasks involving subjective human judgments, such as toxicity detection. To address these issues, we propose a preliminary approach for bias discovery within human raters by exploring individual ratings for specific sensitive topics annotated in the texts. Our analysis’s object consists of the Jigsaw dataset, a collection of comments aiming at challenging online toxicity identification.

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The Viability of Best-worst Scaling and Categorical Data Label Annotation Tasks in Detecting Implicit Bias
Parker Glenn | Cassandra L. Jacobs | Marvin Thielk | Yi Chu

Annotating workplace bias in text is a noisy and subjective task. In encoding the inherently continuous nature of bias, aggregated binary classifications do not suffice. Best-worst scaling (BWS) offers a framework to obtain real-valued scores through a series of comparative evaluations, but it is often impractical to deploy to traditional annotation pipelines within industry. We present analyses of a small-scale bias dataset, jointly annotated with categorical annotations and BWS annotations. We show that there is a strong correlation between observed agreement and BWS score (Spearman’s r=0.72). We identify several shortcomings of BWS relative to traditional categorical annotation: (1) When compared to categorical annotation, we estimate BWS takes approximately 4.5x longer to complete; (2) BWS does not scale well to large annotation tasks with sparse target phenomena; (3) The high correlation between BWS and the traditional task shows that the benefits of BWS can be recovered from a simple categorically annotated, non-aggregated dataset.

A unified gold standard commonly exploited in natural language processing (NLP) tasks requires high inter-annotator agreement. However, there are many subjective problems that should respect users individual points of view. Therefore in this paper, we evaluate three different personalized methods on the task of hate speech detection. The user-centered techniques are compared to the generalizing baseline approach. We conduct our experiments on three datasets including single-task and multi-task hate speech detection. For validation purposes, we introduce a new data-split strategy, preventing data leakage between training and testing. In order to better understand the model behavior for individual users, we carried out personalized ablation studies. Our experiments revealed that all models leveraging user preferences in any case provide significantly better results than most frequently used generalized approaches. This supports our overall observation that personalized models should always be considered in all subjective NLP tasks, including hate speech detection.

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StudEmo: A Non-aggregated Review Dataset for Personalized Emotion Recognition
Anh Ngo | Agri Candri | Teddy Ferdinan | Jan Kocon | Wojciech Korczynski

Humans’ emotional perception is subjective by nature, in which each individual could express different emotions regarding the same textual content. Existing datasets for emotion analysis commonly depend on a single ground truth per data sample, derived from majority voting or averaging the opinions of all annotators. In this paper, we introduce a new non-aggregated dataset, namely StudEmo, that contains 5,182 customer reviews, each annotated by 25 people with intensities of eight emotions from Plutchik’s model, extended with valence and arousal. We also propose three personalized models that use not only textual content but also the individual human perspective, providing the model with different approaches to learning human representations. The experiments were carried out as a multitask classification on two datasets: our StudEmo dataset and GoEmotions dataset, which contains 28 emotional categories. The proposed personalized methods significantly improve prediction results, especially for emotions that have low inter-annotator agreement.

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Annotator Response Distributions as a Sampling Frame
Christopher Homan | Tharindu Cyril Weerasooriya | Lora Aroyo | Chris Welty

Annotator disagreement is often dismissed as noise or the result of poor annotation process quality. Others have argued that it can be meaningful. But lacking a rigorous statistical foundation, the analysis of disagreement patterns can resemble a high-tech form of tea-leaf-reading. We contribute a framework for analyzing the variation of per-item annotator response distributions to data for humans-in-the-loop machine learning. We provide visualizations for, and use the framework to analyze the variance in, a crowdsourced dataset of hard-to-classify examples from the OpenImages archive.

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Variation in the Expression and Annotation of Emotions: A Wizard of Oz Pilot Study
Sofie Labat | Naomi Ackaert | Thomas Demeester | Veronique Hoste

This pilot study employs the Wizard of Oz technique to collect a corpus of written human-computer conversations in the domain of customer service. The resulting dataset contains 192 conversations and is used to test three hypotheses related to the expression and annotation of emotions. First, we hypothesize that there is a discrepancy between the emotion annotations of the participant (the experiencer) and the annotations of our external annotator (the observer). Furthermore, we hypothesize that the personality of the participants has an influence on the emotions they expressed, and on the way they evaluated (annotated) these emotions. We found that for an external, trained annotator, not all emotion labels were equally easy to work with. We also noticed that the trained annotator had a tendency to opt for emotion labels that were more centered in the valence-arousal space, while participants made more ‘extreme’ annotations. For the second hypothesis, we discovered a positive correlation between the personality trait extraversion and the emotion dimensions valence and dominance in our sample. Finally, for the third premise, we observed a positive correlation between the internal-external agreement on emotion labels and the personality traits conscientiousness and extraversion. Our insights and findings will be used in future research to conduct a larger Wizard of Oz experiment.

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Beyond Explanation: A Case for Exploratory Text Visualizations of Non-Aggregated, Annotated Datasets
Lucy Havens | Benjamin Bach | Melissa Terras | Beatrice Alex

This paper presents an overview of text visualization techniques relevant for data perspectivism, aiming to facilitate analysis of annotated datasets for the datasets’ creators and stakeholders. Data perspectivism advocates for publishing non-aggregated, annotated text data, recognizing that for highly subjective tasks, such as bias detection and hate speech detection, disagreements among annotators may indicate conflicting yet equally valid interpretations of a text. While the publication of non-aggregated, annotated data makes different interpretations of text corpora available, barriers still exist to investigating patterns and outliers in annotations of the text. Techniques from text visualization can overcome these barriers, facilitating intuitive data analysis for NLP researchers and practitioners, as well as stakeholders in NLP systems, who may not have data science or computing skills. In this paper we discuss challenges with current dataset creation practices and annotation platforms, followed by a discussion of text visualization techniques that enable open-ended, multi-faceted, and iterative analysis of annotated data.

We introduce the Measuring Hate Speech corpus, a dataset created to measure hate speech while adjusting for annotators’ perspectives. It consists of 50,070 social media comments spanning YouTube, Reddit, and Twitter, labeled by 11,143 annotators recruited from Amazon Mechanical Turk. Each observation includes 10 ordinal labels: sentiment, disrespect, insult, attacking/defending, humiliation, inferior/superior status, dehumanization, violence, genocide, and a 3-valued hate speech benchmark label. The labels are aggregated using faceted Rasch measurement theory (RMT) into a continuous score that measures each comment’s location on a hate speech spectrum. The annotation experimental design assigned comments to multiple annotators in order to yield a linked network, allowing annotator disagreement (perspective) to be statistically summarized. Annotators’ labeling strictness was estimated during the RMT scaling, projecting their perspective onto a linear measure that was adjusted for the hate speech score. Models that incorporate this annotator perspective parameter as an auxiliary input can generate label- and score-level predictions conditional on annotator perspective. The corpus includes the identity group targets of each comment (8 groups, 42 subgroups) and annotator demographics (6 groups, 40 subgroups), facilitating analyses of interactions between annotator- and comment-level identities, i.e. identity-related annotator perspective.

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Improving Label Quality by Jointly Modeling Items and Annotators
Tharindu Cyril Weerasooriya | Alexander Ororbia | Christopher Homan

We propose a fully Bayesian framework for learning ground truth labels from noisy annotators. Our framework ensures scalability by factoring a generative, Bayesian soft clustering model over label distributions into the classic David and Skene joint annotator-data model. Earlier research along these lines has neither fully incorporated label distributions nor explored clustering by annotators only or data only. Our framework incorporates all of these properties within a graphical model designed to provide better ground truth estimates of annotator responses as input to any black box supervised learning algorithm. We conduct supervised learning experiments with variations of our models and compare them to the performance of several baseline models.

This paper presents Lutma, a collaborative, semi-constrained, tutorial-based tool for contributing frames and lexical units to the Global FrameNet initiative. The tool parameterizes the process of frame creation, avoiding consistency violations and promoting the integration of frames contributed by the community with existing frames. Lutma is structured in a wizard-like fashion so as to provide users with text and video tutorials relevant for each step in the frame creation process. We argue that this tool will allow for a sensible expansion of FrameNet coverage in terms of both languages and cultural perspectives encoded by them, positioning frames as a viable alternative for representing perspective in language models.

This paper argues in favor of the adoption of annotation practices for multimodal datasets that recognize and represent the inherently perspectivized nature of multimodal communication. To support our claim, we present a set of annotation experiments in which FrameNet annotation is applied to the Multi30k and the Flickr 30k Entities datasets. We assess the cosine similarity between the semantic representations derived from the annotation of both pictures and captions for frames. Our findings indicate that: (i) frame semantic similarity between captions of the same picture produced in different languages is sensitive to whether the caption is a translation of another caption or not, and (ii) picture annotation for semantic frames is sensitive to whether the image is annotated in presence of a caption or not.

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Change My Mind: How Syntax-based Hate Speech Recognizer Can Uncover Hidden Motivations Based on Different Viewpoints
Michele Mastromattei | Valerio Basile | Fabio Massimo Zanzotto

Hate speech recognizers may mislabel sentences by not considering the different opinions that society has on selected topics. In this paper, we show how explainable machine learning models based on syntax can help to understand the motivations that induce a sentence to be offensive to a certain demographic group. By comparing and contrasting the results, we show the key points that make a sentence labeled as hate speech and how this varies across different ethnic groups.

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bib (full) Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection

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Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection
Hend Al-Khalifa | Tamer Elsayed | Hamdy Mubarak | Abdulmohsen Al-Thubaity | Walid Magdy | Kareem Darwish

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TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation
El Moatez Billah Nagoudi | AbdelRahim Elmadany | Muhammad Abdul-Mageed

We present TURJUMAN, a neural toolkit for translating from 20 languages into Modern Standard Arabic (MSA). TURJUMAN exploits the recently-introduced text-to-text Transformer AraT5 model, endowing it with a powerful ability to decode into Arabic. The toolkit offers the possibility of employing a number of diverse decoding methods, making it suited for acquiring paraphrases for the MSA translations as an added value. To train TURJUMAN, we sample from publicly available parallel data employing a simple semantic similarity method to ensure data quality. This allows us to prepare and release AraOPUS-20, a new machine translation benchmark. We publicly release our translation toolkit (TURJUMAN) as well as our benchmark dataset (AraOPUS-20).

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Detecting Users Prone to Spread Fake News on Arabic Twitter
Zien Sheikh Ali | Abdulaziz Al-Ali | Tamer Elsayed

The spread of misinformation has become a major concern to our society, and social media is one of its main culprits. Evidently, health misinformation related to vaccinations has slowed down global efforts to fight the COVID-19 pandemic. Studies have shown that fake news spreads substantially faster than real news on social media networks. One way to limit this fast dissemination is by assessing information sources in a semi-automatic way. To this end, we aim to identify users who are prone to spread fake news in Arabic Twitter. Such users play an important role in spreading misinformation and identifying them has the potential to control the spread. We construct an Arabic dataset on Twitter users, which consists of 1,546 users, of which 541 are prone to spread fake news (based on our definition). We use features extracted from users’ recent tweets, e.g., linguistic, statistical, and profile features, to predict whether they are prone to spread fake news or not. To tackle the classification task, multiple learning models are employed and evaluated. Empirical results reveal promising detection performance, where an F1 score of 0.73 was achieved by the logistic regression model. Moreover, when tested on a benchmark English dataset, our approach has outperformed the current state-of-the-art for this task.

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AraSAS: The Open Source Arabic Semantic Tagger
Mahmoud El-Haj | Elvis de Souza | Nouran Khallaf | Paul Rayson | Nizar Habash

This paper presents (AraSAS) the first open-source Arabic semantic analysis tagging system. AraSAS is a software framework that provides full semantic tagging of text written in Arabic. AraSAS is based on the UCREL Semantic Analysis System (USAS) which was first developed to semantically tag English text. Similarly to USAS, AraSAS uses a hierarchical semantic tag set that contains 21 major discourse fields and 232 fine-grained semantic field tags. The paper describes the creation, validation and evaluation of AraSAS. In addition, we demonstrate a first case study to illustrate the affordances of applying USAS and AraSAS semantic taggers on the Zayed University Arabic-English Bilingual Undergraduate Corpus (ZAEBUC) (Palfreyman and Habash, 2022), where we show and compare the coverage of the two semantic taggers through running them on Arabic and English essays on different topics. The analysis expands to compare the taggers when run on texts in Arabic and English written by the same writer and texts written by male and by female students. Variables for comparison include frequency of use of particular semantic sub-domains, as well as the diversity of semantic elements within a text.

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AraNPCC: The Arabic Newspaper COVID-19 Corpus
Abdulmohsen Al-Thubaity | Sakhar Alkhereyf | Alia O. Bahanshal

This paper introduces a corpus for Arabic newspapers during COVID-19: AraNPCC. The AraNPCC corpus covers 2019 until 2021 via automatically-collected data from 12 Arab countries. It comprises more than 2 billion words and 7.2 million texts alongside their metadata. AraNPCC can be used for several natural language processing tasks, such as updating available Arabic language models or corpus linguistics tasks, including language change over time. We utilized the corpus in two case studies. In the first case study, we investigate the correlation between the number of officially reported infected cases and the collective word frequency of “COVID” and “Corona.” The data shows a positive correlation that varies among Arab countries. For the second case study, we extract and compare the top 50 keywords in 2020 and 2021 to study the impact of the COVID-19 pandemic on two Arab countries, namely Algeria and Saudi Arabia. For 2020, the data shows that the two countries’ newspapers strongly interacted with the pandemic, emphasizing its spread and dangerousness, and in 2021 the data suggests that the two countries coped with the pandemic.

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Pre-trained Models or Feature Engineering: The Case of Dialectal Arabic
Kathrein Abu Kwaik | Stergios Chatzikyriakidis | Simon Dobnik

The usage of social media platforms has resulted in the proliferation of work on Arabic Natural Language Processing (ANLP), including the development of resources. There is also an increased interest in processing Arabic dialects and a number of models and algorithms have been utilised for the purpose of Dialectal Arabic Natural Language Processing (DANLP). In this paper, we conduct a comparison study between some of the most well-known and most commonly used methods in NLP in order to test their performance on different corpora and two NLP tasks: Dialect Identification and Sentiment Analysis. In particular, we compare three general classes of models: a) traditional Machine Learning models with features, b) classic Deep Learning architectures (LSTMs) with pre-trained word embeddings and lastly c) different Bidirectional Encoder Representations from Transformers (BERT) models such as (Multilingual-BERT, Ara-BERT, and Twitter-Arabic-BERT). The results of the comparison show that using feature-based classification can still compete with BERT models in these dialectal Arabic contexts. The use of transformer models have the ability to outperform traditional Machine Learning approaches, depending on the type of text they have been trained on, in contrast to classic Deep Learning models like LSTMs which do not perform well on the tasks

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A Context-free Arabic Emoji Sentiment Lexicon (CF-Arab-ESL)
Shatha Ali A. Hakami | Robert Hendley | Phillip Smith

Emoji can be valuable features in textual sentiment analysis. One of the key elements of the use of emoji in sentiment analysis is the emoji sentiment lexicon. However, constructing such a lexicon is a challenging task. This is because interpreting the sentiment conveyed by these pictographic symbols is highly subjective, and differs depending upon how each person perceives them. Cultural background is considered to be one of the main factors that affects emoji sentiment interpretation. Thus, we focus in this work on targeting people from Arab cultures. This is done by constructing a context-free Arabic emoji sentiment lexicon annotated by native Arabic speakers from seven different regions (Gulf, Egypt, Levant, Sudan, North Africa, Iraq, and Yemen) to see how these Arabic users label the sentiment of these symbols without a textual context. We recruited 53 annotators (males and females) to annotate 1,069 unique emoji. Then we evaluated the reliability of the annotation for each participant by applying sensitivity (Recall) and consistency (Krippendorff’s Alpha) tests. For the analysis, we investigated the resulting emoji sentiment annotations to explore the impact of the Arabic cultural context. We analyzed this cultural reflection from different perspectives, including national affiliation, use of colour indications, animal indications, weather indications and religious impact.

In sentiment analysis, detecting irony is considered a major challenge. The key problem with detecting irony is the difficulty to recognize the implicit and indirect phrases which signifies the opposite meaning. In this paper, we present Sa‘7r ساخرthe Saudi irony dataset, and describe our efforts in constructing it. The dataset was collected using Twitter API and it consists of 19,810 tweets, 8,089 of them are labeled as ironic tweets. We trained several models for irony detection task using machine learning models and deep learning models. The machine learning models include: K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Naïve Bayes (NB). While the deep learning models include BiLSTM and AraBERT. The detection results show that among the tested machine learning models, the SVM outperformed other classifiers with an accuracy of 0.68. On the other hand, the deep learning models achieved an accuracy of 0.66 in the BiLSTM model and 0.71 in the AraBERT model. Thus, the AraBERT model achieved the most accurate result in detecting irony phrases in Saudi Dialect.

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Classifying Arabic Crisis Tweets using Data Selection and Pre-trained Language Models
Alaa Alharbi | Mark Lee

User-generated Social Media (SM) content has been explored as a valuable and accessible source of data about crises to enhance situational awareness and support humanitarian response efforts. However, the timely extraction of crisis-related SM messages is challenging as it involves processing large quantities of noisy data in real-time. Supervised machine learning methods have been successfully applied to this task but such approaches require human-labelled data, which are unlikely to be available from novel and emerging crises. Supervised machine learning algorithms trained on labelled data from past events did not usually perform well when classifying a new disaster due to data variations across events. Using the BERT embeddings, we propose and investigate an instance distance-based data selection approach for adaptation to improve classifiers’ performance under a domain shift. The K-nearest neighbours algorithm selects a subset of multi-event training data that is most similar to the target event. Results show that fine-tuning a BERT model on a selected subset of data to classify crisis tweets outperforms a model that has been fine-tuned on all available source data. We demonstrated that our approach generally works better than the self-training adaptation method. Combing the self-training with our proposed classifier does not enhance the performance.

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Qur’an QA 2022: Overview of The First Shared Task on Question Answering over the Holy Qur’an
Rana Malhas | Watheq Mansour | Tamer Elsayed

Motivated by the resurgence of the machine reading comprehension (MRC) research, we have organized the first Qur’an Question Answering shared task, “Qur’an QA 2022”. The task in its first year aims to promote state-of-the-art research on Arabic QA in general and MRC in particular on the Holy Qur’an, which constitutes a rich and fertile source of knowledge for Muslim and non-Muslim inquisitors and knowledge-seekers. In this paper, we provide an overview of the shared task that succeeded in attracting 13 teams to participate in the final phase, with a total of 30 submitted runs. Moreover, we outline the main approaches adopted by the participating teams in the context of highlighting some of our perceptions and general trends that characterize the participating systems and their submitted runs.

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DTW at Qur’an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain
Damith Premasiri | Tharindu Ranasinghe | Wajdi Zaghouani | Ruslan Mitkov

The task of machine reading comprehension (MRC) is a useful benchmark to evaluate the natural language understanding of machines. It has gained popularity in the natural language processing (NLP) field mainly due to the large number of datasets released for many languages. However, the research in MRC has been understudied in several domains, including religious texts. The goal of the Qur’an QA 2022 shared task is to fill this gap by producing state-of-the-art question answering and reading comprehension research on Qur’an. This paper describes the DTW entry to the Quran QA 2022 shared task. Our methodology uses transfer learning to take advantage of available Arabic MRC data. We further improve the results using various ensemble learning strategies. Our approach provided a partial Reciprocal Rank (pRR) score of 0.49 on the test set, proving its strong performance on the task.

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eRock at Qur’an QA 2022: Contemporary Deep Neural Networks for Qur’an based Reading Comprehension Question Answers
Esha Aftab | Muhammad Kamran Malik

Question Answering (QA) has enticed the interest of NLP community in recent years. NLP enthusiasts are engineering new Models and fine-tuning the existing ones that can give out answers for the posed questions. The deep neural network models are found to perform exceptionally on QA tasks, but these models are also data intensive. For instance, BERT has outperformed many of its contemporary contenders on SQuAD dataset. In this work, we attempt at solving the closed domain reading comprehension Question Answering task on QRCD (Qur’anic Reading Comprehension Dataset) to extract an answer span from the provided passage, using BERT as a baseline model. We improved the model’s output by applying regularization techniques like weight-decay and data augmentation. Using different strategies we had 0.59% and 0.31% partial Reciprocal Ranking (pRR) on development and testing data splits respectively.

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GOF at Qur’an QA 2022: Towards an Efficient Question Answering For The Holy Qu’ran In The Arabic Language Using Deep Learning-Based Approach
Ali Mostafa | Omar Mohamed

Recently, significant advancements were achieved in Question Answering (QA) systems in several languages. However, QA systems in the Arabic language require further research and improvement because of several challenges and limitations, such as a lack of resources. Especially for QA systems in the Holy Qur’an since it is in classical Arabic and most recent publications are in Modern Standard Arabic. In this research, we report our submission to the Qur’an QA 2022 Shared task-organized with the 5th Workshop on Open-Source Arabic Corpora and Processing Tools Arabic (OSACT5). We propose a method for dealing with QA issues in the Holy Qur’an using Deep Learning models. Furthermore, we address the issue of the proposed dataset’s limited sample size by fine-tuning the model several times on several large datasets before fine-tuning it on the proposed dataset achieving 66.9% pRR 54.59% pRR on the development and test sets, respectively.

Question Answering (QA) is one of the main foсuses of Natural Language Proсessing (NLP) researсh. However, Arabiс Question Answering is still not within reaсh. The сhallenges of the Arabiс language and the laсk of resourсes have made it diffiсult to provide powerful Arabiс QA systems with high aссuraсy. While low aссuraсy may be aссepted for general purpose systems, it is сritiсal in some fields suсh as religious affairs. Therefore, there is a need for speсialized aссurate systems that target these сritiсal fields. In this paper, we propose a Transformer-based QA system using the mT5 Language Model (LM). We finetuned the model on the Qur’aniс Reading Сomprehension Dataset (QRСD) whiсh was provided in the сontext of the Qur’an QA 2022 shared task. The QRСD dataset сonsists of question-passage pairs as input, and the сorresponding adequate answers provided by expert annotators as output. Evaluation results on the same DataSet show that our best model сan aсhieve 0.98 (F1 Sсore) on the Dev Set and 0.40 on the Test Set. We disсuss those results and сhallenges, then propose potential solutions for possible improvements. The sourсe сode is available on our repository.

Question answering is a specialized area in the field of NLP that aims to extract the answer to a user question from a given text. Most studies in this area focus on the English language, while other languages, such as Arabic, are still in their early stage. Recently, research tend to develop question answering systems for Arabic Islamic texts, which may impose challenges due to Classical Arabic. In this paper, we use Simple Transformers Question Answering model with three Arabic pre-trained language models (AraBERT, CAMeL-BERT, ArabicBERT) for Qur’an Question Answering task using Qur’anic Reading Comprehension Dataset. The model is set to return five answers ranking from the best to worst based on their probability scores according to the task details. Our experiments with development set shows that AraBERT V0.2 model outperformed the other Arabic pre-trained models. Therefore, AraBERT V0.2 was chosen for the the test set and it performed fair results with 0.45 pRR score, 0.16 EM score and 0.42 F1 score.

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niksss at Qur’an QA 2022: A Heavily Optimized BERT Based Model for Answering Questions from the Holy Qu’ran
Nikhil Singh

This paper presents the system description by team niksss for the Qur’an QA 2022 Shared Task. The goal of this shared task was to evaluate systems for Arabic Reading Comprehension over the Holy Quran. The task was set up as a question-answering task, such that, given a passage from the Holy Quran (consisting of consecutive verses in a specific surah(Chapter)) and a question (posed in Modern Standard Arabic (MSA)) over that passage, the system is required to extract a span of text from that passage as an answer to the question. The span was required to be an exact sub-string of the passage. We attempted to solve this task using three techniques namely conditional text-to-text generation, embedding clustering, and transformers-based question answering.

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QQATeam at Qur’an QA 2022: Fine-Tunning Arabic QA Models for Qur’an QA Task
Basem Ahmed | Motaz Saad | Eshrag A. Refaee

The problem of auto-extraction of reliable answers from a reference text like a constitution or holy book is a real challenge for the natural languages research community. Qurán is the holy book of Islam and the primary source of legislation for millions of Muslims around the world, which can trigger the curiosity of non-Muslims to find answers about various topics from the Qurán. Previous work on Question Answering (Q&A) from Qurán is scarce and lacks the benchmark of previously developed systems on a testbed to allow meaningful comparison and identify developments and challenges. This work presents an empirical investigation of our participation in the Qurán QA shared task (2022) that utilizes a benchmark dataset of 1,093 tuples of question-Qurán passage pairs. The dataset comprises Qurán verses, questions and several ranked possible answers. This paper describes the approach we follow with our participation in the shared task and summarises our main findings. Our system attained the best score at 0.63 pRR and 0.59 F1 on the development set and 0.56 pRR and 0.51 F1 on the test set. The best results of the Exact Match (EM) score at 0.34 indicate the difficulty of the task and the need for more future work to tackle this challenging task.

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SMASH at Qur’an QA 2022: Creating Better Faithful Data Splits for Low-resourced Question Answering Scenarios
Amr Keleg | Walid Magdy

The Qur’an QA 2022 shared task aims at assessing the possibility of building systems that can extract answers to religious questions given relevant passages from the Holy Qur’an. This paper describes SMASH’s system that was used to participate in this shared task. Our experiments reveal a data leakage issue among the different splits of the dataset. This leakage problem hinders the reliability of using the models’ performance on the development dataset as a proxy for the ability of the models to generalize to new unseen samples. After creating better faithful splits from the original dataset, the basic strategy of fine-tuning a language model pretrained on classical Arabic text yielded the best performance on the new evaluation split. The results achieved by the model suggests that the small scale dataset is not enough to fine-tune large transformer-based language models in a way that generalizes well. Conversely, we believe that further attention could be paid to the type of questions that are being used to train the models given the sensitivity of the data.

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Stars at Qur’an QA 2022: Building Automatic Extractive Question Answering Systems for the Holy Qur’an with Transformer Models and Releasing a New Dataset
Ahmed Sleem | Eman Mohammed lotfy Elrefai | Marwa Mohammed Matar | Haq Nawaz

The Holy Qur’an is the most sacred book for more than 1.9 billion Muslims worldwide, and it provides a guide for their behaviours and daily interactions. Its miraculous eloquence and the divine essence of its verses (Khorami, 2014)(Elhindi,2017) make it far more difficult for non-scholars to answer their questions from the Qur’an. Here comes the significant role of technology in assisting all Muslims in answering their Qur’anic questions with state-of-the-art advancements in natural language processing (NLP) and information retrieval (IR). The task of constructing the finest automatic extractive Question Answering system from the Holy Qur’an with the use of the recently available Qur’anic Reading Comprehension Dataset(QRCD) was announced for LREC 2022 (Malhas et al., 2022) which opened up this new area for researchers around the world. In this paper, we propose a novel Qur’an Question Answering dataset with over 700 samples to aid future Qur’an research projects and three different approaches where we utilised self-attention based deep learning models (transformers) for building reliable intelligent question-answering systems for the Holy Qur’an that achieved a partial Reciprocal Rank (pRR) best score of 52% on the released QRCD test se

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TCE at Qur’an QA 2022: Arabic Language Question Answering Over Holy Qur’an Using a Post-Processed Ensemble of BERT-based Models
Mohamemd Elkomy | Amany M. Sarhan

In recent years, we witnessed great progress in different tasks of natural language understanding using machine learning. Question answering is one of these tasks which is used by search engines and social media platforms for improved user experience. Arabic is the language of the Holy Qur’an; the sacred text for 1.8 billion people across the world. Arabic is a challenging language for Natural Language Processing (NLP) due to its complex structures. In this article, we describe our attempts at OSACT5 Qur’an QA 2022 Shared Task, which is a question answering challenge on the Holy Qur’an in Arabic. We propose an ensemble learning model based on Arabic variants of BERT models. In addition, we perform post-processing to enhance the model predictions. Our system achieves a Partial Reciprocal Rank (pRR) score of 56.6% on the official test set.

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Overview of OSACT5 Shared Task on Arabic Offensive Language and Hate Speech Detection
Hamdy Mubarak | Hend Al-Khalifa | Abdulmohsen Al-Thubaity

This paper provides an overview of the shard task on detecting offensive language, hate speech, and fine-grained hate speech at the fifth workshop on Open-Source Arabic Corpora and Processing Tools (OSACT5). The shared task comprised of three subtasks; Subtask A, involving the detection of offensive language, which contains socially unacceptable or impolite content including any kind of explicit or implicit insults or attacks against individuals or groups; Subtask B, involving the detection of hate speech, which contains offensive language targeting individuals or groups based on common characteristics such as race, religion, gender, etc.; and Subtask C, involving the detection of the fine-grained type of hate speech which takes one value from the following types: (i) race/ethnicity/nationality, (ii) religion/belief, (iii) ideology, (iv) disability/disease, (v) social class, and (vi) gender. In total, 40 teams signed up to participate in Subtask A, and 17 of them submitted test runs. For Subtask B, 26 teams signed up to participate and 12 of them submitted runs. And for Subtask C, 23 teams signed up to participate and 10 of them submitted runs. 10 teams submitted papers describing their participation in one subtask or more, and 8 papers were accepted. We present and analyze all submissions in this paper.

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GOF at Arabic Hate Speech 2022: Breaking The Loss Function Convention For Data-Imbalanced Arabic Offensive Text Detection
Ali Mostafa | Omar Mohamed | Ali Ashraf

With the rise of social media platforms, we need to ensure that all users have a secure online experience by eliminating and identifying offensive language and hate speech. Furthermore, detecting such content is challenging, particularly in the Arabic language, due to a number of challenges and limitations. In general, one of the most challenging issues in real-world datasets is long-tailed data distribution. We report our submission to the Offensive Language and hate-speech Detection shared task organized with the 5th Workshop on Open-Source Arabic Corpora and Processing Tools Arabic (OSACT5); in our approach, we focused on how to overcome such a problem by experimenting with alternative loss functions rather than using the traditional weighted cross-entropy loss. Finally, we evaluated various pre-trained deep learning models using the suggested loss functions to determine the optimal model. On the development and test sets, our final model achieved 86.97% and 85.17%, respectively.

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iCompass at Arabic Hate Speech 2022: Detect Hate Speech Using QRNN and Transformers
Mohamed Aziz Bennessir | Malek Rhouma | Hatem Haddad | Chayma Fourati

This paper provides a detailed overview of the system we submitted as part of the OSACT2022 Shared Tasks on Fine-Grained Hate Speech Detection on Arabic Twitter, its outcome, and limitations. Our submission is accomplished with a hard parameter sharing Multi-Task Model that consisted of a shared layer containing state-of-the-art contextualized text representation models such as MarBERT, AraBERT, ArBERT and task specific layers that were fine-tuned with Quasi-recurrent neural networks (QRNN) for each down-stream subtask. The results show that MARBERT fine-tuned with QRNN outperforms all of the previously mentioned models.

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UPV at the Arabic Hate Speech 2022 Shared Task: Offensive Language and Hate Speech Detection using Transformers and Ensemble Models
Angel Felipe Magnossão de Paula | Paolo Rosso | Imene Bensalem | Wajdi Zaghouani

This paper describes our participation in the shared task Fine-Grained Hate Speech Detection on Arabic Twitter at the 5th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT). The shared task is divided into three detection subtasks: (i) Detect whether a tweet is offensive or not; (ii) Detect whether a tweet contains hate speech or not; and (iii) Detect the fine-grained type of hate speech (race, religion, ideology, disability, social class, and gender). It is an effort toward the goal of mitigating the spread of offensive language and hate speech in Arabic-written content on social media platforms. To solve the three subtasks, we employed six different transformer versions: AraBert, AraElectra, Albert-Arabic, AraGPT2, mBert, and XLM-Roberta. We experimented with models based on encoder and decoder blocks and models exclusively trained on Arabic and also on several languages. Likewise, we applied two ensemble methods: Majority vote and Highest sum. Our approach outperformed the official baseline in all the subtasks, not only considering F1-macro results but also accuracy, recall, and precision. The results suggest that the Highest sum is an excellent approach to encompassing transformer output to create an ensemble since this method offered at least top-two F1-macro values across all the experiments performed on development and test data.

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Meta AI at Arabic Hate Speech 2022: MultiTask Learning with Self-Correction for Hate Speech Classification
Badr AlKhamissi | Mona Diab

In this paper, we tackle the Arabic Fine-Grained Hate Speech Detection shared task and demonstrate significant improvements over reported baselines for its three subtasks. The tasks are to predict if a tweet contains (1) Offensive language; and whether it is considered (2) Hate Speech or not and if so, then predict the (3) Fine-Grained Hate Speech label from one of six categories. Our final solution is an ensemble of models that employs multitask learning and a self-consistency correction method yielding 82.7% on the hate speech subtask—reflecting a 3.4% relative improvement compared to previous work.

Hate speech and offensive language have become a crucial problem nowadays due to the extensive usage of social media by people of different gender, nationality, religion and other types of characteristics allowing anyone to share their thoughts and opinions. In this research paper, We proposed a hybrid model for the first and second tasks of OSACT2022. This model used the Arabic pre-trained Bert language model MARBERT for feature extraction of the Arabic tweets in the dataset provided by the OSACT2022 shared task, then fed the features to two classic machine learning classifiers (Logistic Regression, Random Forest). The best results achieved for the offensive tweet detection task were by the Logistic Regression model with accuracy, precision, recall, and f1-score of 80%, 78%, 78%, and 78%, respectively. The results for the hate speech tweet detection task were 89%, 72%, 80%, and 76%.

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AlexU-AIC at Arabic Hate Speech 2022: Contrast to Classify
Ahmad Shapiro | Ayman Khalafallah | Marwan Torki

Online presence on social media platforms such as Facebook and Twitter has become a daily habit for internet users. Despite the vast amount of services the platforms offer for their users, users suffer from cyber-bullying, which further leads to mental abuse and may escalate to cause physical harm to individuals or targeted groups. In this paper, we present our submission to the Arabic Hate Speech 2022 Shared Task Workshop (OSACT5 2022) using the associated Arabic Twitter dataset. The Shared Task consists of 3 Sub-tasks, Sub-task A focuses on detecting whether the tweet is Offensive or not. Then, For offensive Tweets, Sub-task B focuses on detecting whether the tweet is Hate Speech or not. Finally, For Hate Speech Tweets, Sub-task C focuses on detecting the fine-grained type of hate speech among six different classes. Transformer models proved their efficiency in classification tasks, but with the problem of over-fitting when fine-tuned on a small or an imbalanced dataset. We overcome this limitation by investigating multiple training paradigms such as Contrastive learning and Multi-task learning along with classification fine-tuning and an ensemble of our top 5 performers. Our proposed solution achieved 0.841, 0.817, and 0.476 macro F1-average in sub-tasks A, B, and C respectively.

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GUCT at Arabic Hate Speech 2022: Towards a Better Isotropy for Hatespeech Detection
Nehal Elkaref | Mervat Abu-Elkheir

Hate Speech is an increasingly common occurrence in verbal and textual exchanges on online platforms, where many users, especially those from vulnerable minorities, are in danger of being attacked or harassed via text messages, posts, comments, or articles. Therefore, it is crucial to detect and filter out hate speech in the various forms of text encountered on online and social platforms. In this paper, we present our work on the shared task of detecting hate speech in dialectical Arabic tweets as part of the OSACT shared task on Fine-grained Hate Speech Detection. Normally, tweets have a short length, and hence do not have sufficient context for language models, which in turn makes a classification task challenging. To contribute to sub-task A, we leverage MARBERT’s pre-trained contextual word representations and aim to improve their semantic quality using a cluster-based approach. Our work explores MARBERT’s embedding space and assess its geometric properties in-order to achieve better representations and subsequently better classification performance. We propose to improve the isotropic word representations of MARBERT via clustering. we compare the word representations generated by our approach to MARBERT’s default word representations via feeding each to a bidirectional LSTM to detect offensive and non-offensive tweets. Our results show that enhancing the isotropy of an embedding space can boost performance. Our system scores 81.2% on accuracy and a macro-averaged F1 score of 79.1% on sub-task A’s development set and achieves 76.5% for accuracy and an F1 score of 74.2% on the test set.

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aiXplain at Arabic Hate Speech 2022: An Ensemble Based Approach to Detecting Offensive Tweets
Salaheddin Alzubi | Thiago Castro Ferreira | Lucas Pavanelli | Mohamed Al-Badrashiny

Abusive speech on online platforms has a detrimental effect on users’ mental health. This warrants the need for innovative solutions that automatically moderate content, especially on online platforms such as Twitter where a user’s anonymity is loosely controlled. This paper outlines aiXplain Inc.’s ensemble based approach to detecting offensive speech in the Arabic language based on OSACT5’s shared sub-task A. Additionally, this paper highlights multiple challenges that may hinder progress on detecting abusive speech and provides potential avenues and techniques that may lead to significant progress.

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bib (full) Proceedings of the Workshop ParlaCLARIN III within the 13th Language Resources and Evaluation Conference

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Proceedings of the Workshop ParlaCLARIN III within the 13th Language Resources and Evaluation Conference
Darja Fišer | Maria Eskevich | Jakob Lenardič | Franciska de Jong

In ParlaMint I, a CLARIN-ERIC supported project in pandemic times, a set of comparable and uniformly annotated multilingual corpora for 17 national parliaments were developed and released in 2021. For 2022 and 2023, the project has been extended to ParlaMint II, again with the CLARIN ERIC financial support, in order to enhance the existing corpora with new data and metadata; upgrade the XML schema; add corpora for 10 new parliaments; provide more application scenarios and carry out additional experiments. The paper reports on these planned steps, including some that have already been taken, and outlines future plans.

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How GermaParl Evolves: Improving Data Quality by Reproducible Corpus Preparation and User Involvement
Andreas Blaette | Julia Rakers | Christoph Leonhardt

The development and curation of large-scale corpora of plenary debates requires not only care and attention to detail when the data is created but also effective means of sustainable quality control. This paper makes two contributions: Firstly, it presents an updated version of the GermaParl corpus of parliamentary debates in the German *Bundestag*. Secondly, it shows how the corpus preparation pipeline is designed to serve the quality of the resource by facilitating effective community involvement. Centered around a workflow which combines reproducibility, transparency and version control, the pipeline allows for continuous improvements to the corpus.

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Between History and Natural Language Processing: Study, Enrichment and Online Publication of French Parliamentary Debates of the Early Third Republic (1881-1899)
Marie Puren | Aurélien Pellet | Nicolas Bourgeois | Pierre Vernus | Fanny Lebreton

We present the AGODA (Analyse sémantique et Graphes relationnels pour l’Ouverture des Débats à l’Assemblée nationale) project, which aims to create a platform for consulting and exploring digitised French parliamentary debates (1881-1940) available in the digital library of the National Library of France. This project brings together historians and NLP specialists: parliamentary debates are indeed an essential source for French history of the contemporary period, but also for linguistics. This project therefore aims to produce a corpus of texts that can be easily exploited with computational methods, and that respect the TEI standard. Ancient parliamentary debates are also an excellent case study for the development and application of tools for publishing and exploring large historical corpora. In this paper, we present the steps necessary to produce such a corpus. We detail the processing and publication chain of these documents, in particular by mentioning the problems linked to the extraction of texts from digitised images. We also introduce the first analyses that we have carried out on this corpus with “bag-of-words” techniques not too sensitive to OCR quality (namely topic modelling and word embedding).

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A French Corpus of Québec’s Parliamentary Debates
Pierre André Ménard | Desislava Aleksandrova

Parliamentary debates offer a window on political stances as well as a repository of linguistic and semantic knowledge. They provide insights and reasons for laws and regulations that impact electors in their everyday life. One such resource is the transcribed debates available online from the Assemblée Nationale du Québec (ANQ). This paper describes the effort to convert the online ANQ debates from various HTML formats into a standardized ParlaMint TEI annotated corpus and to enrich it with annotations extracted from related unstructured members and political parties list. The resulting resource includes 88 years of debates over a span of 114 years with more than 33.3 billion words. The addition of linguistic annotations is detailed as well as a quantitative analysis of part-of-speech tags and distribution of utterances across the corpus.

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Parliamentary Corpora and Research in Political Science and Political History
Luke Blaxill

This keynote reflects on some of the barriers to digitised parliamentary resources achieving greater impact as research tools in political history and political science. As well as providing a view on researchers’ priorities for resource enhancement, I also argue that one of the main challenges for historians and political scientists is simply establishing how to make best use of these datasets through asking new research questions and through understanding and embracing unfamiliar and controversial methods than enable their analysis. I suggest parliamentary resources should be designed and presented to support pioneers trying to publish in often sceptical and traditional fields.

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Error Correction Environment for the Polish Parliamentary Corpus
Maciej Ogrodniczuk | Michał Rudolf | Beata Wójtowicz | Sonia Janicka

The paper introduces the environment for detecting and correcting various kinds of errors in the Polish Parliamentary Corpus. After performing a language model-based error detection experiment which resulted in too many false positives, a simpler rule-based method was introduced and is currently used in the process of manual verification of corpus texts. The paper presents types of errors detected in the corpus, the workflow of the correction process and the tools newly implemented for this purpose. To facilitate comparison of a target corpus XML file with its usually graphical PDF source, a new mechanism for inserting PDF page markers into XML was developed and is used for displaying a single source page corresponding to a given place in the resulting XML directly in the error correction environment.

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Clustering Similar Amendments at the Italian Senate
Tommaso Agnoloni | Carlo Marchetti | Roberto Battistoni | Giuseppe Briotti

In this paper we describe an experiment for the application of text clustering techniques to dossiers of amendments to proposed legislation discussed in the Italian Senate. The aim is to assist the Senate staff in the detection of groups of amendments similar in their textual formulation in order to schedule their simultaneous voting. Experiments show that the exploitation (extraction, annotation and normalization) of domain features is crucial to improve the clustering performance in many problematic cases not properly dealt with by standard approaches. The similarity engine was implemented and integrated as an experimental feature in the internal application used for the management of amendments in the Senate Assembly and Committees. Thanks to the Open Data strategy pursued by the Senate for several years, all documents and data produced by the institution are publicly available for reuse in open formats.

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Entity Linking in the ParlaMint Corpus
Ruben van Heusden | Maarten Marx | Jaap Kamps

The ParlaMint corpus is a multilingual corpus consisting of the parliamentary debates of seventeen European countries over a span of roughly five years. The automatically annotated versions of these corpora provide us with a wealth of linguistic information, including Named Entities. In order to further increase the research opportunities that can be created with this corpus, the linking of Named Entities to a knowledge base is a crucial step. If this can be done successfully and accurately, a lot of additional information can be gathered from the entities, such as political stance and party affiliation, not only within countries but also between the parliaments of different countries. However, due to the nature of the ParlaMint dataset, this entity linking task is challenging. In this paper, we investigate the task of linking entities from ParlaMint in different languages to a knowledge base, and evaluating the performance of three entity linking methods. We will be using DBPedia spotlight, WikiData and YAGO as the entity linking tools, and evaluate them on local politicians from several countries. We discuss two problems that arise with the entity linking in the ParlaMint corpus, namely inflection, and aliasing or the existence of name variants in text. This paper provides a first baseline on entity linking performance on multiple multilingual parliamentary debates, describes the problems that occur when attempting to link entities in ParlaMint, and makes a first attempt at tackling the aforementioned problems with existing methods.

In this paper, we present a web based interactive visualization tool for lexical networks based on the utterances of Austrian Members of Parliament. The tool is designed to compare two networks in parallel and is composed of graph visualization, node-metrics comparison and time-series comparison components that are interconnected with each other.

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Emotions Running High? A Synopsis of the state of Turkish Politics through the ParlaMint Corpus
Gül M. Kurtoğlu Eskişar | Çağrı Çöltekin

We present the initial results of our quantitative study on emotions (Anger, Disgust, Fear, Happiness, Sadness and Surprise) in Turkish parliament (2011–2021). We use machine learning models to assign emotion scores to all speeches delivered in the parliament during this period, and observe any changes to them in relation to major political and social events in Turkey. We highlight a number of interesting observations, such as anger being the dominant emotion in parliamentary speeches, and the ruling party showing more stable emotions compared to the political opposition, despite its depiction as a populist party in the literature.

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Immigration in the Manifestos and Parliament Speeches of Danish Left and Right Wing Parties between 2009 and 2020
Costanza Navarretta | Dorte Haltrup Hansen | Bart Jongejan

The paper presents a study of how seven Danish left and right wing parties addressed immigration in their 2011, 2015 and 2019 manifestos and in their speeches in the Danish Parliament from 2009 to 2020. The annotated manifestos are produced by the Comparative Manifesto Project, while the parliamentary speeches annotated with policy areas (subjects) have been recently released under CLARIN-DK. In the paper, we investigate how often the seven parties addressed immigration in the manifestos and parliamentary debates, and we analyse both datasets after having applied NLP tools to them. A sentiment analysis tool was run on the manifestos and its results were compared with the manifestos’ annotations, while topic modeling was applied to the parliamentary speeches in order to outline central themes in the immigration debates. Many of the resulting topic groups are related to cultural, religious and integration aspects which were heavily debated by politicians and media when discussing immigration policy during the past decade. Our analyses also show differences and similarities between parties and indicate how the 2015 immigrant crisis is reflected in the two types of data. Finally, we discuss advantages and limitations of our quantitative and tool-based analyses.

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Parliamentary Discourse Research in Sociology: Literature Review
Jure Skubic | Darja Fišer

One of the major sociological research interests has always been the study of political discourse. This literature review gives an overview of the most prominent topics addressed and the most popular methods used by sociologists. We identify the commonalities and the differences of the approaches established in sociology with corpus-driven approaches in order to establish how parliamentary corpora and corpus-based approaches could be successfully integrated in sociological research. We also highlight how parliamentary corpora could be made even more useful for sociologists. Keywords: parliamentary discourse, sociology, parliamentary corpora

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FrameASt: A Framework for Second-level Agenda Setting in Parliamentary Debates through the Lense of Comparative Agenda Topics
Christopher Klamm | Ines Rehbein | Simone Paolo Ponzetto

This paper presents a framework for studying second-level political agenda setting in parliamentary debates, based on the selection of policy topics used by political actors to discuss a specific issue on the parliamentary agenda. For example, the COVID-19 pandemic as an agenda item can be contextualised as a health issue or as a civil rights issue, as a matter of macroeconomics or can be discussed in the context of social welfare. Our framework allows us to observe differences regarding how different parties discuss the same agenda item by emphasizing different topical aspects of the item. We apply and evaluate our framework on data from the German Bundestag and discuss the merits and limitations of our approach. In addition, we present a new annotated data set of parliamentary debates, following the coding schema of policy topics developed in the Comparative Agendas Project (CAP), and release models for topic classification in parliamentary debates.

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Comparing Formulaic Language in Human and Machine Translation: Insight from a Parliamentary Corpus
Yves Bestgen

A recent study has shown that, compared to human translations, neural machine translations contain more strongly-associated formulaic sequences made of relatively high-frequency words, but far less strongly-associated formulaic sequences made of relatively rare words. These results were obtained on the basis of translations of quality newspaper articles in which human translations can be thought to be not very literal. The present study attempts to replicate this research using a parliamentary corpus. The results confirm the observations on the news corpus, but the differences are less strong. They suggest that the use of text genres that usually result in more literal translations, such as parliamentary corpora, might be preferable when comparing human and machine translations.

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Adding the Basque Parliament Corpus to ParlaMint Project
Jon Alkorta | Mikel Iruskieta Quintian

The aim of this work is to describe the colection created with transcript of the Basque parliamentary speeches. This corpus follows the constraints of the ParlaMint project. The Basque ParlaMint corpus consists of two versions: the first version stands for what was said in the Basque Parliament, that is, the original bilingual corpus in Basque and in Spanish to analyse what and how was said, while the second is only in Basque with the original and translated passages to promote studies on the content of the parliament speeches.

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ParlaSpeech-HR - a Freely Available ASR Dataset for Croatian Bootstrapped from the ParlaMint Corpus
Nikola Ljubešić | Danijel Koržinek | Peter Rupnik | Ivo-Pavao Jazbec

This paper presents our bootstrapping efforts of producing the first large freely available Croatian automatic speech recognition (ASR) dataset, 1,816 hours in size, obtained from parliamentary transcripts and recordings from the ParlaMint corpus. The bootstrapping approach to the dataset building relies on a commercial ASR system for initial data alignment, and building a multilingual-transformer-based ASR system from the initial data for full data alignment. Experiments on the resulting dataset show that the difference between the spoken content and the parliamentary transcripts is present in ~4-5% of words, which is also the word error rate of our best-performing ASR system. Interestingly, fine-tuning transformer models on either normalized or original data does not show a difference in performance. Models pre-trained on a subset of raw speech data consisting of Slavic languages only show to perform better than those pre-trained on a wider set of languages. With our public release of data, models and code, we are paving the way forward for the preparation of the multi-modal corpus of Croatian parliamentary proceedings, as well as for the development of similar free datasets, models and corpora for other under-resourced languages.

This paper describes the process of acquisition, cleaning, interpretation, coding and linguistic annotation of a collection of parliamentary debates from the Senate of the Italian Republic covering the COVID-19 period and a former period for reference and comparison according to the CLARIN ParlaMint guidelines and prescriptions. The corpus contains 1199 sessions and 79,373 speeches, for a total of about 31 million words and was encoded according to the ParlaCLARIN TEI XML format, as well as in CoNLL-UD format. It includes extensive metadata about the speakers, the sessions, the political parties and Parliamentary groups. As required by the ParlaMint initiative, the corpus was also linguistically annotated for sentences, tokens, POS tags, lemmas and dependency syntax according to the universal dependencies guidelines. Named entity classification was also included. All linguistic annotation was performed automatically using state-of-the-art NLP technology with no manual revision. The Italian dataset is freely available as part of the larger ParlaMint 2.1 corpus deposited and archived in CLARIN repository together with all other national corpora. It is also available for direct analysis and inspection via various CLARIN services and has already been used both for research and educational purposes.

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ParlamentParla: A Speech Corpus of Catalan Parliamentary Sessions
Baybars Kulebi | Carme Armentano-Oller | Carlos Rodriguez-Penagos | Marta Villegas

Recently, various end-to-end architectures of Automatic Speech Recognition (ASR) are being showcased as an important step towards providing language technologies to all languages instead of a select few such as English. However many languages are still suffering due to the “digital gap,” lacking thousands of hours of transcribed speech data openly accessible that is necessary to train modern ASR architectures. Although Catalan already has access to various open speech corpora, these corpora lack diversity and are limited in total volume. In order to address this lack of resources for Catalan language, in this work we present ParlamentParla, a corpus of more than 600 hours of speech from Catalan Parliament sessions. This corpus has already been used in training of state-of-the-art ASR systems, and proof-of-concept text-to-speech (TTS) models. In this work we explain in detail the pipeline that allows the information publicly available on the parliamentary website to be converted to a speech corpus compatible with training of ASR and possibly TTS models.

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ParlaMint-RO: Chamber of the Eternal Future
Petru Rebeja | Mădălina Chitez | Roxana Rogobete | Andreea Dincă | Loredana Bercuci

The present paper aims to describe the collection of ParlaMint-RO corpus and to analyse several trends in parliamentary debates (plenary sessions of the Lower House) held in between 2000 and 2020). After a short description of the data collection (of existing transcripts), the workflow of data processing (text extraction, conversion, encoding, linguistic annotation), and an overview of the corpus, the paper will move on to a multi-layered linguistic analysis to validate interdisciplinary perspectives. We use computational methods and corpus linguistics approaches to scrutinize the future tense forms used by Romanian speakers, in order to create a data-supported profile of the parliamentary group strategies and planning.

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bib (full) Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences

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NewYeS: A Corpus of New Year’s Speeches with a Comparative Analysis
Anna Tramarin | Carlo Strapparava

This paper introduces the NewYeS corpus, which contains the Christmas messages and New Year’s speeches held at the end of the year by the heads of state of different European countries (namely Denmark, France, Italy, Norway, Spain and the United Kingdom). The corpus was collected via web scraping of the speech transcripts available online. A comparative analysis was conducted to examine some of the cultural differences showing through the texts, namely a frequency distribution analysis of the term “God” and the identification of the three most frequent content words per year, with a focus on years in which significant historical events happened. An analysis of positive and negative emotion scores, examined along with the frequency of religious references, was carried out for those countries whose languages are supported by LIWC, a tool for sentiment analysis. The corpus is available for further analyses, both comparative (across countries) and diachronic (over the years).

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Correlating Political Party Names in Tweets, Newspapers and Election Results
Eric Sanders | Antal van den Bosch

Twitter has been used as a textual resource to attempt to predict the outcome of elections for over a decade. A body of literature suggests that this is not consistently possible. In this paper we test the hypothesis that mentions of political parties in tweets are better correlated with the appearance of party names in newspapers than to the intention of the tweeter to vote for that party. Five Dutch national elections are used in this study. We find only a small positive, negligible difference in Pearson’s correlation coefficient as well as in the absolute error of the relation between tweets and news, and between tweets and elections. However, we find a larger correlation and a smaller absolute error between party mentions in newspapers and the outcome of the elections in four of the five elections. This suggests that newspapers are a better starting point for predicting the election outcome than tweets.

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Debating Europe: A Multilingual Multi-Target Stance Classification Dataset of Online Debates
Valentin Barriere | Alexandra Balahur | Brian Ravenet

We present a new dataset of online debates in English, annotated with stance. The dataset was scraped from the “Debating Europe” platform, where users exchange opinions over different subjects related to the European Union. The dataset is composed of 2600 comments pertaining to 18 debates related to the “European Green Deal”, in a conversational setting. After presenting the dataset and the annotated sub-part, we pre-train a model for a multilingual stance classification over the X-stance dataset before fine-tuning it over our dataset, and vice-versa. The fine-tuned models are shown to improve stance classification performance on each of the datasets, even though they have different languages, topics and targets. Subsequently, we propose to enhance the performances over “Debating Europe” with an interaction-aware model, taking advantage of the online debate structure of the platform. We also propose a semi-supervised self-training method to take advantage of the imbalanced and unlabeled data from the whole website, leading to a final improvement of accuracy by 3.4% over a Vanilla XLM-R model.

Media framing refers to highlighting certain aspect of an issue in the news to promote a particular interpretation to the audience. Supervised learning has often been used to recognize frames in news articles, requiring a known pool of frames for a particular issue, which must be identified by communication researchers through thorough manual content analysis. In this work, we devise an unsupervised learning approach to discover the frames in news articles automatically. Given a set of news articles for a given issue, e.g., gun violence, our method first extracts frame elements from these articles using related Wikipedia articles and the Wikipedia category system. It then uses a community detection approach to identify frames from these frame elements. We discuss the effectiveness of our approach by comparing the frames it generates in an unsupervised manner to the domain-expert-derived frames for the issue of gun violence, for which a supervised learning model for frame recognition exists.

The popularity of social media makes politicians use it for political advertisement. Therefore, social media is full of electoral agitation (electioneering), especially during the election campaigns. The election administration cannot track the spread and quantity of messages that count as agitation under the election code. It addresses a crucial problem, while also uncovering a niche that has not been effectively targeted so far. Hence, we present the first publicly open data set for detecting electoral agitation in the Polish language. It contains 6,112 human-annotated tweets tagged with four legally conditioned categories. We achieved a 0.66 inter-annotator agreement (Cohen’s kappa score). An additional annotator resolved the mismatches between the first two improving the consistency and complexity of the annotation process. The newly created data set was used to fine-tune a Polish Language Model called HerBERT (achieving a 68% F1 score). We also present a number of potential use cases for such data sets and models, enriching the paper with an analysis of the Polish 2020 Presidential Election on Twitter.

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Enhancing Geocoding of Adjectival Toponyms With Heuristics
Breno Dourado Sá | Ticiana Coelho da Silva | Jose Antonio Fernandes de Macedo

Unstructured text documents such as news and blogs often present references to places. Those references, called toponyms, can be used in various applications like disaster warning and touristic planning. However, obtaining the correct coordinates for toponyms, called geocoding, is not easy since it’s common for places to have the same name as other locations. The process becomes even more challenging when toponyms appear in adjectival form, as they are different from the place’s actual name. This paper addresses the geocoding task and aims to improve, through a heuristic approach, the process for adjectival toponyms. So first, a baseline geocoder is defined through experimenting with a set of heuristics. After that, the baseline is enhanced by adding a normalization step to map adjectival toponyms to their noun form at the beginning of the geocoding process. The results show improved performance for the enhanced geocoder compared to the baseline and other geocoders.

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Cause and Effect in Governmental Reports: Two Data Sets for Causality Detection in Swedish
Luise Dürlich | Sebastian Reimann | Gustav Finnveden | Joakim Nivre | Sara Stymne

Causality detection is the task of extracting information about causal relations from text. It is an important task for different types of document analysis, including political impact assessment. We present two new data sets for causality detection in Swedish. The first data set is annotated with binary relevance judgments, indicating whether a sentence contains causality information or not. In the second data set, sentence pairs are ranked for relevance with respect to a causality query, containing a specific hypothesized cause and/or effect. Both data sets are carefully curated and mainly intended for use as test data. We describe the data sets and their annotation, including detailed annotation guidelines. In addition, we present pilot experiments on cross-lingual zero-shot and few-shot causality detection, using training data from English and German.

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Does Twitter know your political views? POLiTweets dataset and semi-automatic method for political leaning discovery
Joanna Baran | Michał Kajstura | Maciej Ziolkowski | Krzysztof Rajda

Every day, the world is flooded by millions of messages and statements posted on Twitter or Facebook. Social media platforms try to protect users’ personal data, but there still is a real risk of misuse, including elections manipulation. Did you know, that only 10 posts addressing important or controversial topics for society are enough to predict one’s political affiliation with a 0.85 F1-score? To examine this phenomenon, we created a novel universal method of semi-automated political leaning discovery. It relies on a heuristical data annotation procedure, which was evaluated to achieve 0.95 agreement with human annotators (counted as an accuracy metric). We also present POLiTweets - the first publicly open Polish dataset for political affiliation discovery in a multi-party setup, consisting of over 147k tweets from almost 10k Polish-writing users annotated heuristically and almost 40k tweets from 166 users annotated manually as a test set. We used our data to study the aspects of domain shift in the context of topics and the type of content writers - ordinary citizens vs. professional politicians.

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Political Communities on Twitter: Case Study of the 2022 French Presidential Election
Hadi Abdine | Yanzhu Guo | Virgile Rennard | Michalis Vazirgiannis

With the significant increase in users on social media platforms, a new means of political campaigning has appeared. Twitter and Facebook are now notable campaigning tools during elections. Indeed, the candidates and their parties now take to the internet to interact and spread their ideas. In this paper, we aim to identify political communities formed on Twitter during the 2022 French presidential election and analyze each respective community. We create a large-scale Twitter dataset containing 1.2 million users and 62.6 million tweets that mention keywords relevant to the election. We perform community detection on a retweet graph of users and propose an in-depth analysis of the stance of each community. Finally, we attempt to detect offensive tweets and automatic bots, comparing across communities in order to gain insight into each candidate’s supporter demographics and online campaign strategy.

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What Does the Indian Parliament Discuss? An Exploratory Analysis of the Question Hour in the Lok Sabha
Suman Adhya | Debarshi Kumar Sanyal

The TCPD-IPD dataset is a collection of questions and answers discussed in the Lower House of the Parliament of India during the Question Hour between 1999 and 2019. Although it is difficult to analyze such a huge collection manually, modern text analysis tools can provide a powerful means to navigate it. In this paper, we perform an exploratory analysis of the dataset. In particular, we present insightful corpus-level statistics and perform a more detailed analysis of three subsets of the dataset. In the latter analysis, the focus is on understanding the temporal evolution of topics using a dynamic topic model. We observe that the parliamentary conversation indeed mirrors the political and socio-economic tensions of each period.

Online news consumption plays an important role in shaping the political opinions of citizens. The news is often served by recommendation algorithms, which adapt content to users’ preferences. Such algorithms can lead to political polarization as the societal effects of the recommended content and recommendation design are disregarded. We posit that biases appear, at least in part, due to a weak entanglement between natural language processing and recommender systems, both processes yet at work in the diffusion and personalization of online information. We assume that both diversity and acceptability of recommended content would benefit from such a synergy. We discuss the limitations of current approaches as well as promising leads of opinion-mining integration for the political news recommendation process.

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Creation of Polish Online News Corpus for Political Polarization Studies
Joanna Szwoch | Mateusz Staszkow | Rafal Rzepka | Kenji Araki

In this paper we describe a Polish news corpus as an attempt to create a filtered, organized and representative set of texts coming from contemporary online press articles from two major Polish TV news providers: commercial TVN24 and state-owned TVP Info. The process consists of web scraping, data cleaning and formatting. A random sample was selected from prepared data to perform a classification task. The random forest achieved the best prediction results out of all considered models. We believe that this dataset is a valuable contribution to existing Polish language corpora as online news are considered to be formal and relatively mistake-free, therefore, a reliable source of correct written language, unlike other online platforms such as blogs or social media. Furthermore, to our knowledge, such corpus from this period of time has not been created before. In the future we would like to expand this dataset with articles coming from other online news providers, repeat the classification task on a bigger scale, utilizing other algorithms. Our data analysis outcomes might be a relevant basis to improve research on a political polarization and propaganda techniques in media.

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Annotation of expressive dimensions on a multimodal French corpus of political interviews
Jules Cauzinille | Marc Evrard | Nikita Kiselov | Albert Rilliard

We present a French corpus of political interviews labeled at the utterance level according to expressive dimensions such as Arousal. This corpus consists of 7.5 hours of high-quality audio-visual recordings with transcription. At the time of this publication, 1 hour of speech was segmented into short utterances, each manually annotated in Arousal. Our segmentation approach differs from similar corpora and allows us to perform an automatic Arousal prediction baseline by building a speech-based classification model. Although this paper focuses on the acoustic expression of Arousal, it paves the way for future work on conflictual and hostile expression recognition as well as multimodal architectures.

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TransCasm: A Bilingual Corpus of Sarcastic Tweets
Desline Simon | Sheila Castilho | Pintu Lohar | Haithem Afli

Sarcasm is extensively used in User Generated Content (UGC) in order to express one’s discontent, especially through blogs, forums, or social media such as Twitter. Several works have attempted to detect and analyse sarcasm in UGC. However, the lack of freely available corpora in this field makes the task even more difficult. In this work, we present “TransCasm” corpus, a parallel corpus of sarcastic tweets translated from English into French along with their non-sarcastic representations. To build the bilingual corpus of sarcasm, we select the “SIGN” corpus, a monolingual data set of sarcastic tweets and their non-sarcastic interpretations, created by (Peled and Reichart, 2017). We propose to define linguistic guidelines for developing “TransCasm” which is the first ever bilingual corpus of sarcastic tweets. In addition, we utilise “TransCasm” for building a binary sarcasm classifier in order to identify whether a tweet is sarcastic or not. Our experiment reveals that the sarcasm classifier achieves 61% accuracy on detecting sarcasm in tweets. “TransCasm” is now freely available online and is ready to be explored for further research.

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bib (full) Proceedings of the 2nd Workshop on People in Vision, Language, and the Mind

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Proceedings of the 2nd Workshop on People in Vision, Language, and the Mind
Patrizia Paggio | Albert Gatt | Marc Tanti

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Exploring the GLIDE model for Human Action Effect Prediction
Fangjun Li | David C. Hogg | Anthony G. Cohn

We address the following action-effect prediction task. Given an image depicting an initial state of the world and an action expressed in text, predict an image depicting the state of the world following the action. The prediction should have the same scene context as the input image. We explore the use of the recently proposed GLIDE model for performing this task. GLIDE is a generative neural network that can synthesize (inpaint) masked areas of an image, conditioned on a short piece of text. Our idea is to mask-out a region of the input image where the effect of the action is expected to occur. GLIDE is then used to inpaint the masked region conditioned on the required action. In this way, the resulting image has the same background context as the input image, updated to show the effect of the action. We give qualitative results from experiments using the EPIC dataset of ego-centric videos labelled with actions.

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Do Multimodal Emotion Recognition Models Tackle Ambiguity?
Hélène Tran | Issam Falih | Xavier Goblet | Engelbert Mephu Nguifo

Most databases used for emotion recognition assign a single emotion to data samples. This does not match with the complex nature of emotions: we can feel a wide range of emotions throughout our lives with varying degrees of intensity. We may even experience multiple emotions at once. Furthermore, each person physically expresses emotions differently, which makes emotion recognition even more challenging: we call this emotional ambiguity. This paper investigates the problem as a review of ambiguity in multimodal emotion recognition models. To lay the groundwork, the main representations of emotions along with solutions for incorporating ambiguity are described, followed by a brief overview of ambiguity representation in multimodal databases. Thereafter, only models trained on a database that incorporates ambiguity have been studied in this paper. We conclude that although databases provide annotations with ambiguity, most of these models do not fully exploit them, showing that there is still room for improvement in multimodal emotion recognition systems.

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Development of a MultiModal Annotation Framework and Dataset for Deep Video Understanding
Erika Loc | Keith Curtis | George Awad | Shahzad Rajput | Ian Soboroff

In this paper we introduce our approach and methods for collecting and annotating a new dataset for deep video understanding. The proposed dataset is composed of 3 seasons (15 episodes) of the BBC Land Girls TV Series in addition to 14 Creative Common movies with total duration of 28.5 hr. The main contribution of this paper is a novel annotation framework on the movie and scene levels to support an automatic query generation process that can capture the high-level movie features (e.g. how characters and locations are related to each other) as well as fine grained scene-level features (e.g. character interactions, natural language descriptions, and sentiments). Movie-level annotations include constructing a global static knowledge graph (KG) to capture major relationships, while the scene-level annotations include constructing a sequence of knowledge graphs (KGs) to capture fine-grained features. The annotation framework supports generating multiple query types. The objective of the framework is to provide a guide to annotating long duration videos to support tasks and challenges in the video and multimedia understanding domains. These tasks and challenges can support testing automatic systems on their ability to learn and comprehend a movie or long video in terms of actors, entities, events, interactions and their relationship to each other.

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Cognitive States and Types of Nods
Taiga Mori | Kristiina Jokinen | Yasuharu Den

In this paper we will study how different types of nods are related to the cognitive states of the listener. The distinction is made between nods with movement starting upwards (up-nods) and nods with movement starting downwards (down-nods) as well as between single or repetitive nods. The data is from Japanese multiparty conversations, and the results accord with the previous findings indicating that up-nods are related to the change in the listener’s cognitive state after hearing the partner’s contribution, while down-nods convey the meaning that the listener’s cognitive state is not changed.

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Examining the Effects of Language-and-Vision Data Augmentation for Generation of Descriptions of Human Faces
Nikolai Ilinykh | Rafal Černiavski | Eva Elžbieta Sventickaitė | Viktorija Buzaitė | Simon Dobnik

We investigate how different augmentation techniques on both textual and visual representations affect the performance of the face description generation model. Specifically, we provide the model with either original images, sketches of faces, facial composites or distorted images. In addition, on the language side, we experiment with different methods to augment the original dataset with paraphrased captions, which are semantically equivalent to the original ones, but differ in terms of their form. We also examine if augmenting the dataset with descriptions from a different domain (e.g., image captions of real-world images) has an effect on the performance of the models. We train models on different combinations of visual and linguistic features and perform both (i) automatic evaluation of generated captions and (ii) examination of how useful different visual features are for the task of facial feature classification. Our results show that although original images encode the best possible representation for the task, the model trained on sketches can still perform relatively well. We also observe that augmenting the dataset with descriptions from a different domain can boost performance of the model. We conclude that face description generation systems are more susceptible to language rather than vision data augmentation. Overall, we demonstrate that face caption generation models display a strong imbalance in the utilisation of language and vision modalities, indicating a lack of proper information fusion. We also describe ethical implications of our study and argue that future work on human face description generation should create better, more representative datasets.

Current image description generation models do not transfer well to the task of describing human faces. To encourage the development of more human-focused descriptions, we developed a new data set of facial descriptions based on the CelebA image data set. We describe the properties of this data set, and present results from a face description generator trained on it, which explores the feasibility of using transfer learning from VGGFace/ResNet CNNs. Comparisons are drawn through both automated metrics and human evaluation by 76 English-speaking participants. The descriptions generated by the VGGFace-LSTM + Attention model are closest to the ground truth according to human evaluation whilst the ResNet-LSTM + Attention model obtained the highest CIDEr and CIDEr-D results (1.252 and 0.686 respectively). Together, the new data set and these experimental results provide data and baselines for future work in this area.

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bib (full) Proceedings of the RaPID Workshop - Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments - within the 13th Language Resources and Evaluation Conference

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Proceedings of the RaPID Workshop - Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments - within the 13th Language Resources and Evaluation Conference
Dimitrios Kokkinakis | Charalambos K. Themistocleous | Kristina Lundholm Fors | Athanasios Tsanas | Kathleen C. Fraser

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The Effect of eHealth Training on Dysarthric Speech
Chiara Pesenti | Loes Van Bemmel | Roeland van Hout | Helmer Strik

In the current study on dysarthric speech, we investigate the effect of web-based treatment, and whether there is a difference between content and function words. Since the goal of the treatment is to speak louder, without raising pitch, we focus on acoustic-phonetic features related to loudness, intensity, and pitch. We analyse dysarthric read speech from eight speakers at word level. We also investigate whether there are differences between content words and function words, and whether the treatment has a different impact on these two classes of words. Linear Mixed-Effects models show that there are differences before and after treatment, that for some speakers the treatment has the desired effect, but not for all speakers, and that the effect of the treatment on words for the two categories does not seem to be different. To a large extent, our results are in line with the results of a previous study in which the same data were analyzed in a different way, i.e. by studying intelligibility scores.

Training classification models on clinical speech is a time-saving and effective solution for many healthcare challenges, such as screening for Alzheimer’s Disease over the phone. One of the primary limiting factors of the success of artificial intelligence (AI) solutions is the amount of relevant data available. Clinical data is expensive to collect, not sufficient for large-scale machine learning or neural methods, and often not shareable between institutions due to data protection laws. With the increasing demand for AI in health systems, generating synthetic clinical data that maintains the nuance of underlying patient pathology is the next pressing task. Previous work has shown that automated evaluation of clinical speech tasks via automatic speech recognition (ASR) is comparable to manually annotated results in diagnostic scenarios even though ASR systems produce errors during the transcription process. In this work, we propose to generate synthetic clinical data by simulating ASR deletion errors on the transcript to produce additional data. We compare the synthetic data to the real data with traditional machine learning methods to test the feasibility of the proposed method. Using a dataset of 50 cognitively impaired and 50 control Dutch speakers, ten additional data points are synthetically generated for each subject, increasing the training size for 100 to 1000 training points. We find consistent and comparable performance of models trained on only synthetic data (AUC=0.77) to real data (AUC=0.77) in a variety of traditional machine learning scenarios. Additionally, linear models are not able to distinguish between real and synthetic data.

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A Novel Metrological Approach to a More Consistent Way of Defining and Analyzing Memory Task Difficulty in Word Learning List Tests with Repeated Trials
Jeanette Melin | Leslie Pendrill

New candidate diagnostics for cognitive decline and dementia have recently been proposed based on effects such as primacy and recency in word learning memory list tests. The diagnostic value is, however, currently limited by the multiple ways in which raw scores, and in particular these serial position effects (SPE), have been defined and analyzed to date. In this work, we build on previous analyses taking a metrological approach to the 10-item word learning list. We show i) how the variation in task difficulty reduces successively for trials 2 and 3, ii) how SPE change with repeated trials as predicted with our entropy-based theory, and iii) how possibilities to separate cohort members according to cognitive health status are limited. These findings mainly depend on the test design itself: A test with only 10 words, where SPE do not dominate over trials, requires more challenging words to increase the variation in task difficulty, and in turn to challenge the test persons. The work is novel and also contributes to the endeavour to develop for more consistent ways of defining and analyzing memory task difficulty, and in turn opens up for more practical and accurate measurement in clinical practice, research and trials.

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Extraction and Classification of Acoustic Features from Italian Speaking Children with Autism Spectrum Disorders.
Federica Beccaria | Gloria Gagliardi | Dimitrios Kokkinakis

Autism Spectrum Disorders (ASD) are a group of complex developmental conditions whose effects and severity show high intraindividual variability. However, one of the main symptoms shared along the spectrum is social interaction impairments that can be explored through acoustic analysis of speech production. In this paper, we compare 14 Italian-speaking children with ASD and 14 typically developing peers. Accordingly, we extracted and selected the acoustic features related to prosody, quality of voice, loudness, and spectral distribution using the parameter set eGeMAPS provided by the openSMILE feature extraction toolkit. We implemented four supervised machine learning methods to evaluate the extraction performances. Our findings show that Decision Trees (DTs) and Support Vector Machines (SVMs) are the best-performing methods. The overall DT models reach a 100% recall on all the trials, meaning they correctly recognise autistic features. However, half of its models overfit, while SVMs are more consistent. One of the results of the work is the creation of a speech pipeline to extract Italian speech biomarkers typical of ASD by comparing our results with studies based on other languages. A better understanding of this topic can support clinicians in diagnosing the disorder.

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Classification of German Jungian Extraversion and Introversion Texts with Assessment of Changes During the COVID-19 Pandemic
Dirk Johannßen | Chris Biemann | David Scheffer

The corona pandemic and countermeasures such as social distancing and lockdowns have confronted individuals with new challenges for their mental health and well-being. It can be assumed that the Jungian psychology types of extraverts and introverts react differently to these challenges. We propose a Bi-LSTM model with an attention mechanism for classifying introversion and extraversion from German tweets, which is trained on hand-labeled data created by 335 participants. With this work, we provide this novel dataset for free use and validation. The proposed model achieves solid performance with F1 = .72. Furthermore, we created a feature engineered logistic model tree (LMT) trained on hand-labeled tweets, to which the data is also made available with this work. With this second model, German tweets before and during the pandemic have been investigated. Extraverts display more positive emotions, whilst introverts show more insight and higher rates of anxiety. Even though such a model can not replace proper psychological diagnostics, it can help shed light on linguistic markers and to help understand introversion and extraversion better for a variety of applications and investigations.

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The Post-Stroke Speech Transcription (PSST) Challenge
Robert C. Gale | Mikala Fleegle | Gerasimos Fergadiotis | Steven Bedrick

We present the outcome of the Post-Stroke Speech Transcription (PSST) challenge. For the challenge, we prepared a new data resource of responses to two confrontation naming tests found in AphasiaBank, extracting audio and adding new phonemic transcripts for each response. The challenge consisted of two tasks. Task A asked challengers to build an automatic speech recognizer (ASR) for phonemic transcription of the PSST samples, evaluated in terms of phoneme error rate (PER) as well as a finer-grained metric derived from phonological feature theory, feature error rate (FER). The best model had a 9.9% FER / 20.0% PER, improving on our baseline by a relative 18% and 24%, respectively. Task B approximated a downstream assessment task, asking challengers to identify whether each recording contained a correctly pronounced target word. Challengers were unable to improve on the baseline algorithm; however, using this algorithm with the improved transcripts from Task A resulted in 92.8% accuracy / 0.921 F1, a relative improvement of 2.8% and 3.3%, respectively.

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Post-Stroke Speech Transcription Challenge (Task B): Correctness Detection in Anomia Diagnosis with Imperfect Transcripts
Trang Tran

Aphasia is a language disorder that affects millions of adults worldwide annually; it is most commonly caused by strokes or neurodegenerative diseases. Anomia, or word finding difficulty, is a prominent symptom of aphasia, which is often diagnosed through confrontation naming tasks. In the clinical setting, identification of correctness in responses to these naming tasks is useful for diagnosis, but currently is a labor-intensive process. This year’s Post-Stroke Speech Transcription Challenge provides an opportunity to explore ways of automating this process. In this work, we focus on Task B of the challenge, i.e. identification of response correctness. We study whether a simple aggregation of using the 1-best automatic speech recognition (ASR) output and acoustic features could help predict response correctness. This was motivated by the hypothesis that acoustic features could provide complementary information to the (imperfect) ASR transcripts. We trained several classifiers using various sets of acoustic features standard in speech processing literature in an attempt to improve over the 1-best ASR baseline. Results indicated that our approach to using the acoustic features did not beat the simple baseline, at least on this challenge dataset. This suggests that ASR robustness still plays a significant role in the correctness detection task, which has yet to benefit from acoustic features.

As part of the PSST challenge, we explore how data augmentations, data sources, and model size affect phoneme transcription accuracy on speech produced by individuals with aphasia. We evaluate model performance in terms of feature error rate (FER) and phoneme error rate (PER). We find that data augmentations techniques, such as pitch shift, improve model performance. Additionally, increasing the size of the model decreases FER and PER. Our experiments also show that adding manually-transcribed speech from non-aphasic speakers (TIMIT) improves performance when Room Impulse Response is used to augment the data. The best performing model combines aphasic and non-aphasic data and has a 21.0% PER and a 9.2% FER, a relative improvement of 9.8% compared to the baseline model on the primary outcome measurement. We show that data augmentation, larger model size, and additional non-aphasic data sources can be helpful in improving automatic phoneme recognition models for people with aphasia.

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Data Augmentation for the Post-Stroke Speech Transcription (PSST) Challenge: Sometimes Less Is More
Jiahong Yuan | Xingyu Cai | Kenneth Church

We employ the method of fine-tuning wav2vec2.0 for recognition of phonemes in aphasic speech. Our effort focuses on data augmentation, by supplementing data from both in-domain and out-of-domain datasets for training. We found that although a modest amount of out-of-domain data may be helpful, the performance of the model degrades significantly when the amount of out-of-domain data is much larger than in-domain data. Our hypothesis is that fine-tuning wav2vec2.0 with a CTC loss not only learns bottom-up acoustic properties but also top-down constraints. Therefore, out-of-domain data augmentation is likely to degrade performance if there is a language model mismatch between “in” and “out” domains. For in-domain audio without ground truth labels, we found that it is beneficial to exclude samples with less confident pseudo labels. Our final model achieves 16.7% PER (phoneme error rate) on the validation set, without using a language model for decoding. The result represents a relative error reduction of 14% over the baseline model trained without data augmentation. Finally, we found that “canonicalized” phonemes are much easier to recognize than manually transcribed phonemes.

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CorEDs: A Corpus on Eating Disorders
Melissa Donati | Carlo Strapparava

Eating disorders (EDs) constitute a widespread group of mental illnesses affecting the everyday life of many individuals in all age groups. One of the main difficulties in the diagnosis and treatment of these disorders is the interpersonal variability of symptoms and the variety of underlying psychological states that are not considered in traditional approaches. In order to gain a better understanding of these disorders, many studies have collected data from social media and analysed them from a computational perspective, but the resulting dataset were very limited and task-specific. Aiming to address this shortage by providing a dataset that could be easily adapted to different tasks, we built a corpus collecting ED-related and ED-unrelated comments from Reddit focusing on a limited number of topics (fitness, nutrition, etc.). To validate the effectiveness of the dataset, we evaluated the performance of two classifiers in distinguishing between ED-related and unrelated comments. The high-level accuracy of both classifiers indicates that ED-related texts are separable from texts on similar topics that do not address EDs. For explorative purposes, we also carried out a linguistic analysis of word class dominance in ED-related texts, whose results are consistent with the findings of psychological research on EDs.

An assistive robot that could communicate with dementia patients would have great social benefit. An assistive robot Pepper has been designed to administer Referential Communication Tasks (RCTs) to human subjects without dementia as a step towards an agent to administer RCTs to dementia patients, potentially for earlier diagnosis. Currently, Pepper follows a rigid RCT script, which affects the user experience. We aim to replace Pepper’s RCT script with a dialogue management approach, to generate more natural interactions with RCT subjects. A Partially Observable Markov Decision Process (POMDP) dialogue policy will be trained using reinforcement learning, using simulated dialogue partners. This paper describes two RCT datasets and a methodology for their use in creating a database that the simulators can access for training the POMDP policies.

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Segmentation of the Speech Flow for the Evaluation of Spontaneous Productions in Pathologies Affecting the Language Capacity. 4 Case Studies of Schizophrenia
Valentina Saccone | Simona Trillocco

This paper aims to present a multi-level analysis of spoken language, which is carried out through Praat software for the analysis of speech in its prosodic aspects. The main object of analysis is the pathological speech of schizophrenic patients with a focus on pausing and its information structure. Spoken data (audio recordings in clinical settings; 4 case studies from CIPPS corpus) has been processed to create an implementable layer grid. The grid is an incremental annotation with layers dedicated to silent/sounding detection; orthographic transcription with the annotation of different vocal phenomena; Utterance segmentation; Information Units segmentation. The theoretical framework we are dealing with is the Language into Act Theory and its pragmatic and empirical studies on spontaneous spoken language. The core of the analysis is the study of pauses (signaled in the silent/sounding tier) starting from their automatic detection, then manually validated, and their classification based on duration and position inter/intra Turn and Utterance. In this respect, an interesting point arises: beyond the expected result of longer pauses in pathological schizophrenic than non-pathological, aside from the type of pause, analysis shows that pauses after Utterances are specific to pathological speech when >500 ms.

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bib (full) Proceedings of the 2nd Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI) within the 13th Language Resources and Evaluation Conference

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Proceedings of the 2nd Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI) within the 13th Language Resources and Evaluation Conference
Rodrigo Wilkens | David Alfter | Rémi Cardon | Núria Gala

We present an overview of LARA, the Learning And Reading Assistant, an open source platform for easy creation and use of multimedia annotated texts designed to support the improvement of reading skills. The paper is divided into three parts. In the first, we give a brief summary of LARA’s processing. In the second, we describe some generic functionality specially relevant for reading assistance: support for phonetically annotated texts, support for image-based texts, and integrated production of text-to-speech (TTS) generated audio. In the third, we outline some of the larger projects so far carried out with LARA, involving development of content for learning second and foreign (L2) languages such as Icelandic, Farsi, Irish, Old Norse and the Australian Aboriginal language Barngarla, where the issues involved overlap with those that arise when trying to help students improve first-language (L1) reading skills. All software and almost all content is freely available.

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Agree to Disagree: Exploring Subjectivity in Lexical Complexity
Matthew Shardlow

Subjective factors affect our familiarity with different words. Our education, mother tongue, dialect or social group all contribute to the words we know and understand. When asking people to mark words they understand some words are unanimously agreed to be complex, whereas other annotators universally disagree on the complexity of other words. In this work, we seek to expose this phenomenon and investigate the factors affecting whether a word is likely to be subjective, or not. We investigate two recent word complexity datasets from shared tasks. We demonstrate that subjectivity is present and describable in both datasets. Further we show results of modelling and predicting the subjectivity of the complexity annotations in the most recent dataset, attaining an F1-score of 0.714.

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A Dictionary-Based Study of Word Sense Difficulty
David Alfter | Rémi Cardon | Thomas François

In this article, we present an exploratory study on perceived word sense difficulty by native and non-native speakers of French. We use a graded lexicon in conjunction with the French Wiktionary to generate tasks in bundles of four items. Annotators manually rate the difficulty of the word senses based on their usage in a sentence by selecting the easiest and the most difficult word sense out of four. Our results show that the native and non-native speakers largely agree when it comes to the difficulty of words. Further, the rankings derived from the manual annotation broadly follow the levels of the words in the graded resource, although these levels were not overtly available to annotators. Using clustering, we investigate whether there is a link between the complexity of a definition and the difficulty of the associated word sense. However, results were inconclusive. The annotated data set is available for research purposes.

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A Multilingual Simplified Language News Corpus
Renate Hauser | Jannis Vamvas | Sarah Ebling | Martin Volk

Simplified language news articles are being offered by specialized web portals in several countries. The thousands of articles that have been published over the years are a valuable resource for natural language processing, especially for efforts towards automatic text simplification. In this paper, we present SNIML, a large multilingual corpus of news in simplified language. The corpus contains 13k simplified news articles written in one of six languages: Finnish, French, Italian, Swedish, English, and German. All articles are shared under open licenses that permit academic use. The level of text simplification varies depending on the news portal. We believe that even though SNIML is not a parallel corpus, it can be useful as a complement to the more homogeneous but often smaller corpora of news in the simplified variety of one language that are currently in use.

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The Swedish Simplification Toolkit: – Designed with Target Audiences in Mind
Evelina Rennes | Marina Santini | Arne Jonsson

In this paper, we present the current version of The Swedish Simplification Toolkit. The toolkit includes computational and empirical tools that have been developed along the years to explore a still neglected area of NLP, namely the simplification of “standard” texts to meet the needs of target audiences. Target audiences, such as people affected by dyslexia, aphasia, autism, but also children and second language learners, require different types of text simplification and adaptation. For example, while individual with aphasia have difficulties in reading compounds (such as arbetsmarknadsdepartement, eng. ministry of employment), second language learners struggle with cultural-specific vocabulary (e.g. konflikträdd, eng. afraid of conflicts). The toolkit allows user to selectively decide the types of simplification that meet the specific needs of the target audience they belong to. The Swedish Simplification Toolkit is one of the first attempts to overcome the one-fits-all approach that is still dominant in Automatic Text Simplification, and proposes a set of computational methods that, used individually or in combination, may help individuals reduce reading (and writing) difficulties.

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HIBOU: an eBook to improve Text Comprehension and Reading Fluency for Beginning Readers of French
Ludivine Javourey Drevet | Stéphane Dufau | Johannes Christoph Ziegler | Núria Gala

In this paper, we present HIBOU, an eBook application initially developed for iOs, displaying adapted texts (i.e. simplified), and proposing text comprehension activities. The application has been used in six elementary schools in France to evaluate and train reading fluency and comprehension skills on beginning readers of French. HIBOU displays two versions of French literary and documentary texts from the ALECTOR corpus, the ‘original’, and a simplified version. Text simplifications have been manually performed at the lexical, syntactic, and discursive levels. The child can read in autonomy and has access to different games on word identification. HIBOU is at present being developed to be online in a platform that will be available at elementary schools in France.

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PADDLe: a Platform to Identify Complex Words for Learners of French as a Foreign Language (FFL)
Camille Pirali | Thomas François | Núria Gala

Annotations of word difficulty by readers provide invaluable insights into lexical complexity. Yet, there is currently a paucity of tools allowing researchers to gather such annotations in an adaptable and simple manner. This article presents PADDLe, an online platform aiming to fill that gap and designed to encourage best practices when collecting difficulty judgements. Studies crafted using the tool ask users to provide a selection of demographic information, then to annotate a certain number of texts and answer multiple-choice comprehension questions after each text. Researchers are encouraged to use a multi-level annotation scheme, to avoid the drawbacks of binary complexity annotations. Once a study is launched, its results are summarised in a visual representation accessible both to researchers and teachers, and can be downloaded in .csv format. Some findings of a pilot study designed with the tool are also provided in the article, to give an idea of the types of research questions it allows to answer.

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Open corpora and toolkit for assessing text readability in French
Nicolas Hernandez | Nabil Oulbaz | Tristan Faine

Measuring the linguistic complexity or assessing the readability of spoken or written productions has been the concern of several researchers in pedagogy and (foreign) language teaching for decades. Researchers study for example the children’s language development or the second language (L2) learning with tasks such as age or reader’s level recommendation, or text simplification. Despite the interest for the topic, open datasets and toolkits for processing French are scarce. Our contributions are: (1) three open corpora for supporting research on readability assessment in French, (2) a dataset analysis with traditional formulas and an unsupervised measure, (3) a toolkit dedicated for French processing which includes the implementation of statistical formulas, a pseudo-perplexity measure, and state-of-the-art classifiers based on SVM and fine-tuned BERT for predicting readability levels, and (4) an evaluation of the toolkit on the three data sets.

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MWE for Essay Scoring English as a Foreign Language
Rodrigo Wilkens | Daiane Seibert | Xiaoou Wang | Thomas François

Mastering a foreign language like English can bring better opportunities. In this context, although multiword expressions (MWE) are associated with proficiency, they are usually neglected in the works of automatic scoring language learners. Therefore, we study MWE-based features (i.e., occurrence and concreteness) in this work, aiming at assessing their relevance for automated essay scoring. To achieve this goal, we also compare MWE features with other classic features, such as length-based, graded resource, orthographic neighbors, part-of-speech, morphology, dependency relations, verb tense, language development, and coherence. Although the results indicate that classic features are more significant than MWE for automatic scoring, we observed encouraging results when looking at the MWE concreteness through the levels.

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bib (full) Proceedings of the Second International Workshop on Resources and Techniques for User Information in Abusive Language Analysis

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A First Attempt at Unreliable News Detection in Swedish
Ricardo Muñoz Sánchez | Eric Johansson | Shakila Tayefeh | Shreyash Kad

Throughout the COVID-19 pandemic, a parallel infodemic has also been going on such that the information has been spreading faster than the virus itself. During this time, every individual needs to access accurate news in order to take corresponding protective measures, regardless of their country of origin or the language they speak, as misinformation can cause significant loss to not only individuals but also society. In this paper we train several machine learning models (ranging from traditional machine learning to deep learning) to try to determine whether news articles come from either a reliable or an unreliable source, using just the body of the article. Moreover, we use a previously introduced corpus of news in Swedish related to the COVID-19 pandemic for the classification task. Given that our dataset is both unbalanced and small, we use subsampling and easy data augmentation (EDA) to try to solve these issues. In the end, we realize that, due to the small size of our dataset, using traditional machine learning along with data augmentation yields results that rival those of transformer models such as BERT.

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BanglaHateBERT: BERT for Abusive Language Detection in Bengali
Md Saroar Jahan | Mainul Haque | Nabil Arhab | Mourad Oussalah

This paper introduces BanglaHateBERT, a retrained BERT model for abusive language detection in Bengali. The model was trained with a large-scale Bengali offensive, abusive, and hateful corpus that we have collected from different sources and made available to the public. Furthermore, we have collected and manually annotated 15K Bengali hate speech balanced dataset and made it publicly available for the research community. We used existing pre-trained BanglaBERT model and retrained it with 1.5 million offensive posts. We presented the results of a detailed comparison between generic pre-trained language model and retrained with the abuse-inclined version. In all datasets, BanglaHateBERT outperformed the corresponding available BERT model.

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A Comparison of Machine Learning Techniques for Turkish Profanity Detection
Levent Soykan | Cihan Karsak | Ilknur Durgar Elkahlout | Burak Aytan

Profanity detection became an important task with the increase of social media usage. Most of the users prefer a clean and profanity free environment to communicate with others. In order to provide a such environment for the users, service providers are using various profanity detection tools. In this paper, we researched on Turkish profanity detection in our search engine. We collected and labeled a dataset from search engine queries as one of the two classes: profane and not-profane. We experimented with several classical machine learning and deep learning methods and compared methods in means of speed and accuracy. We performed our best scores with transformer based Electra model with 0.93 F1 Score. We also compared our models with the state-of-the-art Turkish profanity detection tool and observed that we outperform it from all aspects.

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Features and Categories of Hyperbole in Cyberbullying Discourse on Social Media
Simona Ignat | Carl Vogel

Cyberbullying discourse is achieved with multiple linguistic conveyances. Hyperboles witnessed in a corpus of cyberbullying utterances are studied. Linguistic features of hyperbole using the traditional grammatical indications of exaggerations are analyzed. The method relies on data selected from a larger corpus of utterances identified and labelled as “bullying”, from Twitter, from October 2020 to March 2022. An outcome is a lexicon of 250 entries. A small number of lexical level features have been isolated, and chi-squared contingency tests applied to evaluating their information value in identifying hyperbole. Words or affixes indicating superlatives or extremes of scales, with positive but not negative valency items, interact with hyperbole classification in this data set. All utterances extracted has been considered exaggerations and the stylistic status of “hyperbole” has been commented within the frame of new meanings in the context of social media.

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bib (full) Proceedings of the 2nd Workshop on Sentiment Analysis and Linguistic Linked Data

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Proceedings of the 2nd Workshop on Sentiment Analysis and Linguistic Linked Data
Ilan Kernerman | Sara Carvalho | Carlos A. Iglesias | Rachele Sprugnoli

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Invited talk: From Data to Meaning in Representation of Emotions
Anna Fensel

Historically, now we have an unprecedentedly large amount of data available in various systems, and the growth of data volumes is rapid and continuous. The numbers of scientific papers published per year are higher than ever before. While it is desirable to have the context of the users of a social system known and represented in a machine-readable form, capturing this context is notoriously complex (as social context is more difficult to measure with simple sensors, unlike some physical characteristics). This complexity applies especially to the domain of emotions, but also to other context information relevant for social systems and social sciences (for example, in case of experimental study set up in sociology or marketing, detailed user profiles, exact background and experimental settings need to be recorded in a precise manner). Which data and scientific findings get shared, for which purposes, and how? How to address open and closed data, and reproducibility crisis? How to convert Big Data into Smart Data, which is interpretable by both machine and human? And how to make sure that the resulting Smart Data is trustworthy and appropriately handling biases? In my talk, I discuss these questions from the technical perspective, and give examples for relevant solutions implemented with Semantic Web technology, linked data, knowledge graphs and FAIR (Findable, Accessible, Interoperable, Reusable) data management. Specifically, I will be discussing experiences with combining machine learning and knowledge graphs for semantic representation of emotions. Further, I will talk about research data infrastructures and tools for social sciences that can facilitate semantic interoperability and bring more meaning with sharing semantic representation of context, such as one about emotions. Such semantic representations and infrastructures can serve as a basis for industrial applications, including recommender systems, personal assistants and chatbots, and also serve to improve research data management in social sciences.

Inside the NLP community there is a considerable amount of language resources created, annotated and released every day with the aim of studying specific linguistic phenomena. Despite a variety of attempts in order to organize such resources has been carried on, a lack of systematic methods and of possible interoperability between resources are still present. Furthermore, when storing linguistic information, still nowadays, the most common practice is the concept of “gold standard”, which is in contrast with recent trends in NLP that aim at stressing the importance of different subjectivities and points of view when training machine learning and deep learning methods. In this paper we present O-Dang!: The Ontology of Dangerous Speech Messages, a systematic and interoperable Knowledge Graph (KG) for the collection of linguistic annotated data. O-Dang! is designed to gather and organize Italian datasets into a structured KG, according to the principles shared within the Linguistic Linked Open Data community. The ontology has also been designed to account a perspectivist approach, since it provides a model for encoding both gold standard and single-annotator labels in the KG. The paper is structured as follows. In Section 1 the motivations of our work are outlined. Section 2 describes the O-Dang! Ontology, that provides a common semantic model for the integration of datasets in the KG. The Ontology Population stage with information about corpora, users, and annotations is presented in Section 3. Finally, in Section 4 an analysis of offensiveness across corpora is provided as a first case study for the resource.

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Movie Rating Prediction using Sentiment Features
João Ramos | Diogo Apóstolo | Hugo Gonçalo Oliveira

We analyze the impact of using sentiment features in the prediction of movie review scores. The effort included the creation of a new lexicon, Expanded OntoSenticNet (EON), by merging OntoSenticNet and SentiWordNet, and experiments were made on the “IMDB movie review” dataset, with the three main approaches for sentiment analysis: lexicon-based, supervised machine learning and hybrids of the previous. Hybrid approaches performed the best, demonstrating the potential of merging knowledge bases and machine learning, but supervised approaches based on review embeddings were not far.

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Evaluating a New Danish Sentiment Resource: the Danish Sentiment Lexicon, DSL
Nina Schneidermann | Bolette Pedersen

In this paper, we evaluate a new sentiment lexicon for Danish, the Danish Sentiment Lexicon (DSL), to gain input regarding how to carry out the final adjustments of the lexicon. A feature of the lexicon that differentiates it from other sentiment resources for Danish is that it is linked to a large number of other Danish lexical resources via the DDO lemma and sense inventory and the LLOD via the Danish wordnet, DanNet. We perform our evaluation on four datasets labeled with sentiments. In addition, we compare the lexicon against two existing benchmarks for Danish: the Afinn and the Sentida resources. We observe that DSL performs mostly comparably to the existing resources, but that more fine-grained explorations need to be done in order to fully exploit its possibilities given its linking properties.

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Correlating Facts and Social Media Trends on Environmental Quantities Leveraging Commonsense Reasoning and Human Sentiments
Brad McNamee | Aparna Varde | Simon Razniewski

As climate change alters the physical world we inhabit, opinions surrounding this hot-button issue continue to fluctuate. This is apparent on social media, particularly Twitter. In this paper, we explore concrete climate change data concerning the Air Quality Index (AQI), and its relationship to tweets. We incorporate commonsense connotations for appeal to the masses. Earlier work focuses primarily on accuracy and performance of sentiment analysis tools / models, much geared towards experts. We present commonsense interpretations of results, such that they are not impervious to the masses. Moreover, our study uses real data on multiple environmental quantities comprising AQI. We address human sentiments gathered from linked data on hashtagged tweets with geolocations. Tweets are analyzed using VADER, subtly entailing commonsense reasoning. Interestingly, correlations between climate change tweets and air quality data vary not only based upon the year, but also the specific environmental quantity. It is hoped that this study will shed light on possible areas to increase awareness of climate change, and methods to address it, by the scientists as well as the common public. In line with Linked Data initiatives, we aim to make this work openly accessible on a network, published with the Creative Commons license.

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Sentiment Analysis of Serbian Old Novels
Ranka Stanković | Miloš Košprdić | Milica Ikonić Nešić | Tijana Radović

In this paper we present first study of Sentiment Analysis (SA) of Serbian novels from the 1840-1920 period. The preparation of sentiment lexicon was based on three existing lexicons: NRC, AFFIN and Bing with additional extensive corrections. The first phase of dataset refinement included filtering the word that are not found in Serbian morphological dictionary and in second automatic POS tagging and lemma were manually corrected. The polarity lexicon was extracted and transformed into ontolex-lemon and published as initial version. The complex inflection system of Serbian language required expansion of sentiment lexicon with inflected forms from Serbian morphological dictionaries. Set of sentences for SA was extracted from 120 novels of Serbian part of ELTeC collection, labelled for polarity and used for several model training. Several approaches for SA are compared, starting with for variation of lexicon based and followed by Logistic Regression, Naive Bayes, Decision Tree, Random Forest, SVN and k-NN. The comparison with models trained on labelled movie reviews dataset indicates that it can not successfully be used for sentiment analysis of sentences in old novels.

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bib (full) Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

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PeruSIL: A Framework to Build a Continuous Peruvian Sign Language Interpretation Dataset
Gissella Bejarano | Joe Huamani-Malca | Francisco Cerna-Herrera | Fernando Alva-Manchego | Pablo Rivas

Video-based datasets for Continuous Sign Language are scarce due to the challenging task of recording videos from native signers and the reduced number of people who can annotate sign language. COVID-19 has evidenced the key role of sign language interpreters in delivering nationwide health messages to deaf communities. In this paper, we present a framework for creating a multi-modal sign language interpretation dataset based on videos and we use it to create the first dataset for Peruvian Sign Language (LSP) interpretation annotated by hearing volunteers who have intermediate knowledge of PSL guided by the video audio. We rely on hearing people to produce a first version of the annotations, which should be reviewed by native signers in the future. Our contributions: i) we design a framework to annotate a sign Language dataset; ii) we release the first annotated LSP multi-modal interpretation dataset (AEC); iii) we evaluate the annotation done by hearing people by training a sign language recognition model. Our model reaches up to 80.3% of accuracy among a minimum of five classes (signs) AEC dataset, and 52.4% in a second dataset. Nevertheless, analysis by subject in the second dataset show variations worth to discuss.

Wordnets have been a popular lexical resource type for many years. Their sense-based representation of lexical items and numerous relation structures have been used for a variety of computational and linguistic applications. The inclusion of different wordnets into multilingual wordnet networks has further extended their use into the realm of cross-lingual research. Wordnets have been released for many spoken languages. Research has also been carried out into the creation of wordnets for several sign languages, but none have yet resulted in publicly available datasets. This article presents our own efforts towards an inclusion of sign languages in a multilingual wordnet, starting with Greek Sign Language (GSL) and German Sign Language (DGS). Based on differences in available language resources between GSL and DGS, we trial two workflows with different coverage priorities. We also explore how synergies between both workflows can be leveraged and how future work on additional sign languages could profit from building on existing sign language wordnet data. The results of our work are made publicly available.

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Introducing the signglossR Package
Carl Börstell

The signglossR package is a library written in the programming language R, intended as an easy-to-use resource for those who work with signed language data and are familiar with R. The package contains a variety of functions designed specifically towards signed language research, facilitating a single-pipeline workflow with R when accessing public language resources remotely (online) or a user’s own files and data. The package specifically targets processing of image and video files, but also features some interaction with software commonly used by researchers working on signed language and gesture, such as ELAN and OpenPose. The signglossR package combines features and functionality from many other libraries and tools in order to simplify and collect existing resources in one place, as well as adding some new functionality, and adapt everything to the needs of researchers working with visual language data. In this paper, the main features of this package are introduced.

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Moving towards a Functional Approach in the Flemish Sign Language Dictionary Making Process
Caro Brosens | Margot Janssens | Sam Verstraete | Thijs Vandamme | Hannes De Durpel

This presentation will outline the dictionary making process of the new online Flemish Sign Language dictionary launched in 2019. First some necessary background information is provided, consisting of a brief history of Flemish Sign Language (VGT) lexicography. Then three phases in the development of the renewed dictionary of VGT will be explored: (i) user research, (ii) data-cleaning and modeling, and (iii) innovations. More than wanting to project a report of lexicographic research on a website, the goal was to make the new dictionary a practical, user-friendly reference tool that meets the needs, expectations, and skills of the dictionary users. To gain a better understanding of who the users were, several sources were consulted: the user research by Joni Oyserman (2013), the quantitative data from Google Analytics and VGTC’s own user profiles. Since 2017, VGTC has been using Signbank, an electronic database specifically developed to compile and manage lexicographic data for sign languages. Bringing together all this raw data inadvertently led to inconsistencies and small mistakes, therefore the data had to be manually revised and complemented. The VGT dictionary was mainly formally modernized, but there are also several substantive differences regarding the previous dictionary: for instance, search options were expanded, and semantic categories were added as well as a new feedback feature. In addition, the new website is also structurally different, it is now responsive to all screen sizes. Lastly, possible future innovations will briefly be discussed. VGTC aims to continuously improve both the user-based interface and the content of the current dictionary. Future goals include, but are not limited to, adding definitions and sample sentences (preferably extracted from the corpus), as well as information on the etymology and common use of signs.

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Phonetics of Negative Headshake in Russian Sign Language: A Small-Scale Corpus Study
Anastasia Chizhikova | Vadim Kimmelman

We analyzed negative headshake found in the online corpus of Russian Sign Language. We found that negative headshake can co-occur with negative manual signs, although most of these signs are not accompanied by it. We applied OpenFace, a Computer Vision toolkit, to extract head rotation measurements from video recordings, and analyzed the headshake in terms of the number of peaks (turns), the amplitude of the turns, and their frequency. We find that such basic phonetic measurements of headshake can be extracted using a combination of manual annotation and Computer Vision, and can be further used in comparative research across constructions and sign languages.

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Documenting the Use of Iranian Sign Language (ZEI) in Kermanshah
Yassaman Choubsaz | Onno Crasborn | Sara Siyavoshi | Farzaneh Soleimanbeigi

We describe a sign language documentation project funded by the Endangered Languages Documentation Project (ELDP) in the province of Kermanshah, a city in west of Iran. The deposit at ELDP archive (elararchive.org) includes recording of 38 native signers of Zaban Eshareh Irani living in Kermanshah. The recordings start with an elicitation of the signs of the Farsi alphabet along with fingerspelling of some words as well as vocabulary elicitation of some basic concepts. Subsequently, the participants are asked to watch short movies and then they are asked to retell the story. Later, the participants have natural conversations in pairs guided by a deaf moderator. Initial annotations of ID-glosses and translations to Persian and English were also archived. ID-glosses are stored as a dataset in Global Signbank, along with a citation form of signs and their phonological description. The resulting datasets and one-hour annotation of the conversations are available to other researchers in ELDP archive.

Research on sign languages (SLs) requires dedicated, efficient and comprehensive transcription systems to analyze and compare the sign parameters; at present, many transcription systems focus on manual parameters, relegating the non-manual component to a lesser role. This article presents Typannot, a formal transcription system, and in particular its application to mouth gestures: 1) first, exposing its kinesiological approach, i.e. an intrinsic articulatory description anchored in the body; 2) then, showing its conception to integrate linguistic, graphic and technical aspects within a typeface; 3) finally, presenting its application to a corpus in French Sign Language (LSF) recorded with motion capture.

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Libras Portal: A Way of Documentation, a Way of Sharing
Ronice de Quadros | Renata Krusser | Daniela Saito

Libras Portal is an interface that makes available in one single site a series of elements and tools related to the Brazilian Sign Language (Libras) and comprises Libras documentation which may be employed for research and for educational aims. Libras Portal was developed to codify tools that prop an education network and practice community, making possible the sharing of knowledge, data, and interaction in Libras and Portuguese. It involves accessibility and usability of the web, especially videos in Libras. The latter are access-friendly to available hyperlinks and tools related to communication with the target practice community. The layout also employs visual and textual resources for deaf users. The portal makes available resources for research and the teaching of language, namely Libras Grammar, Libras corpus, Sign Bank, and Literary Anthology of Libras. It is also a store for the sharing of literary, academic, and didactic materials, courses, glossaries, anthologies, lesson models, and grammar analyses. Consequently, tools were developed for the accessibility of deaf people, for easy web browsing, index information, video upload, research, and development of products for communities of deaf people. The current paper will describe the development of research and resources for accessibility.

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Representation and Synthesis of Geometric Relocations
Michael Filhol | John McDonald

One of the key features of signed discourse is the geometric placements of gestural units in signing space. Signers use the geometry of signing space to describe the placements and forms of objects and also use it to contrast participants or locales in a story. Depending on the specific functions of the placement in the discourse, features such as geometric precision, gaze redirection and timing will all differ. A signing avatar must capture these differences to sign such discourse naturally. This paper builds on prior work that animated geometric depictions to enable a signing avatar to more naturally use signing space for opposing participants and concepts in discourse. Building from a structured linguistic description of a signed newscast, they system automatically synthesizes animation that correctly utilizes signing space to lay out the opposing locales in the report. The efficacy of the approach is demonstrated through comparisons of the avatar’s motion with the source signing.

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Sign Language Phonetic Annotator-Analyzer: Open-Source Software for Form-Based Analysis of Sign Languages
Kathleen Currie Hall | Yurika Aonuki | Kaili Vesik | April Poy | Nico Tolmie

This paper provides an introduction to the Sign Language Phonetic Annotator-Analyzer (SLP-AA) software, a free and open-source tool currently under development, for facilitating detailed form-based transcription of signs. The software is designed to have a user-friendly interface that allows coders to transcribe a great deal of phonetic detail without being constrained to a particular phonetic annotation system or phonological framework. Here, we focus on the ‘annotator’ component of the software, outlining the functionality for transcribing movement, location, hand configuration, orientation, and contact, as well as the timing relations between them.

We are releasing a dataset containing videos of both fluent and non-fluent signers using American Sign Language (ASL), which were collected using a Kinect v2 sensor. This dataset was collected as a part of a project to develop and evaluate computer vision algorithms to support new technologies for automatic detection of ASL fluency attributes. A total of 45 fluent and non-fluent participants were asked to perform signing homework assignments that are similar to the assignments used in introductory or intermediate level ASL courses. The data is annotated to identify several aspects of signing including grammatical features and non-manual markers. Sign language recognition is currently very data-driven and this dataset can support the design of recognition technologies, especially technologies that can benefit ASL learners. This dataset might also be interesting to ASL education researchers who want to contrast fluent and non-fluent signing.

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MY DGS – ANNIS: ANNIS and the Public DGS Corpus
Amy Isard | Reiner Konrad

In 2018 the DGS-Korpus project published the first full release of the Public DGS Corpus. The data have already been published in two different ways to fulfil the needs of different user groups, and we have now published the third portal MY DGS – ANNIS using the ANNIS browser-based corpus software. ANNIS is a corpus query tool for visualization and querying of multi-layer corpus data. It has its own query language, AQL, and is accessed from a web browser without requiring a login. It allows more complex queries and visualizations than those provided by the existing research portal. We introduce ANNIS and its query language AQL, describe the structure of MY DGS – ANNIS, and give some example queries. The use cases with queries over multiple annotation tiers and metadata illustrate the research potential of this powerful tool and show how students and researchers can explore the Public DGS Corpus.

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Outreach and Science Communication in the DGS-Korpus Project: Accessibility of Data and the Benefit of Interactive Exchange between Communities
Elena Jahn | Calvin Khan | Annika Herrmann

In this paper, we tackle the issues of science communication and dissemination within a sign language corpus project with a focus on spreading accessible information and involving the D/deaf community on various levels. We will discuss successful examples, challenges, and limitations to public relations in such a project and particularly elaborate on use cases. The focus group is presented as a best-practice example of a what we think is a necessary perspective: taking external knowledge seriously and let community experts interact with and provide feedback on a par with academic personnel. Showing both social media and on-site events, we present some exemplary approaches from our team involved in public relations. Keywords: public relations, science communication, sign language community, DGS-Korpus project

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MC-TRISLAN: A Large 3D Motion Capture Sign Language Data-set
Pavel Jedlička | Zdeněk Krňoul | Milos Zelezny | Ludek Muller

The new 3D motion capture data corpus expands the portfolio of existing language resources by a corpus of 18 hours of Czech sign language. This helps to alleviate the current problem, which is a critical lack of high quality data necessary for research and subsequent deployment of machine learning techniques in this area. We currently provide the largest collection of annotated sign language recordings acquired by state-of-the-art 3D human body recording technology for the successful future deployment in communication technologies, especially machine translation and sign language synthesis.

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A Machine Learning-based Segmentation Approach for Measuring Similarity between Sign Languages
Tonni Das Jui | Gissella Bejarano | Pablo Rivas

Due to the lack of more variate, native and continuous datasets, sign languages are low-resources languages that can benefit from multilingualism in machine translation. In order to analyze the benefits of approaches like multilingualism, finding the similarity between sign languages can guide better matches and contributions between languages. However, calculating the similarity between sign languages again implies a laborious work to measure how close or distant signs are and their respective contexts. For that reason, we propose to support the similarity measurement between sign languages through a video-segmentation-based machine learning model that will quantify this match among signs of different countries’ sign languages. Using a machine learning approach the similarity measurement process can run more smoothly, compared to a more manual approach. We use a pre-trained temporal segmentation model for British Sign Language (BSL). We test it on three datasets, an American Sign Language (ASL) dataset, an Indian Sign Language (ISL), and an Australian Sign Language (AUSLAN) dataset. We hypothesize that the percentage of segmented and recognized signs by this machine learning model can represent the percentage of overlap or similarity between British and the other three sign languages. In our ongoing work, we evaluate three metrics considering Swadesh’s and Woodward’s list and their synonyms. We found that our intermediate-strict metric coincides with a more classical analysis of the similarity between British and American Sign Language, as well as with the classical low measurement between Indian and British sign languages. On the other hand, our similarity measurement between British and Australian Sign language just holds for part of the Australian Sign Language and not the whole data sample.

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The Sign Language Dataset Compendium: Creating an Overview of Digital Linguistic Resources
Maria Kopf | Marc Schulder | Thomas Hanke

One of the challenges that sign language researchers face is the identification of suitable language datasets, particularly for cross-lingual studies. There is no single source of information on what sign language corpora and lexical resources exist or how they compare. Instead, they have to be found through extensive literature review or word-of-mouth. The amount of information available on individual datasets can also vary widely and may be distributed across different publications, data repositories and (potentially defunct) project websites. This article introduces the Sign Language Dataset Compendium, an extensive overview of linguistic resources for sign languages. It covers existing corpora and lexical resources, as well as commonly used data collection tasks. Special attention is paid to covering resources for many different languages from around the globe. All information is provided in a standardised format to make entries comparable, but kept flexible enough to allow for differences in content. The compendium is intended as a growing resource that will be updated regularly.

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Making Sign Language Corpora Comparable: A Study of Palm-Up and Throw-Away in Polish Sign Language, German Sign Language, and Russian Sign Language
Anna Kuder

This paper is primarily devoted to describing the preparation phase of a large-scale comparative study based on naturalistic linguistic data drawn from multiple sign language corpora. To provide an example, I am using my current project on manual gestural elements in Polish Sign Language, German Sign Language, and Russian Sign Language. The paper starts with a description of the reasons behind undertaking this project. Then, I describe the scope of my study, which is focused on two manual elements present in all three mentioned sign languages: palm-up and throw-away; and the three corpora which are my data sources. This is followed by a presentation of the steps taken in the initial stages of the project in order to make the data comparable. Those steps are: choosing the adequate data samples from all three corpora, gathering all data within the chosen software, and creating an annotation schema that builds on the annotations already present in all three corpora. Even though the project is still underway, and the annotation process is ongoing, preliminary discussions about the nature of the analysed manual activities are presented based on the initial annotations for the sake of evaluating the created annotation schema. I conclude the paper with some remarks about the performance of the employed methodology.

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Open Repository of the Polish Sign Language Corpus: Publication Project of the Polish Sign Language Corpus
Anna Kuder | Joanna Wójcicka | Piotr Mostowski | Paweł Rutkowski

Between 2010 and 2020, the research team of the Section for Sign Linguistics collected, annotated, and translated a large corpus of Polish Sign Language (polski język migowy, PJM). After this task was finished, a substantial part of the gathered materials was published online as the Open Repository of the Polish Sign Language Corpus. The current paper gives an overview of the process of converting the material from the Corpus into the Repository. If presents and explains the decisions made along the way and describes the process of data preparation and publication. There are two levels of access to the Repository, which are meant to fulfil the needs of a wide range of public users, from members of the Deaf community, through hearing students of PJM, sign language teachers and interpreters, to users with academic background. We describe how corpus material available in open access was prepared to be searchable by text type and elicitation tasks, by sociolinguistic metadata, and by translation into written Polish. We go on to explain how access for research purposes differs from open access. We present possible ways in which data gathered in the Repository may be used by members of the signing community in Poland and abroad.

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Functional Data Analysis of Non-manual Marking of Questions in Kazakh-Russian Sign Language
Anna Kuznetsova | Alfarabi Imashev | Medet Mukushev | Anara Sandygulova | Vadim Kimmelman

This paper is a continuation of Kuznetsova et al. (2021), which described non-manual markers of polar and wh-questions in comparison with statements in an NLP dataset of Kazakh-Russian Sign Language (KRSL) using Computer Vision. One of the limitations of the previous work was the distortion of the 3D face landmarks when the head was rotated. The proposed solution was to train a simple linear regression model to predict the distortion and then subtract it from the original output. We improve this technique with a multilayer perceptron. Another limitation that we intend to address in this paper is the discrete analysis of the continuous movement of non-manuals. In Kuznetsova et al. (2021) we averaged the value of the non-manual over its scope for statistical analysis. To preserve information on the shape of the movement, in this study we use a statistical tool that is often used in speech research, Functional Data Analysis, specifically Functional PCA.

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Two New AZee Production Rules Refining Multiplicity in French Sign Language
Emmanuella Martinod | Claire Danet | Michael Filhol

This paper is a contribution to sign language (SL) modeling. We focus on the hitherto imprecise notion of “Multiplicity”, assumed to express plurality in French Sign Language (LSF), using AZee approach. AZee is a linguistic and formal approach to modeling LSF. It takes into account the linguistic properties and specificities of LSF while respecting constraints linked to a modeling process. We present the methodology to extract AZee production rules. Based on the analysis of strong form-meaning associations in SL data (elicited image descriptions and short news), we identified two production rules structuring the expression of multiplicity in LSF. We explain how these newly extracted production rules are different from existing ones. Our goal is to refine the AZee approach to allow the coverage of a growing part of LSF. This work could lead to an improvement in SL synthesis and SL automatic translation.

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Language Planning in Action: Depiction as a Driver of New Terminology in Irish Sign Language
Rachel Moiselle | Lorraine Leeson

In this paper, we examine the linguistic phenomenon known as ‘depiction’, which relates to the ability to visually represent semantic components (Dudis, 2004). While some elements of this have been described for Irish Sign Language, with particular attention to the ‘productive lexicon’ (Leeson & Grehan, 2004; Leeson & Saeed, 2012; Matthews, 1996; O’Baoill & Matthews, 2000), here, we take the analysis further, drawing on what we have learned from cognitive linguistics over the past decade. Drawing on several recently developed domain-specific glossaries (e.g., STEM1, Covid-192, political domain, Sexual, Domestic and Gender Based Violence (SDGBV)-related vocabulary) we present ongoing analysis indicating that a deliberate focus on iconicity, in particular, elements of depiction, appears to be a primary driver. We also consider the potential implications of the insights we intend to gain from Deaf-led glossary glossary development work in the context of Machine Translation goals, for example, for work in progress on the Horizon 2020 funded SignON project.

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Facilitating the Spread of New Sign Language Technologies across Europe
Hope Morgan | Onno Crasborn | Maria Kopf | Marc Schulder | Thomas Hanke

For developing sign language technologies like automatic translation, huge amounts of training data are required. Even the larger corpora available for some sign languages are tiny compared to the amounts of data used for corresponding spoken language technologies. The overarching goal of the European project EASIER is to develop a framework for bidirectional automatic translation between sign and spoken languages and between sign languages. One part of this multi-dimensional project is that it will pool available language resources from European sign languages into a larger dataset to address the data scarcity problem. This approach promises to open the floor for lower-resourced sign languages in Europe. This article focusses on efforts in the EASIER project to allow for new languages to make use of such technologies in the future. What are the characteristics of sign language resources needed to train recognition, translation, and synthesis algorithms, and how can other countries including those without any sign resources follow along with these developments? The efforts undertaken in EASIER include creating workflow documents and organizing training sessions in online workshops. They reflect the current state of the art, and will likely need to be updated in the coming decade.

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ISL-LEX v.1: An Online Lexical Resource of Israeli Sign Language
Hope Morgan | Wendy Sandler | Rose Stamp | Rama Novogrodsky

This paper describes a new online lexical resource and interactive tool for Israeli Sign Language, ISL-LEX v.1. The dataset contains 961 non-compound ISL signs with the following information: subjective frequency ratings from native signers, iconicity ratings from native and non-native signers (presented separately), and phonological properties in six domains. The selection of signs was also designed to reflect a broad distinction between those signs acquired early in childhood and those acquired later. ISL-LEX is an online interface built using the SIGN-LEX visualization (Caselli et al. 2022), and is intended for use by researchers, educators, and students. It is therefore offered in two text-based versions, English and Hebrew, with video instructions in ISL.

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Towards Large Vocabulary Kazakh-Russian Sign Language Dataset: KRSL-OnlineSchool
Medet Mukushev | Aigerim Kydyrbekova | Vadim Kimmelman | Anara Sandygulova

This paper presents a new dataset for Kazakh-Russian Sign Language (KRSL) created for the purposes of Sign Language Processing. In 2020, Kazakhstan’s schools were quickly switched to online mode due to the COVID-19 pandemic. Every working day, the El-arna TV channel was broadcasting video lessons for grades from 1 to 11 with sign language translation. This opportunity allowed us to record a corpus with a large vocabulary and spontaneous SL interpretation. To this end, this corpus contains video recordings of Kazakhstan’s online school translated to Kazakh-Russian sign language by 7 interpreters. At the moment we collected and cleaned 890 hours of video material. A custom annotation tool was created to make the process of data annotation simple and easy-to-use by the Deaf community. To date, around 325 hours of videos have been annotated with glosses and 4,009 lessons out of 4,547 were transcribed with automatic speech-to-text software. The KRSL-OnlineSchool dataset will be made publicly available at https://krslproject.github.io/online-school/

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Towards Semi-automatic Sign Language Annotation Tool: SLAN-tool
Medet Mukushev | Arman Sabyrov | Madina Sultanova | Vadim Kimmelman | Anara Sandygulova

This paper presents a semi-automatic annotation tool for sign languages namely SLAN-tool. The SLAN-tool provides a web-based service for the annotation of sign language videos. Researchers can use the SLAN-tool web service to annotate new and existing sign language datasets with different types of annotations, such as gloss, handshape configurations, and signing regions. This is allowed using a custom tier adding functionality. A unique feature of the tool is its automatic annotation functionality which uses several neural network models in order to recognize signing segments from videos and classify handshapes according to HamNoSys handshape inventory. Furthermore, SLAN-tool users can export annotations and import them into ELAN. The SLAN-tool is publicly available at https://slan-tool.com.

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Resources for Computer-Based Sign Recognition from Video, and the Criticality of Consistency of Gloss Labeling across Multiple Large ASL Video Corpora
Carol Neidle | Augustine Opoku | Carey Ballard | Konstantinos M. Dafnis | Evgenia Chroni | Dimitri Metaxas

The WLASL purports to be “the largest video dataset for Word-Level American Sign Language (ASL) recognition.” It brings together various publicly shared video collections that could be quite valuable for sign recognition research, and it has been used extensively for such research. However, a critical problem with the accompanying annotations has heretofore not been recognized by the authors, nor by those who have exploited these data: There is no 1-1 correspondence between sign productions and gloss labels. Here we describe a large (and recently expanded and enhanced), linguistically annotated, downloadable, video corpus of citation-form ASL signs shared by the American Sign Language Linguistic Research Project (ASLLRP)—with 23,452 sign tokens and an online Sign Bank—in which such correspondences are enforced. We furthermore provide annotations for 19,672 of the WLASL video examples consistent with ASLLRP glossing conventions. For those wishing to use WLASL videos, this provides a set of annotations that makes it possible: (1) to use those data reliably for computational research; and/or (2) to combine the WLASL and ASLLRP datasets, creating a combined resource that is larger and richer than either of those datasets individually, with consistent gloss labeling for all signs. We also offer a summary of our own sign recognition research to date that exploits these data resources.

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Signed Language Transcription and the Creation of a Cross-linguistic Comparative Database
Justin Power | David Quinto-Pozos | Danny Law

As the availability of signed language data has rapidly increased, sign scholars have been confronted with the challenge of creating a common framework for the cross-linguistic comparison of the phonological forms of signs. While transcription techniques have played a fundamental role in the creation of cross-linguistic comparative databases for spoken languages, transcription has featured much less prominently in sign research and lexicography. Here we report the experiences of the Sign Change project in using the signed language transcription system HamNoSys to create a comparative database of basic vocabulary for thirteen signed languages. We report the results of a small-scale study, in which we measured (i) the average time required for two trained transcribers to complete a transcription and (ii) the similarity of their independently produced transcriptions. We find that, across the two transcribers, the transcription of one sign required, on average, one minute and a half. We also find that the similarity of transcriptions differed across phonological parameters. We consider the implications of our findings about transcription time and transcription similarity for other projects that plan to incorporate transcription techniques.

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Integrating Auslan Resources into the Language Data Commons of Australia
River Tae Smith | Louisa Willoughby | Trevor Johnston

This paper describes a project to secure Auslan (Australian Sign Language) resources within a national language data network called the Language Data Commons of Australia (LDaCA). The resources are Auslan Signbank, a web-based multi-media dictionary, and the Auslan Corpus, a collection of video recordings of the language being used in various contexts with time-aligned ELAN annotation files. We aim to make these resources accessible to the language community, encourage community participation in the curation of the data, and facilitate and extend their uses in language teaching and linguistic research. The software platforms of both resources will be made compatible with other LDaCA resources; and the two will also be aggregated and linked so that (i) users of the dictionary can view attested corpus examples for an entry; and (ii) users of the corpus can instantly view the dictionary entry for an already glossed sign to check phonological, lexical and grammatical information about it, and/or to ensure that the correct annotation gloss (aka ‘ID-gloss’) for a sign token has been chosen. This will enhance additions to annotations in the Auslan Corpus, entries in Auslan Signbank and the integrity of research based on both.

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Capturing Distalization
Rose Stamp | Lilyana Khatib | Hagit Hel-Or

Coding and analyzing large amounts of video data is a challenge for sign language researchers, who traditionally code 2D video data manually. In recent years, the implementation of 3D motion capture technology as a means of automatically tracking movement in sign language data has been an important step forward. Several studies show that motion capture technologies can measure sign language movement parameters – such as volume, speed, variance – with high accuracy and objectivity. In this paper, using motion capture technology and machine learning, we attempt to automatically measure a more complex feature in sign language known as distalization. In general, distalized signs use the joints further from the torso (such as the wrist), however, the measure is relative and therefore distalization is not straightforward to measure. The development of a reliable and automatic measure of distalization using motion tracking technology is of special interest in many fields of sign language research.

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The Corpus of Israeli Sign Language
Rose Stamp | Ora Ohanin | Sara Lanesman

The Corpus of Israeli Sign Language is a four-year project (2020-2024) which aims to create a digital open-access corpus of spontaneous and elicited data from a representative sample of the Israeli deaf community. In this paper, the methodology for building the Corpus of Israeli Sign Language is described. Israeli Sign Language (ISL) is the main sign language used across Israel by around 10,000 people. As part of the corpus, data will be collected from 120 deaf ISL signers across four sites in Israel: Tel Aviv and the Centre, Haifa and the North, Be’er Sheva and the South and Jerusalem and the surrounding area. Participants will engage in a variety of tasks, eliciting a range of signing styles from free conversation to lexical elicitation. The dataset will consist of recordings of over 360 hours of video data which will be used to conduct sociolinguistic investigations of language contact, variation, and change in the near term, and other linguistic analyses in the future.

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Segmentation of Signs for Research Purposes: Comparing Humans and Machines
Bencie Woll | Neil Fox | Kearsy Cormier

Sign languages such as British Sign Language (BSL) are visual languages which lack standard writing systems. Annotation of sign language data, especially for the purposes of machine readability, is therefore extremely slow. Tools to help automate and thus speed up the annotation process are very much needed. Here we test the development of one such tool (VIA-SLA), which uses temporal convolutional networks (Renz et al., 2021a, b) for the purpose of segmenting continuous signing in any sign language, and is designed to integrate smoothly with ELAN, the widely used annotation software for analysis of videos of sign language. We compare automatic segmentation by machine with segmentation done by a human, both in terms of time needed and accuracy of segmentation, using samples taken from the BSL Corpus (Schembri et al., 2014). A small sample of four short video files is tested (mean duration 25 seconds). We find that mean accuracy in terms of number and location of segmentations is relatively high, at around 78%. This preliminary test suggests that VIA-SLA promises to be very useful for sign linguists.

Deaf signers who wish to communicate in their native language frequently share videos on the Web. However, videos cannot preserve privacy—as is often desirable for discussion of sensitive topics—since both hands and face convey critical linguistic information and therefore cannot be obscured without degrading communication. Deaf signers have expressed interest in video anonymization that would preserve linguistic content. However, attempts to develop such technology have thus far shown limited success. We are developing a new method for such anonymization, with input from ASL signers. We modify a motion-based image animation model to generate high-resolution videos with the signer identity changed, but with the preservation of linguistically significant motions and facial expressions. An asymmetric encoder-decoder structured image generator is used to generate the high-resolution target frame from the low-resolution source frame based on the optical flow and confidence map. We explicitly guide the model to attain a clear generation of hands and faces by using bounding boxes to improve the loss computation. FID and KID scores are used for the evaluation of the realism of the generated frames. This technology shows great potential for practical applications to benefit deaf signers.

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bib (full) Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

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Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages
Maite Melero | Sakriani Sakti | Claudia Soria

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Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings
Marcely Zanon Boito | Bolaji Yusuf | Lucas Ondel | Aline Villavicencio | Laurent Besacier

Documenting languages helps to prevent the extinction of endangered dialects - many of which are otherwise expected to disappear by the end of the century. When documenting oral languages, unsupervised word segmentation (UWS) from speech is a useful, yet challenging, task. It consists in producing time-stamps for slicing utterances into smaller segments corresponding to words, being performed from phonetic transcriptions, or in the absence of these, from the output of unsupervised speech discretization models. These discretization models are trained using raw speech only, producing discrete speech units that can be applied for downstream (text-based) tasks. In this paper we compare five of these models: three Bayesian and two neural approaches, with regards to the exploitability of the produced units for UWS. For the UWS task, we experiment with two models, using as our target language the Mboshi (Bantu C25), an unwritten language from Congo-Brazzaville. Additionally, we report results for Finnish, Hungarian, Romanian and Russian in equally low-resource settings, using only 4 hours of speech. Our results suggest that neural models for speech discretization are difficult to exploit in our setting, and that it might be necessary to adapt them to limit sequence length. We obtain our best UWS results by using Bayesian models that produce high quality, yet compressed, discrete representations of the input speech signal.

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An Open Source Web Reader for Under-Resourced Languages
Judy Fong | Þorsteinn Daði Gunnarsson | Sunneva Þorsteinsdóttir | Gunnar Thor Örnólfsson | Jon Gudnason

We have developed an open source web reader in Iceland for under-resourced languages. The web reader was developed due to the need for a free and good quality web reader for languages which fall outside the scope of commercially available web readers. It relies on a text-to-speech (TTS) pipeline accessed via a cloud service. The web reader was developed using the Icelandic TTS voices Alfur and Dilja, but could be connected to any language which has a TTS pipeline. The design of our web reader focuses on functionality, adaptability and user friendliness. Therefore, the web reader’s feature set heavily overlaps with the minimal features necessary to provide a good web reading experience while still being extensible enough to be adapted to work for other languages, high-resourced and under-resourced. The web reader works well on all the major web browsers and has a Web Content Accessibility Guidelines 2.0 Level AA: Acceptable compliance, meaning that it works well for the largest user groups, people in under-resourced languages with visual impairments and difficulty reading. The code for our web reader is available and published with an Apache 2.0 license at https://github.com/cadia-lvl/WebRICE, which includes a simple demo of the project.

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Text-to-Speech for Under-Resourced Languages: Phoneme Mapping and Source Language Selection in Transfer Learning
Phat Do | Matt Coler | Jelske Dijkstra | Esther Klabbers

We propose a new approach for phoneme mapping in cross-lingual transfer learning for text-to-speech (TTS) in under-resourced languages (URLs), using phonological features from the PHOIBLE database and a language-independent mapping rule. This approach was validated through our experiment, in which we pre-trained acoustic models in Dutch, Finnish, French, Japanese, and Spanish, and fine-tuned them with 30 minutes of Frisian training data. The experiment showed an improvement in both naturalness and pronunciation accuracy in the synthesized Frisian speech when our mapping approach was used. Since this improvement also depended on the source language, we then experimented on finding a good criterion for selecting source languages. As an alternative to the traditionally used language family criterion, we tested a novel idea of using Angular Similarity of Phoneme Frequencies (ASPF), which measures the similarity between the phoneme systems of two languages. ASPF was empirically confirmed to be more effective than language family as a criterion for source language selection, and also to affect the phoneme mapping’s effectiveness. Thus, a combination of our phoneme mapping approach and the ASPF measure can be beneficially adopted by other studies involving multilingual or cross-lingual TTS for URLs.

While the alignment of audio recordings and text (often termed “forced alignment”) is often treated as a solved problem, in practice the process of adapting an alignment system to a new, under-resourced language comes with significant challenges, requiring experience and expertise that many outside of the speech community lack. This puts otherwise “solvable” problems, like the alignment of Indigenous language audiobooks, out of reach for many real-world Indigenous language organizations. In this paper, we detail ReadAlong Studio, a suite of tools for creating and visualizing aligned audiobooks, including educational features like time-aligned highlighting, playing single words in isolation, and variable-speed playback. It is intended to be accessible to creators without an extensive background in speech or NLP, by automating or making optional many of the specialist steps in an alignment pipeline. It is well documented at a beginner-technologist level, has already been adapted to 30 languages, and can work out-of-the-box on many more languages without adaptation.

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Corpus Creation for Sentiment Analysis in Code-Mixed Tulu Text
Asha Hegde | Mudoor Devadas Anusha | Sharal Coelho | Hosahalli Lakshmaiah Shashirekha | Bharathi Raja Chakravarthi

Sentiment Analysis (SA) employing code-mixed data from social media helps in getting insights to the data and decision making for various applications. One such application is to analyze users’ emotions from comments of videos on YouTube. Social media comments do not adhere to the grammatical norms of any language and they often comprise a mix of languages and scripts. The lack of annotated code-mixed data for SA in a low-resource language like Tulu makes the SA a challenging task. To address the lack of annotated code-mixed Tulu data for SA, a gold standard trlingual code-mixed Tulu annotated corpus of 7,171 YouTube comments is created. Further, Machine Learning (ML) algorithms are employed as baseline models to evaluate the developed dataset and the performance of the ML algorithms are found to be encouraging.

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Crowd-sourcing for Less-resourced Languages: Lingua Libre for Polish
Mathilde Hutin | Marc Allassonnière-Tang

Oral corpora for linguistic inquiry are frequently built based on the content of news, radio, and/or TV shows, sometimes also of laboratory recordings. Most of these existing corpora are restricted to languages with a large amount of data available. Furthermore, such corpora are not always accessible under a free open-access license. We propose a crowd-sourced alternative to this gap. Lingua Libre is the participatory linguistic media library hosted by Wikimedia France. It includes recordings from more than 140 languages. These recordings have been provided by more than 750 speakers worldwide, who voluntarily recorded word entries of their native language and made them available under a Creative Commons license. In the present study, we take Polish, a less-resourced language in terms of phonetic data, as an example, and compare our phonetic observations built on the data from Lingua Libre with the phonetic observations found by previous linguistic studies. We observe that the data from Lingua Libre partially matches the phonetic inventory of Polish as described in previous studies, but that the acoustic values are less precise, thus showing both the potential and the limitations of Lingua Libre to be used for phonetic research.

TuLaR (Tupian Language Resources) is a project for collecting, documenting, analyzing, and developing computational and pedagogical material for low-resource Brazilian indigenous languages. It provides valuable data for language research regarding typological, syntactic, morphological, and phonological aspects. Here we present TuLaR’s databases, with special consideration to TuDeT (Tupian Dependency Treebanks), an annotated corpus under development for nine languages of the Tupian family, built upon the Universal Dependencies framework. The annotation within such a framework serves a twofold goal: enriching the linguistic documentation of the Tupian languages due to the rapid and consistent annotation, and providing computational resources for those languages, thanks to the suitability of our framework for developing NLP tools. We likewise present a related lexical database, some tools developed by the project, and examine future goals for our initiative.

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Quality versus Quantity: Building Catalan-English MT Resources
Ona de Gibert Bonet | Ksenia Kharitonova | Blanca Calvo Figueras | Jordi Armengol-Estapé | Maite Melero

In this work, we make the case of quality over quantity when training a MT system for a medium-to-low-resource language pair, namely Catalan-English. We compile our training corpus out of existing resources of varying quality and a new high-quality corpus. We also provide new evaluation translation datasets in three different domains. In the process of building Catalan-English parallel resources, we evaluate the impact of drastically filtering alignments in the resulting MT engines. Our results show that even when resources are limited, as in this case, it is worth filtering for quality. We further explore the cross-lingual transfer learning capabilities of the proposed model for parallel corpus filtering by applying it to other languages. All resources generated in this work are released under open license to encourage the development of language technology in Catalan.

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A Sentiment Corpus for South African Under-Resourced Languages in a Multilingual Context
Ronny Mabokela | Tim Schlippe

Multilingual sentiment analysis is a process of detecting and classifying sentiment based on textual information written in multiple languages. There has been tremendous research advancement on high-resourced languages such as English. However, progress on under-resourced languages remains underrepresented with limited opportunities for further development of natural language processing (NLP) technologies. Sentiment analysis (SA) for under-resourced language still is a skewed research area. Although, there are some considerable efforts in emerging African countries to develop such resources for under-resourced languages, languages such as indigenous South African languages still suffer from a lack of datasets. To the best of our knowledge, there is currently no dataset dedicated to SA research for South African languages in a multilingual context, i.e. comments are in different languages and may contain code-switching. In this paper, we present the first subset of the multilingual sentiment corpus SAfriSenti for the three most widely spoken languages in South Africa—English, Sepedi (i.e. Northern Sotho), and Setswana. This subset consists of over 40,000 annotated tweets in all the three languages including even 36.6% of code-switched texts. We present data collection, cleaning and annotation strategies that were followed to curate the dataset for these languages. Furthermore, we describe how we developed language-specific sentiment lexicons, morpheme-based sentiment taggers, conduct linguistic analyses and present possible solutions for the challenges of this sentiment dataset. We will release the dataset and sentiment lexicons to the research communities to advance the NLP research of under-resourced languages.

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CUNI Submission to MT4All Shared Task
Ivana Kvapilíková | Ondrej Bojar

This paper describes our submission to the MT4All Shared Task in unsupervised machine translation from English to Ukrainian, Kazakh and Georgian in the legal domain. In addition to the standard pipeline for unsupervised training (pretraining followed by denoising and back-translation), we used supervised training on a pseudo-parallel corpus retrieved from the provided mono-lingual corpora. Our system scored significantly higher than the baseline hybrid unsupervised MT system.

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Resource: Indicators on the Presence of Languages in Internet
Daniel Pimienta

Reliable and maintained indicators of the space of languages on the Internet are required to support appropriate public policies and well-informed linguistic studies. Current sources are scarce and often strongly biased. The model to produce indicators on the presence of languages in the Internet, launched by the Observatory in 2017, has reached a sensible level of maturity and its data products are shared in CC-BY-SA 4.0 license. It reaches now 329 languages (L1 speakers > one million) and all the biases associated with the model have been controlled to an acceptable threshold, giving trust to the data, within an estimated confidence interval of +-20%. Some of the indicators (mainly the percentage of L1+L2 speakers connected to the Internet per language and derivates) rely on Ethnologue Global Dataset #24 for demo-linguistic data and ITU, completed by World Bank, for the percentage of persons connected to the Internet by country. The rest of indicators relies on the previous sources plus a large combination of hundreds of different sources for data related to Web contents per language. This research poster focuses the description of the new linguistic resources created. Methodological considerations are only exposed briefly and will be developed in another paper.

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Language Technologies for Low Resource Languages: Sociolinguistic and Multilingual Insights
A. Seza Doğruöz | Sunayana Sitaram

There is a growing interest in building language technologies (LTs) for low resource languages (LRLs). However, there are flaws in the planning, data collection and development phases mostly due to the assumption that LRLs are similar to High Resource Languages (HRLs) but only smaller in size. In our paper, we first provide examples of failed LTs for LRLs and provide the reasons for these failures. Second, we discuss the problematic issues with the data for LRLs. Finally, we provide recommendations for building better LTs for LRLs through insights from sociolinguistics and multilingualism. Our goal is not to solve all problems around LTs for LRLs but to raise awareness about the existing issues, provide recommendations toward possible solutions and encourage collaboration across academic disciplines for developing LTs that actually serve the needs and preferences of the LRL communities.

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Sentiment Analysis for Hausa: Classifying Students’ Comments
Ochilbek Rakhmanov | Tim Schlippe

We describe our work on sentiment analysis for Hausa, where we investigated monolingual and cross-lingual approaches to classify student comments in course evaluations. Furthermore, we propose a novel stemming algorithm to improve accuracy. For studies in this area, we collected a corpus of more than 40,000 comments—the Hausa-English Sentiment Analysis Corpus For Educational Environments (HESAC). Our results demonstrate that the monolingual approaches for Hausa sentiment analysis slightly outperform the cross-lingual systems. Using our stemming algorithm in the pre-processing even improved the best model resulting in 97.4% accuracy on HESAC.

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Nepali Encoder Transformers: An Analysis of Auto Encoding Transformer Language Models for Nepali Text Classification
Utsav Maskey | Manish Bhatta | Shiva Bhatt | Sanket Dhungel | Bal Krishna Bal

Language model pre-training has significantly impacted NLP and resulted in performance gains on many NLP-related tasks, but comparative study of different approaches on many low-resource languages seems to be missing. This paper attempts to investigate appropriate methods for pretraining a Transformer-based model for the Nepali language. We focus on the language-specific aspects that need to be considered for modeling. Although some language models have been trained for Nepali, the study is far from sufficient. We train three distinct Transformer-based masked language models for Nepali text sequences: distilbert-base (Sanh et al., 2019) for its efficiency and minuteness, deberta-base (P. He et al., 2020) for its capability of modeling the dependency of nearby token pairs and XLM-ROBERTa (Conneau et al., 2020) for its capabilities to handle multilingual downstream tasks. We evaluate and compare these models with other Transformer-based models on a downstream classification task with an aim to suggest an effective strategy for training low-resource language models and their fine-tuning.

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CoSwID, a Code Switching Identification Method Suitable for Under-Resourced Languages
Laurent Kevers

We propose a method for identifying monolingual textual segments in multilingual documents. It requires only a minimal number of linguistic resources – word lists and monolingual corpora – and can therefore be adapted to many under-resourced languages. Taking these languages into account when processing multilingual documents in NLP tools is important as it can contribute to the creation of essential textual resources. This language identification task – code switching detection being its most complex form – can also provide added value to various existing data or tools. Our research demonstrates that a language identification module performing well on short texts can be used to efficiently analyse a document through a sliding window. The results obtained for code switching identification – between 87.29% and 97.97% accuracy – are state-of-the-art, which is confirmed by the benchmarks performed on the few available systems that have been used on our test data.

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A Neural Network Approach to Create Minangkabau-Indonesia Bilingual Dictionary
Kartika Resiandi | Yohei Murakami | Arbi Haza Nasution

Indonesia has many varieties of ethnic languages, and most come from the same language family, namely Austronesian languages. Coming from that same language family, the words in Indonesian ethnic languages are very similar. However, there is research stating that Indonesian ethnic languages are endangered. Thus, to prevent that, we proposed to create a bilingual dictionary between ethnic languages using a neural network approach to extract transformation rules using character level embedding and the Bi-LSTM method in a sequence-to-sequence model. The model has an encoder and decoder. The encoder functions read the input sequence, character by character, generate context, then extract a summary of the input. The decoder will produce an output sequence where every character in each time-step and the next character that comes out are affected by the previous character. The current case for experiment translation focuses on Minangkabau and Indonesian languages with 13761-word pairs. For evaluating the model’s performance, 5-Fold Cross-Validation is used.

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Machine Translation from Standard German to Alemannic Dialects
Louisa Lambrecht | Felix Schneider | Alexander Waibel

Machine translation has been researched using deep neural networks in recent years. These networks require lots of data to learn abstract representations of the input stored in continuous vectors. Dialect translation has become more important since the advent of social media. In particular, when dialect speakers and standard language speakers no longer understand each other, machine translation is of rising concern. Usually, dialect translation is a typical low-resourced language setting facing data scarcity problems. Additionally, spelling inconsistencies due to varying pronunciations and the lack of spelling rules complicate translation. This paper presents the best-performing approaches to handle these problems for Alemannic dialects. The results show that back-translation and conditioning on dialectal manifestations achieve the most remarkable enhancement over the baseline. Using back-translation, a significant gain of +4.5 over the strong transformer baseline of 37.3 BLEU points is accomplished. Differentiating between several Alemannic dialects instead of treating Alemannic as one dialect leads to substantial improvements: Multi-dialectal translation surpasses the baseline on the dialectal test sets. However, training individual models outperforms the multi-dialectal approach. There, improvements range from 7.5 to 10.6 BLEU points over the baseline depending on the dialect.

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Question Answering Classification for Amharic Social Media Community Based Questions
Tadesse Destaw Belay | Seid Muhie Yimam | Abinew Ayele | Chris Biemann

In this work, we build a Question Answering (QA) classification dataset from a social media platform, namely the Telegram public channel called @AskAnythingEthiopia. The channel has more than 78k subscribers and has existed since May 31, 2019. The platform allows asking questions that belong to various domains, like politics, economics, health, education, and so on. Since the questions are posed in a mixed-code, we apply different strategies to pre-process the dataset. Questions are posted in Amharic, English, or Amharic but in a Latin script. As part of the pre-processing tools, we build a Latin to Ethiopic Script transliteration tool. We collect 8k Amharic and 24K transliterated questions and develop deep learning-based questions answering classifiers that attain as high as an F-score of 57.29 in 20 different question classes or categories. The datasets and pre-processing scripts are open-sourced to facilitate further research on the Amharic community-based question answering.

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Automatic Detection of Morphological Processes in the Yorùbá Language
Tunde Adegbola

Automatic morphology induction is important for computational processing of natural language. In resource-scarce languages in particular, it offers the possibility of supplementing data-driven strategies of Natural Language Processing with morphological rules that may cater for out-of-vocabulary words. Unfortunately, popular approaches to unsupervised morphology induction do not work for some of the most productive morphological processes of the Yorùbá language. To the best of our knowledge, the automatic induction of such morphological processes as full and partial reduplication, infixation, interfixation, compounding and other morphological processes, particularly those based on the affixation of stem-derived morphemes have not been adequately addressed in the literature. This study proposes a method for the automatic detection of stem-derived morphemes in Yorùbá. Words in a Yorùbá lexicon of 14,670 word-tokens were clustered around “word-labels”. A word-label is a textual proxy of the patterns imposed on words by the morphological processes through which they were formed. Results confirm a conjectured significant difference between the predicted and observed probabilities of word-labels motivated by stem-derived morphemes. This difference was used as basis for automatic identification of words formed by the affixation of stem-derived morphemes. Keywords: Unsupervised Morphology Induction, Recurrent Partials, Recurrent Patterns, Stem-derived Morphemes, Word-labels.

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Evaluating Unsupervised Approaches to Morphological Segmentation for Wolastoqey
Diego Bear | Paul Cook

Finite-state approaches to morphological analysis have been shown to improve the performance of natural language processing systems for polysynthetic languages, in-which words are generally composed of many morphemes, for tasks such as language modelling (Schwartz et al., 2020). However, finite-state morphological analyzers are expensive to construct and require expert knowledge of a language’s structure. Currently, there is no broad-coverage finite-state model of morphology for Wolastoqey, also known as Passamaquoddy-Maliseet, an endangered low-resource Algonquian language. As this is the case, in this paper, we investigate using two unsupervised models, MorphAGram and Morfessor, to obtain morphological segmentations for Wolastoqey. We train MorphAGram and Morfessor models on a small corpus of Wolastoqey words and evaluate using two an notated datasets. Our results indicate that MorphAGram outperforms Morfessor for morphological segmentation of Wolastoqey.

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Baseline English and Maltese-English Classification Models for Subjectivity Detection, Sentiment Analysis, Emotion Analysis, Sarcasm Detection, and Irony Detection
Keith Cortis | Brian Davis

This paper presents baseline classification models for subjectivity detection, sentiment analysis, emotion analysis, sarcasm detection, and irony detection. All models are trained on user-generated content gathered from newswires and social networking services, in three different languages: English —a high-resourced language, Maltese —a low-resourced language, and Maltese-English —a code-switched language. Traditional supervised algorithms namely, Support Vector Machines, Naïve Bayes, Logistic Regression, Decision Trees, and Random Forest, are used to build a baseline for each classification task, namely subjectivity, sentiment polarity, emotion, sarcasm, and irony. Baseline models are established at a monolingual (English) level and at a code-switched level (Maltese-English). Results obtained from all the classification models are presented.

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Building Open-source Speech Technology for Low-resource Minority Languages with SáMi as an Example – Tools, Methods and Experiments
Katri Hiovain-Asikainen | Sjur Moshagen

This paper presents a work-in-progress report of an open-source speech technology project for indigenous Sami languages. A less detailed description of this work has been presented in a more general paper about the whole GiellaLT language infrastructure, submitted to the LREC 2022 main conference. At this stage, we have designed and collected a text corpus specifically for developing speech technology applications, namely Text-to-speech (TTS) and Automatic speech recognition (ASR) for the Lule and North Sami languages. We have also piloted and experimented with different speech synthesis technologies using a miniature speech corpus as well as developed tools for effective processing of large spoken corpora. Additionally, we discuss effective and mindful use of the speech corpus and also possibilities to use found/archive materials for training an ASR model for these languages.

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Investigating the Quality of Static Anchor Embeddings from Transformers for Under-Resourced Languages
Pranaydeep Singh | Orphee De Clercq | Els Lefever

This paper reports on experiments for cross-lingual transfer using the anchor-based approach of Schuster et al. (2019) for English and a low-resourced language, namely Hindi. For the sake of comparison, we also evaluate the approach on three very different higher-resourced languages, viz. Dutch, Russian and Chinese. Initially designed for ELMo embeddings, we analyze the approach for the more recent BERT family of transformers for a variety of tasks, both mono and cross-lingual. The results largely prove that like most other cross-lingual transfer approaches, the static anchor approach is underwhelming for the low-resource language, while performing adequately for the higher resourced ones. We attempt to provide insights into both the quality of the anchors, and the performance for low-shot cross-lingual transfer to better understand this performance gap. We make the extracted anchors and the modified train and test sets available for future research at https://github.com/pranaydeeps/Vyaapak

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Introducing YakuToolkit. Yakut Treebank and Morphological Analyzer.
Tatiana Merzhevich | Fabrício Ferraz Gerardi

This poster presents the first publicly available treebank of Yakut, a Turkic language spoken in Russia, and a morphological analyzer for this language. The treebank was annotated following the Universal Dependencies (UD) framework and the mor- phological analyzer can directly access and use its data. Yakut is an under-represented language whose prominence can be raised by making reliably annotated data and NLP tools that could process it freely accessible. The publication of both the treebank and the analyzer serves this purpose with the prospect of evolving into a benchmark for the development of NLP online tools for other languages of the Turkic family in the future.

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A Language Model for Spell Checking of Educational Texts in Kurdish (Sorani)
Roshna Abdulrahman | Hossein Hassani

Spell checkers are an integrated feature of most software applications handling text inputs. When we write an email or compile a report on a desktop or a smartphone editor, a spell checker could be activated that assists us to write more correctly. However, this assistance does not exist for all languages equally. The Kurdish language, which still is considered a less-resourced language, currently lacks spell checkers for its various dialects. We present a trigram language model for the Sorani dialect of the Kurdish language that is created using educational text. We also showcase a spell checker for the Sorani dialect of Kurdish that can assist in writing texts in the Persian/Arabic script. The spell checker was developed as a testing environment for the language model. Primarily, we use the probabilistic method and our trigram language model with Stupid Backoff smoothing for the spell checking algorithm. Our spell checker has been trained on the KTC (Kurdish Textbook Corpus) dataset. Hence the system aims at assisting spell checking in the related context. We test our approach by developing a text processing environment that checks for spelling errors on a word and context basis. It suggests a list of corrections for misspelled words. The developed spell checker shows 88.54% accuracy on the texts in the related context and it has an F1 score of 43.33%, and the correct suggestion has an 85% chance of being in the top three positions of the corrections.

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SimRelUz: Similarity and Relatedness Scores as a Semantic Evaluation Dataset for Uzbek Language
Ulugbek Salaev | Elmurod Kuriyozov | Carlos Gómez-Rodríguez

Semantic relatedness between words is one of the core concepts in natural language processing, thus making semantic evaluation an important task. In this paper, we present a semantic model evaluation dataset: SimRelUz - a collection of similarity and relatedness scores of word pairs for the low-resource Uzbek language. The dataset consists of more than a thousand pairs of words carefully selected based on their morphological features, occurrence frequency, semantic relation, as well as annotated by eleven native Uzbek speakers from different age groups and gender. We also paid attention to the problem of dealing with rare words and out-of-vocabulary words to thoroughly evaluate the robustness of semantic models.

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ENRICH4ALL: A First Luxembourgish BERT Model for a Multilingual Chatbot
Dimitra Anastasiou

Machine Translation (MT)-empowered chatbots are not established yet, however, we see an amazing future breaking language barriers and enabling conversation in multiple languages without time-consuming language model building and training, particularly for under-resourced languages. In this paper we focus on the under-resourced Luxembourgish language. This article describes the experiments we have done with a dataset containing administrative questions that we have manually created to offer BERT QA capabilities to a multilingual chatbot. The chatbot supports visual dialog flow diagram creation (through an interface called BotStudio) in which a dialog node manages the user question at a specific step. Dialog nodes can be matched to the user’s question by using a BERT classification model which labels the question with a dialog node label.

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bib (full) Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives

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Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives
Eleni Efthimiou | Stavroula-Evita Fotinea | Thomas Hanke | John C. McDonald | Dimitar Shterionov | Rosalee Wolfe

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Synthesis for the Kinematic Control of Identity in Sign Language
Félix Bigand | Elise Prigent | Annelies Braffort

Sign Language (SL) animations generated from motion capture (mocap) of real signers convey critical information about their identity. It has been suggested that this information is mostly carried by statistics of the movements kinematics. Manipulating these statistics in the generation of SL movements could allow controlling the identity of the signer, notably to preserve anonymity. This paper tests this hypothesis by presenting a novel synthesis algorithm that manipulates the identity-specific statistics of mocap recordings. The algorithm produced convincing new versions of French Sign Language discourses, which accurately modulated the identity prediction of a machine learning model. These results open up promising perspectives toward the automatic control of identity in the motion animation of virtual signers.

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Analysis of Torso Movement for Signing Avatar Using Deep Learning
Shatabdi Choudhury

Avatars are virtual or on-screen representations of a human used in various roles for sign language display, including translation and educational tools. Though the ability of avatars to portray acceptable sign language with believable human-like motion has improved in recent years, many still lack the naturalness and supporting motions of human signing. Such details are generally not included in the linguistic annotation. Nevertheless, these motions are highly essential to displaying lifelike and communicative animations. This paper presents a deep learning model for use in a signing avatar. The study focuses on coordinating torso movements and other human body parts. The proposed model will automatically compute the torso rotation based on the avatar’s wrist positions. The resulting motion can improve the user experience and engagement with the avatar.

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Isolated Sign Recognition using ASL Datasets with Consistent Text-based Gloss Labeling and Curriculum Learning
Konstantinos M. Dafnis | Evgenia Chroni | Carol Neidle | Dimitri Metaxas

We present a new approach for isolated sign recognition, which combines a spatial-temporal Graph Convolution Network (GCN) architecture for modeling human skeleton keypoints with late fusion of both the forward and backward video streams, and we explore the use of curriculum learning. We employ a type of curriculum learning that dynamically estimates, during training, the order of difficulty of each input video for sign recognition; this involves learning a new family of data parameters that are dynamically updated during training. The research makes use of a large combined video dataset for American Sign Language (ASL), including data from both the American Sign Language Lexicon Video Dataset (ASLLVD) and the Word-Level American Sign Language (WLASL) dataset, with modified gloss labeling of the latter—to ensure 1-1 correspondence between gloss labels and distinct sign productions, as well as consistency in gloss labeling across the two datasets. This is the first time that these two datasets have been used in combination for isolated sign recognition research. We also compare the sign recognition performance on several different subsets of the combined dataset, varying in, e.g., the minimum number of samples per sign (and therefore also in the total number of sign classes and video examples).

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Example-based Multilinear Sign Language Generation from a Hierarchical Representation
Boris Dauriac | Annelies Braffort | Elise Bertin-Lemée

This article presents an original method for automatic generation of sign language (SL) content by means of the animation of an avatar, with the aim of creating animations that respect as much as possible linguistic constraints while keeping bio-realistic properties. This method is based on the use of a domain-specific bilingual corpus richly annotated with timed alignments between SL motion capture data, text and hierarchical expressions from the framework called AZee at subsentential level. Animations representing new SL content are built from blocks of animations present in the corpus and adapted to the context if necessary. A smart blending approach has been designed that allows the concatenation, replacement and adaptation of original animation blocks. This approach has been tested on a tailored testset to show as a proof of concept its potential in comprehensibility and fluidity of the animation, as well as its current limits.

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Fine-tuning of Convolutional Neural Networks for the Recognition of Facial Expressions in Sign Language Video Samples
Neha Deshpande | Fabrizio Nunnari | Eleftherios Avramidis

In this paper, we investigate the capability of convolutional neural networks to recognize in sign language video frames the six basic Ekman facial expressions for ‘fear’, ‘disgust’, ‘surprise’, ‘sadness’, ‘happiness’, ‘anger’ along with the ‘neutral’ class. Given the limited amount of annotated facial expression data for the sign language domain, we started from a model pre-trained on general-purpose facial expression datasets and we applied various machine learning techniques such as fine-tuning, data augmentation, class balancing, as well as image preprocessing to reach a better accuracy. The models were evaluated using K-fold cross-validation to get more accurate conclusions. It is experimentally demonstrated that fine-tuning a pre-trained model along with data augmentation by horizontally flipping images and image normalization, helps in providing the best accuracy on the sign language dataset. The best setting achieves satisfactory classification accuracy, comparable to state-of-the-art systems in generic facial expression recognition. Experiments were performed using different combinations of the above-mentioned techniques based on two different architectures, namely MobileNet and EfficientNet, and is deemed that both architectures seem equally suitable for the purpose of fine-tuning, whereas class balancing is discouraged.

The direct involvement of deaf users in the development and evaluation of signing avatars is imperative to achieve legibility and raise trust among synthetic signing technology consumers. A paradigm of constructive cooperation between researchers and the deaf community is the EASIER project , where user driven design and technology development have already started producing results. One major goal of the project is the direct involvement of sign language (SL) users at every stage of development of the project’s signing avatar. As developers wished to consider every parameter of SL articulation including affect and prosody in developing the EASIER SL representation engine, it was necessary to develop a steady communication channel with a wide public of SL users who may act as evaluators and can provide guidance throughout research steps, both during the project’s end-user evaluation cycles and beyond. To this end, we have developed a questionnaire-based methodology, which enables researchers to reach signers of different SL communities on-line and collect their guidance and preferences on all aspects of SL avatar animation that are under study. In this paper, we report on the methodology behind the application of the EASIER evaluation framework for end-user guidance in signing avatar development as it is planned to address signers of four SLs -Greek Sign Language (GSL), French Sign Language (LSF), German Sign Language (DGS) and Swiss German Sign Language (DSGS)- during the first project evaluation cycle. We also briefly report on some interesting findings from the pilot implementation of the questionnaire with content from the Greek Sign Language (GSL).

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Improving Signer Independent Sign Language Recognition for Low Resource Languages
Ruth Holmes | Ellen Rushe | Frank Fowley | Anthony Ventresque

The reliance of deep learning algorithms on large scale datasets represents a significant challenge when learning from low resource sign language datasets. This challenge is compounded when we consider that, for a model to be effective in the real world, it must not only learn the variations of a given sign, but also learn to be invariant to the person signing. In this paper, we first illustrate the performance gap between signer-independent and signer-dependent models on Irish Sign Language manual hand shape data. We then evaluate the effect of transfer learning, with different levels of fine-tuning, on the generalisation of signer independent models, and show the effects of different input representations, namely variations in image data and pose estimation. We go on to investigate the sensitivity of current pose estimation models in order to establish their limitations and areas in need of improvement. The results show that accurate pose estimation outperforms raw RGB image data, even when relying on pre-trained image models. Following on from this, we investigate image texture as a potential contributing factor to the gap in performance between signer-dependent and signer-independent models using counterfactual testing images and discuss potential ramifications for low-resource sign languages. Keywords: Sign language recognition, Transfer learning, Irish Sign Language, Low-resource languages

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Improved Facial Realism through an Enhanced Representation of Anatomical Behavior in Sign Language Avatars
Ronan Johnson

Facial movements and expressions are critical features of signed languages, yet are some of the most challenging to reproduce on signing avatars. Due to the relative lack of research efforts in this area, the facial capabilities of such avatars have yet to receive the approval of those in the Deaf community. This paper revisits the representations of the human face in signed avatars, specifically those based on parameterized muscle simulation such as FACS and the MPEG-4 file definition. An improved framework based on rotational pivots and pre-defined movements is capable of reproducing realistic, natural gestures and mouthings on sign language avatars. The new approach is more harmonious with the underlying construction of signed avatars, generates improved results, and allows for a more intuitive workflow for the artists and animators who interact with the system.

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KoSign Sign Language Translation Project: Introducing The NIASL2021 Dataset
Mathew Huerta-Enochian | Du Hui Lee | Hye Jin Myung | Kang Suk Byun | Jun Woo Lee

We introduce a new sign language production (SLP) and sign language translation (SLT) dataset, NIASL2021, consisting of 201,026 Korean-KSL data pairs. KSL translations of Korean source texts are represented in three formats: video recordings, keypoint position data, and time-aligned gloss annotations for each hand (using a 7,989 sign vocabulary) and for eight different non-manual signals (NMS). We evaluated our sign language elicitation methodology and found that text-based prompting had a negative effect on translation quality in terms of naturalness and comprehension. We recommend distilling text into a visual medium before translating into sign language or adding a prompt-blind review step to text-based translation methodologies.

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A Novel Approach to Managing Lower Face Complexity in Signing Avatars
John McDonald | Ronan Johnson | Rosalee Wolfe

An avatar that produces legible, easy-to-understand signing is one of the essential components to an effective automatic signed/spoken translation system. Facial nonmanual signals are essential to natural signing, but unfortunately signing avatars still do not produce acceptable facial expressions, particularly on the lower face. This paper reports on an innovative method to create more realistic lip postures. The approach manages the complexity of creating lip postures, thus making fewer demands on the artists making them. The method will be integral to our efforts to develop libraries containing lip postures to support the generation of facial expressions for several sign languages.

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A Software Toolkit for Pre-processing Sign Language Video Streams
Fabrizio Nunnari

We present the requirements, design guidelines, and the software architecture of an open-source toolkit dedicated to the pre-processing of sign language video material. The toolkit is a collection of functions and command-line tools designed to be integrated with build automation systems. Every pre-processing tool is dedicated to standard pre-processing operations (e.g., trimming, cropping, resizing) or feature extraction (e.g., identification of areas of interest, landmark detection) and can be used also as a standalone Python module. The UML diagrams of its architecture are presented together with a few working examples of its usage. The software is freely available with an open-source license on a public repository.

There has been increasing interest lately in developing education tools for sign language (SL) learning that enable self-assessment and objective evaluation of learners’ SL productions, assisting both students and their instructors. Crucially, such tools require the automatic recognition of SL videos, while operating in a signer-independent fashion and under realistic recording conditions. Here, we present an early version of a Greek Sign Language (GSL) recognizer that satisfies the above requirements, and integrate it within the SL-ReDu learning platform that constitutes a first in GSL with recognition functionality. We develop the recognition module incorporating state-of-the-art deep-learning based visual detection, feature extraction, and classification, designing it to accommodate a medium-size vocabulary of isolated signs and continuously fingerspelled letter sequences. We train the module on a specifically recorded GSL corpus of multiple signers by a web-cam in non-studio conditions, and conduct both multi-signer and signer-independent recognition experiments, reporting high accuracies. Finally, we let student users evaluate the learning platform during GSL production exercises, reporting very satisfactory objective and subjective assessments based on recognition performance and collected questionnaires, respectively.

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Signing Avatars in a New Dimension: Challenges and Opportunities in Virtual Reality
Lorna Quandt | Jason Lamberton | Carly Leannah | Athena Willis | Melissa Malzkuhn

With improved and more easily accessible technology, immersive virtual reality (VR) head-mounted devices have become more ubiquitous. As signing avatar technology improves, virtual reality presents a new and relatively unexplored application for signing avatars. This paper discusses two primary ways that signed language can be represented in immersive virtual spaces: 1) Third-person, in which the VR user sees a character who communicates in signed language; and 2) First-person, in which the VR user produces signed content themselves, tracked by the head-mounted device and visible to the user herself (and/or to other users) in the virtual environment. We will discuss the unique affordances granted by virtual reality and how signing avatars might bring accessibility and new opportunities to virtual spaces. We will then discuss the limitations of signed con-tent in virtual reality concerning virtual signers shown from both third- and first-person perspectives.

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Mouthing Recognition with OpenPose in Sign Language
Maria Del Carmen Saenz

Many avatars focus on the hands and how they express sign language. However, sign language also uses mouth and face gestures to modify verbs, adjectives, or adverbs; these are known as non-manual components of the sign. To have a translation system that the Deaf community will accept, we need to include these non-manual signs. Just as machine learning is being used on generating hand signs, the work we are focusing on will be doing the same, but with mouthing and mouth gestures. We will be using data from The National Center for Sign Language and Gesture Resources. The data from the center are videos of native signers focusing on different areas of signer movement, gesturing, and mouthing, and are annotated specifically for mouthing studies. With this data, we will run a pre-trained Neural Network application called OpenPose. After running through OpenPose, further analysis of the data is conducted using a Random Forest Classifier. This research looks at how well an algorithm can be trained to spot certain mouthing points and output the mouth annotations with a high degree of accuracy. With this, the appropriate mouthing for animated signs can be easily applied to avatar technologies.

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Skeletal Graph Self-Attention: Embedding a Skeleton Inductive Bias into Sign Language Production
Ben Saunders | Necati Cihan Camgöz | Richard Bowden

Recent approaches to Sign Language Production (SLP) have adopted spoken language Neural Machine Translation (NMT) architectures, applied without sign-specific modifications. In addition, these works represent sign language as a sequence of skeleton pose vectors, projected to an abstract representation with no inherent skeletal structure. In this paper, we represent sign language sequences as a skeletal graph structure, with joints as nodes and both spatial and temporal connections as edges. To operate on this graphical structure, we propose Skeletal Graph Self-Attention (SGSA), a novel graphical attention layer that embeds a skeleton inductive bias into the SLP model. Retaining the skeletal feature representation throughout, we directly apply a spatio-temporal adjacency matrix into the self-attention formulation. This provides structure and context to each skeletal joint that is not possible when using a non-graphical abstract representation, enabling fluid and expressive sign language production. We evaluate our Skeletal Graph Self-Attention architecture on the challenging RWTH-PHOENIX-Weather-2014T (PHOENIX14T) dataset, achieving state-of-the-art back translation performance with an 8% and 7% improvement over competing methods for the dev and test sets.

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Multi-track Bottom-Up Synthesis from Non-Flattened AZee Scores
Paritosh Sharma | Michael Filhol

We present an algorithm to improve the pre-existing bottom-up animation system for AZee descriptions to synthesize sign language utterances. Our algorithm allows us to synthesize AZee descriptions by preserving the dynamics of underlying blocks. This bottom-up approach aims to deliver procedurally generated animations capable of generating any sign language utterance if an equivalent AZee description exists. The proposed algorithm is built upon the modules of an open-source animation toolkit and takes advantage of the integrated inverse kinematics solver and a non-linear editor.

This paper presents first steps towards a sign language avatar for communicating railway travel announcements in Dutch Sign Language. Taking an interdisciplinary approach, it demonstrates effective ways to employ co-design and focus group methods in the context of developing sign language technology, and presents several concrete findings and results obtained through co-design and focus group sessions which have not only led to improvements of our own prototype but may also inform the development of signing avatars for other languages and in other application domains.

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Changing the Representation: Examining Language Representation for Neural Sign Language Production
Harry Walsh | Ben Saunders | Richard Bowden

Neural Sign Language Production (SLP) aims to automatically translate from spoken language sentences to sign language videos. Historically the SLP task has been broken into two steps; Firstly, translating from a spoken language sentence to a gloss sequence and secondly, producing a sign language video given a sequence of glosses. In this paper we apply Natural Language Processing techniques to the first step of the SLP pipeline. We use language models such as BERT and Word2Vec to create better sentence level embeddings, and apply several tokenization techniques, demonstrating how these improve performance on the low resource translation task of Text to Gloss. We introduce Text to HamNoSys (T2H) translation, and show the advantages of using a phonetic representation for sign language translation rather than a sign level gloss representation. Furthermore, we use HamNoSys to extract the hand shape of a sign and use this as additional supervision during training, further increasing the performance on T2H. Assembling best practise, we achieve a BLEU-4 score of 26.99 on the MineDGS dataset and 25.09 on PHOENIX14T, two new state-of-the-art baselines.

A recurring concern, oft repeated, regarding the quality of signing avatars is the lack of proper facial movements, particularly in actions that involve mouthing. An analysis uncovered three challenges contributing to the problem. The first is a difficulty in devising an algorithmic strategy for generating mouthing due to the rich variety of mouthings in sign language. For example, part or all of a spoken word may be mouthed depending on the sign language, the syllabic structure of the mouthed word, as well as the register of address and discourse setting. The second challenge was technological. Previous efforts to create avatar mouthing have failed to model the timing present in mouthing or have failed to properly model the mouth’s appearance. The third challenge is one of usability. Previous editing systems, when they existed, were time-consuming to use. This paper describes efforts to improve avatar mouthing by addressing these challenges, resulting in a new approach for mouthing animation. The paper concludes by proposing an experiment in corpus building using the new approach.

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bib (full) Proceedings of the Workshop on Smiling and Laughter across Contexts and the Life-span within the 13th Language Resources and Evaluation Conference

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Proceedings of the Workshop on Smiling and Laughter across Contexts and the Life-span within the 13th Language Resources and Evaluation Conference
Chiara Mazzocconi | Kevin El Haddad | Catherine Pelachaud | Gary McKeown

Smiles are a fundamental facial expression for successful human-agent communication. The growing number of publications in this domain presents an opportunity for future research and design to be informed by a scoping review of the extant literature. This semi-automated review expedites the first steps toward the mapping of Virtual Human (VH) smile research. This paper contributes an overview of the status quo of VH smile research, identifies research streams through cluster analysis, identifies prolific authors in the field, and provides evidence that a full scoping review is needed to synthesize the findings in the expanding domain of VH smile research. To enable collaboration, we provide full access to the refined VH smile dataset, key word and author word clouds, as well as interactive evidence maps.

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Are You Smiling When I Am Speaking?
Auriane Boudin | Roxane Bertrand | Magalie Ochs | Philippe Blache | Stephane Rauzy

The aim of this study is to investigate conversational feedbacks that contain smiles and laughs. Firstly, we propose a statistical analysis of smiles and laughs used as generic and specific feedbacks in a corpus of French talk-in-interaction. Our results show that smiles of low intensity are preferentially used to produce generic feedbacks while high intensity smiles and laughs are preferentially used to produce specific feedbacks. Secondly, based on a machine learning approach, we propose a hierarchical classification of feedback to automatically predict not only the presence/absence of a smile but, also the type of smiles according to an intensity-scale (low or high).

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Gender Differences, Smiling, and Economic Negotiation Outcomes
Paulina Hiersch | Gary McKeown | Ioana Latu | Magdalena Rychlowska

Research documents gender differences in nonverbal behavior and negotiation outcomes. Women tend to smile more often than men and men generally perform better in economic negotiation contexts. Among nonverbal behaviors, smiling can serve various social functions, from rewarding or appeasing others to conveying dominance, and could therefore be extremely useful in economic negotiations. However, smiling has hardly been studied in negotiation contexts. Here we examine links between smiling, gender, and negotiation outcomes. We analyze a corpus of video recordings of participant dyads during mock salary negotiations and test whether women smile more than men and if the amount of smiling can predict economic negotiation outcomes. Consistent with existing literature, women smiled more than men. There was no significant relationship between smiling and negotiation outcomes and gender did not predict negotiation performance. Exploratory analyses showed that expected negotiation outcomes, strongly correlated with actual outcomes, tended to be higher for men than for women. Implications for the gender pay gap and future research are discussed.

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A Measure of the Smiling Synchrony in the Conversational Face-to-face Interaction Corpus PACO-CHEESE
Stéphane Rauzy | Mary Amoyal | Béatrice Priego-Valverde

The smiling synchrony of the French audio-video conversational corpora “PACO” and “Cheese!” is investigated. The two corpora merged altogether last 6 hours and are made of 25 face-to-face dyadic interactions annotated following the 5 levels Smiling Intensity Scale proposed by Gironzetti et al. (2016). After introducing new indicators for characterizing synchrony phenomena, we find that almost all the 25 interactions of PACO-CHEESE show a strong and significant smiling synchrony behavior. We investigate in a second step the evolution of the synchrony parameters throughout the interaction. No effect is found and it appears rather that the smiling synchrony is present at the very start of the interaction and remains unchanged throughout the conversation.

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Analysis of Co-Laughter Gesture Relationship on RGB Videos in Dyadic Conversation Context
Hugo Bohy | Ahmad Hammoudeh | Antoine Maiorca | Stéphane Dupont | Thierry Dutoit

The development of virtual agents has enabled human-avatar interactions to become increasingly rich and varied. Moreover, an expressive virtual agent i.e. that mimics the natural expression of emotions, enhances social interaction between a user (human) and an agent (intelligent machine). The set of non-verbal behaviors of a virtual character is, therefore, an important component in the context of human-machine interaction. Laughter is not just an audio signal, but an intrinsic relationship of multimodal non-verbal communication, in addition to audio, it includes facial expressions and body movements. Motion analysis often relies on a relevant motion capture dataset, but the main issue is that the acquisition of such a dataset is expensive and time-consuming. This work studies the relationship between laughter and body movements in dyadic conversations between two interlocutors. The body movements were extracted from videos using deep learning based pose estimator model. We found that, in the explored NDC-ME dataset, a single statistical feature (i.e, the maximum value, or the maximum of Fourier transform) of a joint movement weakly correlates with laughter intensity by 30%. However, we did not find a direct correlation between audio features and body movements. We discuss about the challenges to use such dataset for the audio-driven co-laughter motion synthesis task.

Background: Laughter is normally viewed as a spontaneous emotional expression of positive internal states; however, it more often serves as an intentional communicative tool, such as showing politeness, agreement and affiliation to others in daily interaction. Although laughter is a universal non-verbal vocalization that promotes social affiliation and maintains social bonds, its presence and usage is understudied in autism research. Limited research has focused on autistic children and found that they used laughter for expressing happiness and mirth, but rarely used it for social purposes compared to their neurotypical (NT) peers. To date, no research has included autistic adults. Objectives: The current study aims to investigate 1) the difference in laughter behaviour between pairs of one autistic and one neurotypical adult (MIXED dyads) and age-, gender- and IQ-matched pairs of two neurotypical adults (NT dyads); 2) whether the closeness of relationship (Friends/Strangers) would influence laughter production between MIXED and NT dyads. Method: In total, 27 autistic and 66 neurotypical adults were recruited and paired into 30 MIXED and 29 NT dyads in the Stranger condition and 7 MIXED dyads and 12 NT dyads in the Friend condition. (We were sadly only able to recruit 4 AUTISM dyads in the Stranger condition and 2 AUTISM dyads in the Friend condition, so these were not included in the analysis.) We filmed all dyads engaged in a funny conversational task and a video-watching task and their laughter behaviour was extracted, quantified and annotated. We calculated the Total duration of laughter, as well as the duration of all Shared laughter in each dyad. Results: Regardless of the closeness of relationship, MIXED dyads produced significantly less Total laughter than NT dyads in both the conversation task and video-watching task. The same tendency was also found for Shared laughter, although participants shared more laughter during video-watching than conversation and this tendency was more pronounced for NT than MIXED dyads. Strikingly, NT dyads produced more shared laughter when interacting with their friend than with a stranger during video-watching task, whilst the amount of shared laughter in MIXED dyads did not differ when interacting with their friend or a stranger. Conclusions: Autistic adults paired with neurotypical adults generally used laughter less as a communicative signal than neurotypical pairs during social interaction. Neurotypical adults pairs specifically produced more shared laughter when interacting with their friend than a stranger, whilst the amount of shared laughter produced by mixed pairs was not affected by the closeness of the relationship. This may indicate that autistic adults show a different pattern of laughter production relative to neurotypical adults during social communication. However, it is also possible that a mismatch between autistic and neurotypical communication, and specifically in existing friendships, may have resulted in patterns of laughter more akin to that seen between strangers. Future research will study shared laughter between pairs of autistic friends to distinguish between these possibilities.

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Intergroup Bias in Smile Discrimination in Autism
Ruihan Wu | Antonia Hamilton | Sarah White

Genuine and posed smiles are important social cues (Song, Over, & Carpenter, 2016). Autistic individuals struggle to reliably differentiate between them (Blampied, Johnston, Miles, & Liberty, 2010; Boraston, Corden, Miles, Skuse, & Blakemore, 2008), which may contribute to their difficulties in understanding others’ mental states. An intergroup bias has been found in non-autistic adults in identifying genuine from posed smiles (Young, 2017). This is the first study designed to investigate if autistic individuals would show a different pattern when differentiating smiles for in-groups and out-groups. Fifty-nine autistic adults were compared with forty non-autistic adults, matched on sex, age and nonverbal IQ. Roughly, half of each group were further randomly separated into two groups with a minimal group paradigm (adapted from Howard & Rothbart, 1980). There was no real difference between the groups, participants were primed to believe they were more similar to their in-groups. The ability to distinguish smiles was assessed on a 7-point Likert scale. We found both autism and non-autism groups rated genuine smiles more genuine than posed smiles and in-groups more genuine than out-groups. Even though both groups identified themselves more as in-group than out-group members, autistic individuals were less likely to than non-autistic individuals. However, autistic participants generally rated smiles as less genuine than non-autistic counterparts. These results indicate that autistic adults are capable of identifying genuine smiles from posed smiles, unlike previous findings; but they may be less convinced of the genuineness of others, which may affect their social communication thereafter. Importantly, autistic adults were equally influenced by social intergroup biases which has the potential to be used in interventions to alleviate their social difficulties in daily lives.

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Inhalation Noises as Endings of Laughs in Conversational Speech
Jürgen Trouvain | Raphael Werner | Khiet Truong

In this study we investigate the role of inhalation noises at the end of laughter events in two conversational corpora that provide relevant annotations. A re-annotation of the categories for laughter, silence and inbreath noises enabled us to see that inhalation noises terminate laughter events in the majority of all inspected laughs with a duration comparable to inbreath noises initiating speech phases. This type of corpus analysis helps to understand the mechanisms of audible respiratory activities in speaking vs. laughing in conversations.

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Are There Any Body-movement Differences between Women and Men When They Laugh?
Ahmad Hammoudeh | Antoine Maiorca | Stéphane Dupont | Thierry Dutoit

Smiling differences between men and women have been studied in psychology. Women smile more than men although the expressiveness of women is not universally more across all facial actions. There are also body movement differences between women and men. For example, more open-body postures were reported for men, but are there any body-movement differences between men and women when they laugh? To investigate this question, we study body-movement signals extracted from recorded laughter videos using a deep learning pose estimation model. Initial results showed a higher Fourier Transform amplitude of thorax and shoulder movements for females while males had a higher Fourier transform amplitude of Elbow movement. The differences were not limited to a small frequency range but covered most of the frequency spectrum. However, further investigations are still needed.

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Laughter During Cooperative and Competitive Games
Magdalena Rychlowska | Gary McKeown | Ian Sneddon | William Curran

This exploratory study investigates the extent to which social context influences the frequency of laughter. In a within-subjects design, dyads of strangers played two simple laughter-inducing games in a cooperative and competitive setting, ostensibly to earn money individually and as a team. We examined the frequency of laughs produced in both settings. The analysis revealed that, the effects of cooperative versus competitive framing interacted with the game. Specifically, when playing a general knowledge quiz, participants tended to laugh more in the cooperative than in the competitive setting. However, the opposite was true when participants were asked to find a specific number of poker chips under time pressure. During this task participants laughed more in a competitive than in the cooperative setting. Further analyses revealed that familiarity with the task affected the amount of laughter differently for each of the two tasks. Playing the second round of the poker chips task was associated with a significant decreases in laughter frequency compared to the first round. This effect was less marked for the general knowledge quiz, where increased familiarity with the task in the second round led to more laughs in the cooperative, but not competitive setting. Together, the results highlight the flexibility of laughter as an interaction signal and illustrate the challenges of studying laughter in naturalistic settings.

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bib (full) Proceedings of the Workshop Towards Digital Language Equality within the 13th Language Resources and Evaluation Conference

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Proceedings of the Workshop Towards Digital Language Equality within the 13th Language Resources and Evaluation Conference
Itziar Aldabe | Begoña Altuna | Aritz Farwell | German Rigau

This paper introduces the concept of Digital Language Equality (DLE) developed by the EU-funded European Language Equality (ELE) project, and describes the associated DLE Metric with a focus on its technological factors (TFs), which are complemented by situational contextual factors. This work aims at objectively describing the level of technological support of all European languages and lays the foundation to implement a large-scale EU-wide programme to ensure that these languages can continue to exist and prosper in the digital age, to serve the present and future needs of their speakers. The paper situates this ongoing work with a strong European focus in the broader context of related efforts, and explains how the DLE Metric can help track the progress towards DLE for all languages of Europe, focusing in particular on the role played by the TFs. These are derived from the European Language Grid (ELG) Catalogue, that provides the empirical basis to measure the level of digital readiness of all European languages. The DLE Metric scores can be consulted through an online interactive dashboard to show the level of technological support of each European language and track the overall progress toward DLE.

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Introducing the Digital Language Equality Metric: Contextual Factors
Annika Grützner-Zahn | Georg Rehm

In our digital age, digital language equality is an important goal to enable participation in society for all citizens, independent of the language they speak. To assess the current state of play with regard to Europe’s languages, we developed, in the project European Language Equality, a metric for digital language equality that consists of two parts, technological and contextual (i.e., non-technological) factors. We present a metric for calculating the contextual factors for over 80 European languages. For each language, a score is calculated that reflects the broader context or socio-economic ecosystem of a language, which has, for a given language, a direct impact for technology and resource development; it is important to note, though, that Language Technologies and Resources related aspects are reflected by the technological factors. To reduce the vast number of potential contextual factors to an adequate number, five different configurations were calculated and evaluated with a panel of experts. The best results were achieved by a configuration in which 12 manually curated factors were included. In the factor selection process, attention was paid to data quality, automatic updatability, inclusion of data from different domains, and a balance between different data types. The evaluation shows that this specific configuration is stable for the official EU languages; while for regional and minority languages, as well as national non-official EU languages, there is room for improvement.

The European Language Equality (ELE) project develops a strategic research, innovation and implementation agenda (SRIA) and a roadmap for achieving full digital language equality in Europe by 2030. Key component of the SRIA development is an accurate estimation of the current standing of languages with respect to their technological readiness. In this paper we present the empirical basis on which such estimation is grounded, its starting point and in particular the automatic and collaborative methods used for extending it. We focus on the collaborative expert activities, the challenges posed, and the solutions adopted. We also briefly present the dashboard application developed for querying and visualising the empirical data as well as monitoring and comparing the evolution of technological support within and across languages.

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Measuring HLT Research Equality of European Languages
Gorka Artola | German Rigau

This work explores quantitative indicators that could potentially measure the equality and inequality research levels among the languages of the European Union in the field of human language technologies (HLT research equality). Our ultimate goal is to investigate European language equality in HLT research considering the number of papers published on several HLT research venues that mention each language with respect to their estimated number of speakers. This way, inequalities affecting HLT research in Europe will depend on other factors such as history, political status, GDP, level of social or technological development, etc. We have identified several groups of EU languages in the proposed measurement of HLT research equality, each group comprising languages with large differences in the number of speakers. We have discovered a relative equality among surprisingly different languages in terms of number of speakers and also relevant inequalities within the most spoken languages. All data and code will be released upon acceptance.

This article presents the work in progress on the collaborative project of several European countries to develop National Language Technology Platform (NLTP). The project aims at combining the most advanced Language Technology tools and solutions in a new, state-of-the-art, Artificial Intelligence driven, National Language Technology Platform for five EU/EEA official and lower-resourced languages.

The development of language technologies (LTs) such as machine translation, text analytics, and dialogue systems is essential in the current digital society, culture and economy. These LTs, widely supported in languages in high demand worldwide, such as English, are also necessary for smaller and less economically powerful languages, as they are a driving force in the democratization of the communities that use them due to their great social and cultural impact. As an example, dialogue systems allow us to communicate with machines in our own language; machine translation increases access to contents in different languages, thus facilitating intercultural relations; and text-to-speech and speech-to-text systems broaden different categories of users’ access to technology. In the case of Galician (co-official language, together with Spanish, in the autonomous region of Galicia, located in northwestern Spain), incorporating the language into state-of-the-art AI applications can not only significantly favor its prestige (a decisive factor in language normalization), but also guarantee citizens’ language rights, reduce social inequality, and narrow the digital divide. This is the main motivation behind the Nós Project (Proxecto Nós), which aims to have a significant contribution to the development of LTs in Galician (currently considered a low-resource language) by providing openly licensed resources, tools, and demonstrators in the area of intelligent technologies.

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bib (full) Proceedings of the Workshop on Terminology in the 21st century: many faces, many places

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Proceedings of the Workshop on Terminology in the 21st century: many faces, many places
Rute Costa | Sara Carvalho | Ana Ostroški Anić | Anas Fahad Khan

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Lexicon-driven approach for Terminology: specialized resources on the environment in Brazilian Portuguese
Flávia Lamberti Arraes

This paper presents a terminological research carried out to account for terms of the environment in Brazilian Portuguese based on a lexico-semantic perspective for Terminology (L’Homme, 2015, 2016, 2017, 2020; L’Homme et al., 2014, 2020). This work takes place in the context of a collaboration for the development of DiCoEnviro (Dictionnaire Fondamental de l’Environnment – Fundamental Dictionary on the environment), a multilingual terminological resource developed by the Observatoire de Linguistique Sens Texte at the University of Montreal, Canada. By following a methodolgy especially devised to develop terminological work based on a lexicon-driven approach (L’Homme et al., 2020), the terminological analysis reveals how the linguistic behavior of terms may be unveiled and how this is effective for identifying the meaning of a term and supporting meaning distinctions.

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Knowledge Representation and Language Simplification of Human Rights
Sara Silecchia | Federica Vezzani | Giorgio Maria Di Nunzio

In this paper, we propose the description of a very recent interdisciplinary project aiming at analysing both the conceptual and linguistic dimensions of humanitarian rights terminology. This analysis will result in the form of a new knowledge-based multilingual terminological resource which is designed in order to meet the FAIR principles for Open Science and will serve, in the future, as a prototype for the development of a new software for the simplified rewriting of international legal texts relating to human rights. Given the early stage of the project, we will focus on the description of its rationale, the planned workflow, and the theoretical approach which will be adopted to achieve the main goal of this ambitious research project.

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Converting from the Nordic Terminological Record Format to the TBX Format
Maria Skeppstedt | Marie Mattson | Magnus Ahltorp | Rickard Domeij

Rikstermbanken (Sweden’s National Term Bank), which was launched in 2009, uses the Nordic Terminological Record Format (NTRF) for organising its terminological data. Since then, new terminology formats have been established as standards, e.g., the Termbase eXchange format (TBX). We here describe work carried out by the Institute for Language and Folklore within the Federated eTranslation TermBank Network Action. This network develops a technical infrastructure for facilitating sharing of terminology resources throughout Europe. To be able to share some of the term collections of Rikstermbanken within this network and export them to Eurotermbank, we have implemented a conversion from the Nordic Terminological Record Format, as used in Rikstermbanken, to the TBX format.

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A Dataset for Term Extraction in Hindi
Shubhanker Banerjee | Bharathi Raja Chakravarthi | John Philip McCrae

Automatic Term Extraction (ATE) is one of the core problems in natural language processing and forms a key component of text mining pipelines of domain specific corpora. Complex low-level tasks such as machine translation and summarization for domain specific texts necessitate the use of term extraction systems. However, the development of these systems requires the use of large annotated datasets and thus there has been little progress made on this front for under-resourced languages. As a part of ongoing research, we present a dataset for term extraction from Hindi texts in this paper. To the best of our knowledge, this is the first dataset that provides term annotated documents for Hindi. Furthermore, we have evaluated this dataset on statistical term extraction methods and the results obtained indicate the problems associated with development of term extractors for under-resourced languages.

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Terminology extraction using co-occurrence patterns as predictors of semantic relevance
Rogelio Nazar | David Lindemann

We propose a method for automatic term extraction based on a statistical measure that ranks term candidates according to their semantic relevance to a specialised domain. As a measure of relevance we use term co-occurrence, defined as the repeated instantiation of two terms in the same sentences, in indifferent order and at variable distances. In this way, term candidates are ranked higher if they show a tendency to co-occur with a selected group of other units, as opposed to those showing more uniform distributions. No external resources are needed for the application of the method, but performance improves when provided with a pre-existing term list. We present results of the application of this method to a Spanish-English Linguistics corpus, and the evaluation compares favourably with a standard method based on reference corpora.

In the experiments briefly presented in this abstract, we compare the performance of a generalist Swedish pre-trained language model with a domain-specific Swedish pre-trained model on the downstream task of focussed terminology extraction of implant terms, which are terms that indicate the presence of implants in the body of patients. The fine-tuning is identical for both models. For the search strategy we rely on KD-Tree that we feed with two different lists of term seeds, one with noise and one without noise. Results shows that the use of a domain-specific pre-trained language model has a positive impact on focussed terminology extraction only when using term seeds without noise.

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D-Terminer: Online Demo for Monolingual and Bilingual Automatic Term Extraction
Ayla Rigouts Terryn | Veronique Hoste | Els Lefever

This contribution presents D-Terminer: an open access, online demo for monolingual and multilingual automatic term extraction from parallel corpora. The monolingual term extraction is based on a recurrent neural network, with a supervised methodology that relies on pretrained embeddings. Candidate terms can be tagged in their original context and there is no need for a large corpus, as the methodology will work even for single sentences. With the bilingual term extraction from parallel corpora, potentially equivalent candidate term pairs are extracted from translation memories and manual annotation of the results shows that good equivalents are found for most candidate terms. Accompanying the release of the demo is an updated version of the ACTER Annotated Corpora for Term Extraction Research (version 1.5).

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bib (full) Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference

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Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference
Girish Nath Jha | Sobha L. | Kalika Bali | Atul Kr. Ojha

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Introducing EM-FT for Manipuri-English Neural Machine Translation
Rudali Huidrom | Yves Lepage

This paper introduces a pretrained word embedding for Manipuri, a low-resourced Indian language. The pretrained word embedding based on FastText is capable of handling the highly agglutinating language Manipuri (mni). We then perform machine translation (MT) experiments using neural network (NN) models. In this paper, we confirm the following observations. Firstly, the reported BLEU score of the Transformer architecture with FastText word embedding model EM-FT performs better than without in all the NMT experiments. Secondly, we observe that adding more training data from a different domain of the test data negatively impacts translation accuracy. The resources reported in this paper are made available in the ELRA catalogue to help the low-resourced languages community with MT/NLP tasks.

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L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models
Ravindra Nayak | Raviraj Joshi

Code-switching occurs when more than one language is mixed in a given sentence or a conversation. This phenomenon is more prominent on social media platforms and its adoption is increasing over time. Therefore code-mixed NLP has been extensively studied in the literature. As pre-trained transformer-based architectures are gaining popularity, we observe that real code-mixing data are scarce to pre-train large language models. We present L3Cube-HingCorpus, the first large-scale real Hindi-English code mixed data in a Roman script. It consists of 52.93M sentences and 1.04B tokens, scraped from Twitter. We further present HingBERT, HingMBERT, HingRoBERTa, and HingGPT. The BERT models have been pre-trained on codemixed HingCorpus using masked language modelling objectives. We show the effectiveness of these BERT models on the subsequent downstream tasks like code-mixed sentiment analysis, POS tagging, NER, and LID from the GLUECoS benchmark. The HingGPT is a GPT2 based generative transformer model capable of generating full tweets. Our models show significant improvements over currently available models pre-trained on multiple languages and synthetic code-mixed datasets. We also release L3Cube-HingLID Corpus, the largest code-mixed Hindi-English language identification(LID) dataset and HingBERT-LID, a production-quality LID model to facilitate capturing of more code-mixed data using the process outlined in this work. The dataset and models are available at https://github.com/l3cube-pune/code-mixed-nlp.

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Leveraging Sub Label Dependencies in Code Mixed Indian Languages for Part-Of-Speech Tagging using Conditional Random Fields.
Akash Kumar Gautam

Code-mixed text sequences often lead to challenges in the task of correct identification of Part-Of-Speech tags. However, lexical dependencies created while alternating between multiple languages can be leveraged to improve the performance of such tasks. Indian languages with rich morphological structure and highly inflected nature provide such an opportunity. In this work, we exploit these sub-label dependencies using conditional random fields (CRFs) by defining feature extraction functions on three distinct language pairs (Hindi-English, Bengali-English, and Telugu-English). Our results demonstrate a significant increase in the tagging performance if the feature extraction functions employ the rich inner structure of such languages.

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HindiWSD: A package for word sense disambiguation in Hinglish & Hindi
Mirza Yusuf | Praatibh Surana | Chethan Sharma

A lot of commendable work has been done, especially in high resource languages such as English, Spanish, French, etc. However, work done for Indic languages such as Hindi, Tamil, Telugu, etc is relatively less due to difficulty in finding relevant datasets, and the complexity of these languages. With the advent of IndoWordnet, we can explore important tasks such as word sense disambiguation, word similarity, and cross-lingual information retrieval, and carry out effective research regarding the same. In this paper, we worked on improving word sense disambiguation for 20 of the most common ambiguous Hindi words by making use of knowledge-based methods. We also came up with “hindiwsd”, an easy-to-use framework developed in Python that acts as a pipeline for transliteration of Hinglish code-mixed text followed by spell correction, POS tagging, and word sense disambiguation of Hindi text. We also curated a dataset of these 20 most used ambiguous Hindi words. This dataset was then used to enhance a modified Lesk’s algorithm and more accurately carry out word sense disambiguation. We achieved an accuracy of about 71% using our customized Lesk’s algorithm which was an improvement to the accuracy of about 34% using the original Lesk’s algorithm on the test set.

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Pāṇinian Phonological Changes: Computation and Development of Online Access System
Sanju | Subhash Chandra

Pāṇini used the term saṃhitā for phonological changes. Any Sound change which alters phonemes in a particular language is called Phonological Change. It arises when two sounds are pronounced in a language with uninterrupted speed, then those letters are affected by each other due to Articulatory, Acoustic and Auditory principles in language. The pronunciation of two sounds that are in extreme proximity, affects each other and changes them. In simple words, this phenomenon is known as sandhi. Sanskrit is considered one of the oldest languages in the world. It has produced one of the most huge literary text corpora in the world. The tradition of Sanskrit started in the Vedic period. Pāṇini’s Aṣṭādhyāyī (AD) is a complete grammar of Sanskrit. It also covers Sanskrit sounds and phonology. Phonological changes are a natural phenomenon in any language during speech but in Sanskrit, it is highly reflected. Sanskrit corpora contain numerous long words. It looks like a single sentence due to sandhi between multiple words. The process of phonological changes occurred based on certain rules of pronunciation and it is codified by the Pāṇini in AD. Pāṇini has codified these rules systemically but the computation of these rules is a challenging task. Therefore, the objective of the paper is to compute the rules and demonstrate an online access system for Sanskrit sandhi. The system also generates the whole process of phonological changes based on Pāṇinian Rules. It also plays a very effective role in Digital classroom teaching, boosting teaching skills and the learning process.

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L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models
Onkar Litake | Maithili Ravindra Sabane | Parth Sachin Patil | Aparna Abhijeet Ranade | Raviraj Joshi

Named Entity Recognition (NER) is a basic NLP task and finds major applications in conversational and search systems. It helps us identify key entities in a sentence used for the downstream application. NER or similar slot filling systems for popular languages have been heavily used in commercial applications. In this work, we focus on Marathi, an Indian language, spoken prominently by the people of Maharashtra state. Marathi is a low resource language and still lacks useful NER resources. We present L3Cube-MahaNER, the first major gold standard named entity recognition dataset in Marathi. We also describe the manual annotation guidelines followed during the process. In the end, we benchmark the dataset on different CNN, LSTM, and Transformer based models like mBERT, XLM-RoBERTa, IndicBERT, MahaBERT, etc. The MahaBERT provides the best performance among all the models. The data and models are available at https://github.com/l3cube-pune/MarathiNLP .

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Identifying Emotions in Code Mixed Hindi-English Tweets
Sanket Sonu | Rejwanul Haque | Mohammed Hasanuzzaman | Paul Stynes | Pramod Pathak

Emotion detection (ED) in tweets is a text classification problem that is of interest to Natural Language Processing (NLP) researchers. Code-mixing (CM) is a process of mixing linguistic units such as words of two different languages. The CM languages are characteristically different from the languages whose linguistic units are used for mixing. Whilst NLP has been shown to be successful for low-resource languages, it becomes challenging to perform NLP tasks on CM languages. As for ED, it has been rarely investigated on CM languages such as Hindi—English due to the lack of training data that is required for today’s data-driven classification algorithms. This research proposes a gold standard dataset for detecting emotions in CM Hindi–English tweets. This paper also presents our results about the investigation of the usefulness of our gold-standard dataset while testing a number of state-of-the-art classification algorithms. We found that the ED classifier built using SVM provided us the highest accuracy (75.17%) on the hold-out test set. This research would benefit the NLP community in detecting emotions from social media platforms in multilingual societies.

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Digital Accessibility and Information Mining of Dharmaśāstric Knowledge Traditions
Arooshi Nigam | Subhash Chandra

The heritage of Dharmaśāstra (DS) carries extensive cultural history and encapsulates the treatises of Ancient Indian Social Institutions (SI). DS is reckoned as an epitome of the primitive Indian knowledge tradition as it incorporates a variety of genres for sciences and arts such as family law and legislation, civilization, culture, ritualistic procedures, environment, economics, commerce and finance studies, management, mathematical and medical sciences etc. SI represents a distinct tradition of civilization formation, society development and community living. The texts of the DS are primarily written in the Sanskrit language and due to its expansive subject stream, it is later translated into various other languages globally. With the ingress of the internet, the development of advanced digital technologies and IT boom, information is accessed and exchanged via digital platforms. DS texts are studied not only by Sanskrit scholars but also referred by historians, sociologists, political scientists, economists, law enthusiasts and linguists worldwide. Despite its eminence, there is a major setback in digitizing and online information mining for DS texts. The major objective of the paper is to digitize and develop an instant referencing system to amplify the digital accessibility of DS texts. This will act as an effective and immediate learning tool for researchers who are keen on intensive studying of DS concepts.

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Language Resource Building and English-to-Mizo Neural Machine Translation Encountering Tonal Words
Vanlalmuansangi Khenglawt | Sahinur Rahman Laskar | Santanu Pal | Partha Pakray | Ajoy Kumar Khan

Multilingual country like India has an enormous linguistic diversity and has an increasing demand towards developing language resources such that it will outreach in various natural language processing applications like machine translation. Low-resource language translation possesses challenges in the field of machine translation. The challenges include the availability of corpus and differences in linguistic information. This paper investigates a low-resource language pair, English-to-Mizo exploring neural machine translation by contributing an Indian language resource, i.e., English-Mizo corpus. In this work, we explore one of the main challenges to tackling tonal words existing in the Mizo language, as they add to the complexity on top of low-resource challenges for any natural language processing task. Our approach improves translation accuracy by encountering tonal words of Mizo and achieved a state-of-the-art result in English-to-Mizo translation.

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Classification of Multiword Expressions in Malayalam
Treesa Cyriac | Sobha Lalitha Devi

Multiword expression is an interesting concept in languages and the MWEs of a language are not easy for a non-native speaker to understand. It includes lexicalized phrases, idioms, collocations etc. Data on multiwords are helpful in language processing. ‘Multiword expressions in Malayalam’ is a less studied area. In this paper, we are trying to explore multiwords in Malayalam and to classify them as per the three idiosyncrasies: semantic idiosyncrasy, syntactic idiosyncrasy, and statistic idiosyncrasy. Though these are already identified, they are not being studied in Malayalam. The classification and features are given and are studied using Malayalam multiwords. Through this study, we identified how the linguistic features of Malayalam such as agglutination influence its multiword expressions in terms of pronunciation and spelling. Malayalam has a set of code-mixed multiword expressions which is also addressed in this study.

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Bengali and Magahi PUD Treebank and Parser
Pritha Majumdar | Deepak Alok | Akanksha Bansal | Atul Kr. Ojha | John P. McCrae

This paper presents the development of the Parallel Universal Dependency (PUD) Treebank for two Indo-Aryan languages: Bengali and Magahi. A treebank of 1,000 sentences has been created using a parallel corpus of English and the UD framework. A preliminary set of sentences was annotated manually - 600 for Bengali and 200 for Magahi. The rest of the sentences were built using the Bengali and Magahi parser. The sentences have been translated and annotated manually by the authors, some of whom are also native speakers of the languages. The objective behind this work is to build a syntactically-annotated linguistic repository for the aforementioned languages, that can prove to be a useful resource for building further NLP tools. Additionally, Bengali and Magahi parsers were also created which is built on machine learning approach. The accuracy of the Bengali parser is 78.13% in the case of UPOS; 76.99% in the case of XPOS, 56.12% in the case of UAS; and 47.19% in the case of LAS. The accuracy of Magahi parser is 71.53% in the case of UPOS; 66.44% in the case of XPOS, 58.05% in the case of UAS; and 33.07% in the case of LAS. This paper also includes an illustration of the annotation schema followed, the findings of the Parallel Universal Dependency (PUD) treebank, and it’s resulting linguistic analysis

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Makadi: A Large-Scale Human-Labeled Dataset for Hindi Semantic Parsing
Shashwat Vaibhav | Nisheeth Srivastava

Parsing natural language queries into formal database calls is a very well-studied problem. Because of the rich diversity of semantic markers across the world’s languages, progress in solving this problem is irreducibly language-dependent. This has created an asymmetry in progress in NLIDB solutions, with most state-of-the-art efforts focused on the resource-rich English language, with limited progress seen for low resource languages. In this short paper, we present Makadi, a large-scale, complex, cross-lingual, cross-domain semantic parsing and text-to-SQL dataset for semantic parsing in the Hindi language. Produced by translating the recently introduced English language Spider NLIDB dataset, it consists of 9693 questions and SQL queries on 166 databases with multiple tables which cover multiple domains. This is the first large-scale dataset in the Hindi language for semantic parsing and related language understanding tasks. Our dataset is publicly available at: Link removed to preserve anonymization during peer review.

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Automatic Identification of Explicit Connectives in Malayalam
Kumari Sheeja S | Sobha Lalitha Devi

This work presents an automatic identification of explicit connectives and its arguments using supervised method, Conditional Random Fields (CRFs). In this work, we focus on the identification of connectives and their arguments in the corpus. We consider explicit connectives and its arguments for the present study. The corpus we have considered has 4,000 sentences from Malayalam documents and manually annotated the corpus for POS, chunk, clause, discourse connectives and its arguments. The corpus thus annotated is used for building the base engine. The percentage of the performance of the system is evaluated based on the precision, recall and F-score and obtained encouraging results. We have analysed the errors generated by the system and used the features obtained from the anlaysis to improve the performance of the system

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Web based System for Derivational Process of Kṛdanta based on Pāṇinian Grammatical Tradition
Sumit Sharma | Subhash Chandra

Each text of the Sanskrit literature is wadded with the uses of Sanskrit kṛdanta (participles). The knowledge and formation process of Sanskrit kṛdanta play a key role to understand the meaning of a particular kṛdanta word in Sanskrit. Without proper analysis of the kṛdanta, the Sanskrit text cannot be understood. Currently, the mode of Sanskrit learning is traditional classroom teaching which is accessible to the students but not to general Sanskrit learners. The acute growth of Information Technology (IT) is changed the educational pedagogy and web-based learning systems evolved to enhance the teaching-learning process. Though many online tools are being developed by researchers for Sanskrit these are still scarce and untasted. Globe genuinely demands the high impacted tools for Sanskrit. Undoubtedly, Sanskrit kṛdanta is part of the syllabus of all universities offering Sanskrit courses. Approximately 100 plus kṛt suffixes are added with verb roots to generate kṛdanta forms and due to complexity, the learning of these forms is a challenging task. Therefore, the objective of the paper is to present an online system for teaching the derivational process of kṛdantas based on Pāṇinian rules and generate a complete derivational process of the kṛdantas for teaching and learning. It will also provide a platform for e-learning for the derivational process of Sanskrit kṛdantas.

This paper presents the first publicly available treebank of Odia, a morphologically rich low resource Indian language. The treebank contains approx. 1082 tokens (100 sentences) in Odia were selected from “Samantar”, the largest available parallel corpora collection for Indic languages. All the selected sentences are manually annotated following the “Universal Dependency” guidelines. The morphological analysis of the Odia treebank was performed using machine learning techniques. The Odia annotated treebank will enrich the Odia language resource and will help in building language technology tools for cross-lingual learning and typological research. We also build a preliminary Odia parser using a machine learning approach. The accuracy of the parser is 86.6% Tokenization, 64.1% UPOS, 63.78% XPOS, 42.04% UAS and 21.34% LAS. Finally, the paper briefly discusses the linguistic analysis of the Odia UD treebank.

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Computational Referencing System for Sanskrit Grammar
Baldev Khandoliyan | Ram Kishor

The goal of this project was to reconstitute and storage the text of Aṣṭādhyāyī (AD) in a computer text system so that everyone may read it. The proposed work was to do study the structure of AD and to create a relational database system for storing and interacting with AD. The system is available online, including Devanāgari Unicode and other major Indian characters as input and output, MS SQL Server, a Relational Database Management System (RDBMS)-based system, and Java Server Pages (JSP) were used. For AD, the system works as a multi-dimensional interactive knowledge-based computer system. The approach can also be applied to all Sanskrit sūtra texts that have a similar format. Sanskrit heritage texts are projected to benefit from the system’s preservation and promotion. A research is being made here for preparing an AD text as a computer aided dynamic search, learning and instruction system in the Indian context.

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L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources
Raviraj Joshi

We present L3Cube-MahaCorpus a Marathi monolingual data set scraped from different internet sources. We expand the existing Marathi monolingual corpus with 24.8M sentences and 289M tokens. We further present, MahaBERT, MahaAlBERT, and MahaRoBerta all BERT-based masked language models, and MahaFT, the fast text word embeddings both trained on full Marathi corpus with 752M tokens. We show the effectiveness of these resources on downstream Marathi sentiment analysis, text classification, and named entity recognition (NER) tasks. We also release MahaGPT, a generative Marathi GPT model trained on Marathi corpus. Marathi is a popular language in India but still lacks these resources. This work is a step forward in building open resources for the Marathi language. The data and models are available at https://github.com/l3cube-pune/MarathiNLP .