Temporal notions based on a finite set A of properties are represented in strings, on which projections are defined that vary the granularity A. The structure of properties in A is elaborated to describe statives, events and actions, subject to a distinction in meaning (advocated by Levin and Rappaport Hovav) between what the lexicon prescribes and what a context of use supplies. The projections proposed are deployed as labels for records and record types amenable to finite-state methods.
A growing interest in tasks involving language understanding by the NLP community has led to the need for effective semantic parsing and inference. Modern NLP systems use semantic representations that do not quite fulfill the nuanced needs for language understanding: adequately modeling language semantics, enabling general inferences, and being accurately recoverable. This document describes underspecified logical forms (ULF) for Episodic Logic (EL), which is an initial form for a semantic representation that balances these needs. ULFs fully resolve the semantic type structure while leaving issues such as quantifier scope, word sense, and anaphora unresolved; they provide a starting point for further resolution into EL, and enable certain structural inferences without further resolution. This document also presents preliminary results of creating a hand-annotated corpus of ULFs for the purpose of training a precise ULF parser, showing a three-person pairwise interannotator agreement of 0.88 on confident annotations. We hypothesize that a divide-and-conquer approach to semantic parsing starting with derivation of ULFs will lead to semantic analyses that do justice to subtle aspects of linguistic meaning, and will enable construction of more accurate semantic parsers.
This paper describes in brief the proposal called ‘QuantML’ which was accepted by the International Organisation for Standards (ISO) last February as a starting point for developing a standard for the interoperable annotation of quantification phenomena in natural language, as part of the ISO 24617 Semantic Annotation Framework. The proposal, firmly rooted in the theory of generalised quantifiers, neo-Davidsonian semantics, and DRT, covers a wide range of quantification phenomena. The QuantML scheme consists of (1) an abstract syntax which defines ‘annotation structures’ as triples and other set-theoretic constructs; (b) a compositional semantics of annotation structures; (3) an XML representation of annotation structures.
Topics models, such as LDA, are widely used in Natural Language Processing. Making their output interpretable is an important area of research with applications to areas such as the enhancement of exploratory search interfaces and the development of interpretable machine learning models. Conventionally, topics are represented by their n most probable words, however, these representations are often difficult for humans to interpret. This paper explores the re-ranking of topic words to generate more interpretable topic representations. A range of approaches are compared and evaluated in two experiments. The first uses crowdworkers to associate topics represented by different word rankings with related documents. The second experiment is an automatic approach based on a document retrieval task applied on multiple domains. Results in both experiments demonstrate that re-ranking words improves topic interpretability and that the most effective re-ranking schemes were those which combine information about the importance of words both within topics and their relative frequency in the entire corpus. In addition, close correlation between the results of the two evaluation approaches suggests that the automatic method proposed here could be used to evaluate re-ranking methods without the need for human judgements.
This paper builds on previous work using Combinatory Categorial Grammar (CCG) to derive a transparent syntax-semantics interface for Abstract Meaning Representation (AMR) parsing. We define new semantics for the CCG combinators that is better suited to deriving AMR graphs. In particular, we define relation-wise alternatives for the application and composition combinators: these require that the two constituents being combined overlap in one AMR relation. We also provide a new semantics for type raising, which is necessary for certain constructions. Using these mechanisms, we suggest an analysis of eventive nouns, which present a challenge for deriving AMR graphs. Our theoretical analysis will facilitate future work on robust and transparent AMR parsing using CCG.
This paper presents a semantic annotation scheme for Danish adjectives, focusing both on prototypical semantic content and semantic collocational restrictions on an adjective’s head noun. The core type set comprises about 110 categories ordered in a shallow hierarchy with 14 primary and 25 secondary umbrella categories. In addition, domain information and binary sentiment tags are provided, as well as VerbNet-derived frames and semantic roles for those adjectives governing arguments. The scheme has been almost fully implemented on the lexicon of the Danish VISL parser, DanGram, containing 14,000 adjectives. We discuss the annotation scheme and its applicational perspectives, and present a statistical breakdown and coverage evaluation for three Danish reference corpora.
Complex predicates formed of a semantically ‘light’ verbal head and a noun or verb which contributes the major part of the meaning are frequently referred to as ‘light verb constructions’ (LVCs). In the paper, we present a case study of LVCs with the German posture verb stehen ‘stand’. In our account, we model the syntactic as well as semantic composition of such LVCs by combining Lexicalized Tree Adjoining Grammar (LTAG) with frames. Starting from the analysis of the literal uses of posture verbs, we show how the meaning components of the literal uses are systematically exploited in the interpretation of stehen-LVCs. The paper constitutes an important step towards a compositional and computational analysis of LVCs. We show that LTAG allows us to separate constructional from lexical meaning components and that frames enable elegant generalizations over event types and related constraints.
Distributional Semantic Models (DSMs) construct vector representations of word meanings based on their contexts. Typically, the contexts of a word are defined as its closest neighbours, but they can also be retrieved from its syntactic dependency relations. In this work, we propose a new dependency-based DSM. The novelty of our model lies in associating an independent meaning representation, a matrix, with each dependency-label. This allows it to capture specifics of the relations between words and contexts, leading to good performance on both intrinsic and extrinsic evaluation tasks. In addition to that, our model has an inherent ability to represent dependency chains as products of matrices which provides a straightforward way of handling further contexts of a word.
Inferences regarding “Jane’s arrival in London” from predications such as “Jane is going to London” or “Jane has gone to London” depend on tense and aspect of the predications. Tense determines the temporal location of the predication in the past, present or future of the time of utterance. The aspectual auxiliaries on the other hand specify the internal constituency of the event, i.e. whether the event of “going to London” is completed and whether its consequences hold at that time or not. While tense and aspect are among the most important factors for determining natural language inference, there has been very little work to show whether modern embedding models capture these semantic concepts. In this paper we propose a novel entailment dataset and analyse the ability of contextualised word representations to perform inference on predications across aspectual types and tenses. We show that they encode a substantial amount of information relating to tense and aspect, but fail to consistently model inferences that require reasoning with these semantic properties.
Distributional semantics has had enormous empirical success in Computational Linguistics and Cognitive Science in modeling various semantic phenomena, such as semantic similarity, and distributional models are widely used in state-of-the-art Natural Language Processing systems. However, the theoretical status of distributional semantics within a broader theory of language and cognition is still unclear: What does distributional semantics model? Can it be, on its own, a fully adequate model of the meanings of linguistic expressions? The standard answer is that distributional semantics is not fully adequate in this regard, because it falls short on some of the central aspects of formal semantic approaches: truth conditions, entailment, reference, and certain aspects of compositionality. We argue that this standard answer rests on a misconception: These aspects do not belong in a theory of expression meaning, they are instead aspects of speaker meaning, i.e., communicative intentions in a particular context. In a slogan: words do not refer, speakers do. Clearing this up enables us to argue that distributional semantics on its own is an adequate model of expression meaning. Our proposal sheds light on the role of distributional semantics in a broader theory of language and cognition, its relationship to formal semantics, and its place in computational models.
Discourse connectives are known to be subject to both usage and sense ambiguity, as has already been discussed in the literature. But discourse connectives are no different from other linguistic expressions in being subject to other types of ambiguity as well. Four are illustrated and discussed here.
Open Information Extraction (Open IE) aims at generating entity-relation-entity triples from a large amount of text, aiming at capturing key semantics of the text. Given a triple, the relation expresses the type of semantic relation between the entities. Although relations from an Open IE system are more extensible than those used in a traditional Information Extraction system and a Knowledge Base (KB) such as Knowledge Graphs, the former lacks in semantics; an Open IE relation is simply a sequence of words, whereas a KB relation has a predefined meaning. As a way to provide a meaning to an Open IE relation, we attempt to align it with one of the predefined set of relations used in a KB. Our approach is to use a Siamese network that compares two sequences of word embeddings representing an Open IE relation and a predefined KB relation. In order to make the approach practical, we automatically generate a training dataset using a distant supervision approach instead of relying on a hand-labeled dataset. Our experiment shows that the proposed method can capture the relational semantics better than the recent approaches.
In this paper, we propose a language-agnostic deep neural network architecture for aspect-based sentiment analysis. The proposed approach is based on Bidirectional Long Short-Term Memory (Bi-LSTM) network, which is further assisted with extra hand-crafted features. We define three different architectures for the successful combination of word embeddings and hand-crafted features. We evaluate the proposed approach for six languages (i.e. English, Spanish, French, Dutch, German and Hindi) and two problems (i.e. aspect term extraction and aspect sentiment classification). Experiments show that the proposed model attains state-of-the-art performance in most of the settings.
We conduct two experiments to study the effect of context on metaphor paraphrase aptness judgments. The first is an AMT crowd source task in which speakers rank metaphor-paraphrase candidate sentence pairs in short document contexts for paraphrase aptness. In the second we train a composite DNN to predict these human judgments, first in binary classifier mode, and then as gradient ratings. We found that for both mean human judgments and our DNN’s predictions, adding document context compresses the aptness scores towards the center of the scale, raising low out-of-context ratings and decreasing high out-of-context scores. We offer a provisional explanation for this compression effect.
Concreteness of words has been studied extensively in psycholinguistic literature. A number of datasets have been created with average values for perceived concreteness of words. We show that we can train a regression model on these data, using word embeddings and morphological features, that can predict these concreteness values with high accuracy. We evaluate the model on 7 publicly available datasets. Only for a few small subsets of these datasets prediction of concreteness values are found in the literature. Our results clearly outperform the reported results for these datasets.
Implicit discourse relation classification is one of the most difficult steps in discourse parsing. The difficulty stems from the fact that the coherence relation must be inferred based on the content of the discourse relational arguments. Therefore, an effective encoding of the relational arguments is of crucial importance. We here propose a new model for implicit discourse relation classification, which consists of a classifier, and a sequence-to-sequence model which is trained to generate a representation of the discourse relational arguments by trying to predict the relational arguments including a suitable implicit connective. Training is possible because such implicit connectives have been annotated as part of the PDTB corpus. Along with a memory network, our model could generate more refined representations for the task. And on the now standard 11-way classification, our method outperforms the previous state of the art systems on the PDTB benchmark on multiple settings including cross validation.
We describe a transfer method based on annotation projection to develop a dependency-based semantic role labeling system for languages for which no supervised linguistic information other than parallel data is available. Unlike previous work that presumes the availability of supervised features such as lemmas, part-of-speech tags, and dependency parse trees, we only make use of word and character features. Our deep model considers using character-based representations as well as unsupervised stem embeddings to alleviate the need for supervised features. Our experiments outperform a state-of-the-art method that uses supervised lexico-syntactic features on 6 out of 7 languages in the Universal Proposition Bank.
The multimodal models used in the emerging field at the intersection of computational linguistics and computer vision implement the bottom-up processing of the “Hub and Spoke” architecture proposed in cognitive science to represent how the brain processes and combines multi-sensory inputs. In particular, the Hub is implemented as a neural network encoder. We investigate the effect on this encoder of various vision-and-language tasks proposed in the literature: visual question answering, visual reference resolution, and visually grounded dialogue. To measure the quality of the representations learned by the encoder, we use two kinds of analyses. First, we evaluate the encoder pre-trained on the different vision-and-language tasks on an existing “diagnostic task” designed to assess multimodal semantic understanding. Second, we carry out a battery of analyses aimed at studying how the encoder merges and exploits the two modalities.
Learning to follow human instructions is a long-pursued goal in artificial intelligence. The task becomes particularly challenging if no prior knowledge of the employed language is assumed while relying only on a handful of examples to learn from. Work in the past has relied on hand-coded components or manually engineered features to provide strong inductive biases that make learning in such situations possible. In contrast, here we seek to establish whether this knowledge can be acquired automatically by a neural network system through a two phase training procedure: A (slow) offline learning stage where the network learns about the general structure of the task and a (fast) online adaptation phase where the network learns the language of a new given speaker. Controlled experiments show that when the network is exposed to familiar instructions but containing novel words, the model adapts very efficiently to the new vocabulary. Moreover, even for human speakers whose language usage can depart significantly from our artificial training language, our network can still make use of its automatically acquired inductive bias to learn to follow instructions more effectively.
The embedding of words and documents in compact, semantically meaningful vector spaces is a crucial part of modern information systems. Deep Learning models are powerful but their hyperparameter selection is often complex and they are expensive to train, and while pre-trained models are available, embeddings trained on general corpora are not necessarily well-suited to domain specific tasks. We propose a novel embedding method which extends random projection by weighting and projecting raw term embeddings orthogonally to an average language vector, thus improving the discriminating power of resulting term embeddings, and build more meaningful document embeddings by assigning appropriate weights to individual terms. We describe how updating the term embeddings online as we process the training data results in an extremely efficient method, in terms of both computational and memory requirements. Our experiments show highly competitive results with various state-of-the-art embedding methods on different tasks, including the standard STS benchmark and a subject prediction task, at a fraction of the computational cost.
Popular word embedding methods such as word2vec and GloVe assign a single vector representation to each word, even if a word has multiple distinct meanings. Multi-sense embeddings instead provide different vectors for each sense of a word. However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word. In this work, we study the effect of multi-sense embeddings on the task of reverse dictionaries. We propose a technique to easily integrate them into an existing neural network architecture using an attention mechanism. Our experiments demonstrate that large improvements can be obtained when employing multi-sense embeddings both in the input sequence as well as for the target representation. An analysis of the sense distributions and of the learned attention is provided as well.
As opposed to word sense induction, word sense disambiguation (WSD) has the advantage of us-ing interpretable senses, but requires annotated data, which are quite rare for most languages except English (Miller et al. 1993; Fellbaum, 1998). In this paper, we investigate which strategy to adopt to achieve WSD for languages lacking data that was annotated specifically for the task, focusing on the particular case of verb disambiguation in French. We first study the usability of Eurosense (Bovi et al. 2017) , a multilingual corpus extracted from Europarl (Kohen, 2005) and automatically annotated with BabelNet (Navigli and Ponzetto, 2010) senses. Such a resource opened up the way to supervised and semi-supervised WSD for resourceless languages like French. While this perspective looked promising, our evaluation on French verbs was inconclusive and showed the annotated senses’ quality was not sufficient for supervised WSD on French verbs. Instead, we propose to use Wiktionary, a collaboratively edited, multilingual online dictionary, as a resource for WSD. Wiktionary provides both sense inventory and manually sense tagged examples which can be used to train supervised and semi-supervised WSD systems. Yet, because senses’ distribution differ in lexicographic examples found in Wiktionary with respect to natural text, we then focus on studying the impact on WSD of the training data size and senses’ distribution. Using state-of-the art semi-supervised systems, we report experiments of Wiktionary-based WSD for French verbs, evaluated on FrenchSemEval (FSE), a new dataset of French verbs manually annotated with wiktionary senses.
Word embedding representations provide good estimates of word meaning and give state-of-the art performance in semantic tasks. Embedding approaches differ as to whether and how they account for the context surrounding a word. We present a comparison of different word and context representations on the task of proposing substitutes for a target word in context (lexical substitution). We also experiment with tuning contextualized word embeddings on a dataset of sense-specific instances for each target word. We show that powerful contextualized word representations, which give high performance in several semantics-related tasks, deal less well with the subtle in-context similarity relationships needed for substitution. This is better handled by models trained with this objective in mind, where the inter-dependence between word and context representations is explicitly modeled during training.
Propelling, and propelled by, the “deep learning revolution”, recent years have seen the introduction of ever larger corpora of images annotated with natural language expressions. We survey some of these corpora, taking a perspective that reverses the usual directionality, as it were, by viewing the images as semantic annotation of the natural language expressions. We discuss datasets that can be derived from the corpora, and tasks of potential interest for computational semanticists that can be defined on those. In this, we make use of relations provided by the corpora (namely, the link between expression and image, and that between two expressions linked to the same image) and relations that we can add (similarity relations between expressions, or between images). Specifically, we show that in this way we can create data that can be used to learn and evaluate lexical and compositional grounded semantics, and we show that the “linked to same image” relation tracks a semantic implication relation that is recognisable to annotators even in the absence of the linking image as evidence. Finally, as an example of possible benefits of this approach, we show that an exemplar-model-based approach to implication beats a (simple) distributional space-based one on some derived datasets, while lending itself to explainability.
Categorization is a central capability of human cognition, and a number of theories have been developed to account for properties of categorization. Even though many tasks in semantics also involve categorization of some kind, theories of categorization do not play a major role in contemporary research in computational linguistics. This paper follows the idea that embedding-based models of semantics lend themselves well to being formulated in terms of classical categorization theories. The benefit is a space of model families that enables (a) the formulation of hypotheses about the impact of major design decisions, and (b) a transparent assessment of these decisions. We instantiate this idea on the task of frame-semantic frame identification. We define four models that cross two design variables: (a) the choice of prototype vs. exemplar categorization, corresponding to different degrees of generalization applied to the input; and (b) the presence vs. absence of a fine-tuning step, corresponding to generic vs. task-adaptive categorization. We find that for frame identification, generalization and task-adaptive categorization both yield substantial benefits. Our prototype-based, fine-tuned model, which combines the best choices for these variables, establishes a new state of the art in frame identification.
We explore a novel application for interpreting semantic type coercions, motivated by insight into the role that perceptual affordances play in the selection of the semantic roles of artefactual nouns which are observed as arguments for verbs which would stereotypically select for objects of a different type. In order to simulate affordances, which we take to be direct perceptions of context-specific opportunities for action, we preform a distributional analysis dependency relationships between target words and their modifiers and adjuncts. We use these relationships as the basis for generating on-line transformations which project semantic subspaces in which the interpretations of coercive compositions are expected to emerge as salient word-vectors. We offer some preliminary examples of how this model operates on a dataset of sentences involving coercive interactions between verbs and objects specifically designed to evaluate this work.
This paper describes a working system which performs natural language inference using polarity-marked parse trees. The system handles all of the instances of monotonicity inference in the FraCaS data set. Except for the initial parse, it is entirely deterministic. It handles multi-premise arguments, and the kind of inference performed is essentially “logical”, but it goes beyond what is representable in first-order logic. In any case, the system works on surface forms rather than on representations of any kind.
Distributional semantics models (DSMs) are known to produce excellent representations of word meaning, which correlate with a range of behavioural data. As lexical representations, they have been said to be fundamentally different from truth-theoretic models of semantics, where meaning is defined as a correspondence relation to the world. There are two main aspects to this difference: a) DSMs are built over corpus data which may or may not reflect ‘what is in the world’; b) they are built from word co-occurrences, that is, from lexical types rather than entities and sets. In this paper, we inspect the properties of a distributional model built over a set-theoretic approximation of ‘the real world’. To achieve this, we take the annotation a large database of images marked with objects, attributes and relations, convert the data into a representation akin to first-order logic and build several distributional models using various combinations of features. We evaluate those models over both relatedness and similarity datasets, demonstrating their effectiveness in standard evaluations. This allows us to conclude that, despite prior claims, truth-theoretic models are good candidates for building graded lexical representations of meaning.
Recently, sequence-to-sequence models have achieved impressive performance on a number of semantic parsing tasks. However, they often do not exploit available linguistic resources, while these, when employed correctly, are likely to increase performance even further. Research in neural machine translation has shown that employing this information has a lot of potential, especially when using a multi-encoder setup. We employ a range of semantic and syntactic resources to improve performance for the task of Discourse Representation Structure Parsing. We show that (i) linguistic features can be beneficial for neural semantic parsing and (ii) the best method of adding these features is by using multiple encoders.
We present the design of a system for making sense of conflicting rules expressed in a fragment of the prominent controlled natural language ACE, yet extended with means of expressing defeasible rules in the form of normality assumptions. The approach we describe is ultimately based on answer-set-programming (ASP); simulating existential quantification by using skolemization in a manner resembling a translation for ASP recently formalized in the context of ∃-ASP. We discuss the advantages of this approach to building on the existing ACE interface to rule-systems, ACERules.
In recent years, both cognitive and computational research has provided empirical analyses of contextual co-occurrence of concrete and abstract words, partially resulting in inconsistent pictures. In this work we provide a more fine-grained description of the distributional nature in the corpus-based interaction of verbs and nouns within subcategorisation, by investigating the concreteness of verbs and nouns that are in a specific syntactic relationship with each other, i.e., subject, direct object, and prepositional object. Overall, our experiments show consistent patterns in the distributional representation of subcategorising and subcategorised concrete and abstract words. At the same time, the studies reveal empirical evidence why contextual abstractness represents a valuable indicator for automatic non-literal language identification.
Referring expressions and definite descriptions of objects in space exploit information both about object characteristics and locations. To resolve potential ambiguity, referencing strategies in language can rely on increasingly abstract concepts to distinguish an object in a given location from similar ones elsewhere, yet the description of the intended location may still be imprecise or difficult to interpret. Meanwhile, modalities such as gesture may communicate spatial information such as locations in a more concise manner. In real peer-to-peer communication, humans use language and gesture together to reference entities, with a capacity for mixing and changing modalities where needed. While recent progress in AI and human-computer interaction has created systems where a human can interact with a computer multimodally, computers often lack the capacity to intelligently mix modalities when generating referring expressions. We present a novel dataset of referring expressions combining natural language and gesture, describe its creation and evaluation, and its uses to train computational models for generating and interpreting multimodal referring expressions.
Word embedding is a technique in Natural Language Processing (NLP) to map words into vector space representations. Since it has boosted the performance of many NLP downstream tasks, the task of learning word embeddings has been addressing significantly. Nevertheless, most of the underlying word embedding methods such as word2vec and GloVe fail to produce high-quality embeddings if the text corpus is small and sparse. This paper proposes a method to generate effective word embeddings from limited data. Through experiments, we show that our proposed model outperforms existing works for the classical word similarity task and for a domain-specific application.
We present a simple method to find topics in user reviews that accompany ratings for products or services. Standard topic analysis will perform sub-optimal on such data since the word distributions in the documents are not only determined by the topics but by the sentiment as well. We reduce the influence of the sentiment on the topic selection by adding two explicit topics, representing positive and negative sentiment. We evaluate the proposed method on a set of over 15,000 hospital reviews. We show that the proposed method, Latent Semantic Analysis with explicit word features, finds topics with a much smaller bias for sentiments than other similar methods.
We know that word embeddings trained using neural-based methods (such as word2vec SGNS) are sensitive to stability problems and that across two models trained using the exact same set of parameters, the nearest neighbors of a word are likely to change. All words are not equally impacted by this internal instability and recent studies have investigated features influencing the stability of word embeddings. This stability can be seen as a clue for the reliability of the semantic representation of a word. In this work, we investigate the influence of the degree of concreteness of nouns on the stability of their semantic representation. We show that for English generic corpora, abstract words are more affected by stability problems than concrete words. We also found that to a certain extent, the difference between the degree of concreteness of a noun and its nearest neighbors can partly explain the stability or instability of its neighbors.
Under the standard approach to counterfactuals, to determine the meaning of a counterfactual sentence, we consider the “closest” possible world(s) where the antecedent is true, and evaluate the consequent. Building on the standard approach, some researchers have found that the set of worlds to be considered is dependent on context; it evolves with the discourse. Others have focused on how to define the “distance” between possible worlds, using ideas from causal modeling. This paper integrates the two ideas. We present a semantics for counterfactuals that uses a distance measure based on causal laws, that can also change over time. We show how our semantics can be implemented in the Haskell programming language.
In this paper, I will describe a system that was developed for the task of Visual Question Answering. The system uses the rich type universe of Type Theory with Records (TTR) to model both the utterances about the image, the image itself and classifications made related to the two. At its most basic, the decision of whether any given predicate can be assigned to an object in the image is delegated to a CNN. Consequently, images can be judged as evidence for propositions. The end result is a model whose application of perceptual classifiers to a given image is guided by the accompanying utterance.
The challenge of automatically describing images and videos has stimulated much research in Computer Vision and Natural Language Processing. In order to test the semantic abilities of new algorithms, we need reliable and objective ways of measuring progress. We show that standard evaluation measures do not take into account the semantic richness of a description, and give the impression that sparse machine descriptions outperform rich human descriptions. We introduce and test a new measure of semantic ability based on relative lexical diversity. We show how our measure can work alongside existing measures to achieve state of the art correlation with human judgement of quality. We also introduce a new dataset: Rich-Sparse Descriptions, which provides 2K human and machine descriptions to stimulate interest into the semantic evaluation of machine descriptions.
We present and discuss a couple of approaches, including different types of projections, and some examples, discussing the use of fuzzy sets for modeling meaning of certain types of language constructs. We are mostly focusing on words other than adjectives and linguistic hedges as these categories are the most studied from before. We discuss logical and linguistic interpretations of membership functions. We argue that using fuzzy sets for modeling meaning of words and other natural language constructs, along with situations described with natural language is interesting both from purely linguistic perspective, and also as a knowledge representation for problems of computational linguistics and natural language processing.
In this paper we present new results on applying topological data analysis to discourse structures. We show that topological information, extracted from the relationships between sentences can be used in inference, namely it can be applied to the very difficult legal entailment given in the COLIEE 2018 data set. Previous results of Doshi and Zadrozny (2018) and Gholizadeh et al. (2018) show that topological features are useful for classification. The applications of computational topology to entailment are novel in our view provide a new set of tools for discourse semantics: computational topology can perhaps provide a bridge between the brittleness of logic and the regression of neural networks. We discuss the advantages and disadvantages of using topological information, and some open problems such as explainability of the classifier decisions.
The early phases of requirements engineering (RE) deal with a vast amount of software requirements (i.e., requirements that define characteristics of software systems), which are typically expressed in natural language. Analysing such unstructured requirements, usually obtained from users’ inputs, is considered a challenging task due to the inherent ambiguity and inconsistency of natural language. To support such a task, methods based on natural language processing (NLP) can be employed. One of the more recent advances in NLP is the use of word embeddings for capturing contextual information, which can then be applied in word analogy tasks. In this paper, we describe a new resource, i.e., embedding-based representations of semantic frames in FrameNet, which was developed to support the detection of relations between software requirements. Our embeddings, which encapsulate contextual information at the semantic frame level, were trained on a large corpus of requirements (i.e., a collection of more than three million mobile application reviews). The similarity between these frame embeddings is then used as a basis for detecting semantic relatedness between software requirements. Compared with existing resources underpinned by word-level embeddings alone, and frame embeddings built upon pre-trained vectors, our proposed frame embeddings obtained better performance against judgements of an RE expert. These encouraging results demonstrate the strong potential of the resource in supporting RE analysis tasks (e.g., traceability), which we plan to investigate as part of our future work.
This paper investigates data-driven segmentation using Re-Pair or Byte Pair Encoding-techniques. In contrast to previous work which has primarily been focused on subword units for machine translation, we are interested in the general properties of such segments above the word level. We call these segments r-grams, and discuss their properties and the effect they have on the token frequency distribution. The proposed approach is evaluated by demonstrating its viability in embedding techniques, both in monolingual and multilingual test settings. We also provide a number of qualitative examples of the proposed methodology, demonstrating its viability as a language-invariant segmentation procedure.
Commonsense knowledge relations are crucial for advanced NLU tasks. We examine the learnability of such relations as represented in ConceptNet, taking into account their specific properties, which can make relation classification difficult: a given concept pair can be linked by multiple relation types, and relations can have multi-word arguments of diverse semantic types. We explore a neural open world multi-label classification approach that focuses on the evaluation of classification accuracy for individual relations. Based on an in-depth study of the specific properties of the ConceptNet resource, we investigate the impact of different relation representations and model variations. Our analysis reveals that the complexity of argument types and relation ambiguity are the most important challenges to address. We design a customized evaluation method to address the incompleteness of the resource that can be expanded in future work.
Semantic similarity between collocations, along with words similarity, is one of the main issues of NLP, which must be addressed, in particular, in order to facilitate the automatic thesaurus generation. In the paper, we consider the logical-linguistic model that allows defining the relation of semantic similarity of collocations via the logical-algebraic equations. We provide the model for English, Ukrainian and Russian text corpora. The implementation for each language is slightly different in the equations of the finite predicates algebra and used linguistic resources. As a dataset for our experiment, we use 5801 pairs of sentences of Microsoft Research Paraphrase Corpus for English and more than 1 000 texts of scientific papers for Russian and Ukrainian.
For the analysis of contract texts, validated model texts, such as model clauses, can be used to identify reused contract clauses. This paper investigates how to calculate the similarity between titles of model clauses and headings extracted from contracts, and which similarity measure is most suitable for this. For the calculation of the similarities between title pairs we tested various variants of string similarity and token based similarity. We also compare two more semantic similarity measures based on word embeddings using pretrained embeddings and word embeddings trained on contract texts. The identification of the model clause title can be used as a starting point for the mapping of clauses found in contracts to verified clauses.
We present a very simple, unsupervised method for the pairwise matching of documents from heterogeneous collections. We demonstrate our method with the Concept-Project matching task, which is a binary classification task involving pairs of documents from heterogeneous collections. Although our method only employs standard resources without any domain- or task-specific modifications, it clearly outperforms the more complex system of the original authors. In addition, our method is transparent, because it provides explicit information about how a similarity score was computed, and efficient, because it is based on the aggregation of (pre-computable) word-level similarities.
Much work in contemporary computational semantics follows the distributional hypothesis (DH), which is understood as an approach to semantics according to which the meaning of a word is a function of its distribution over contexts which is represented as vectors (word embeddings) within a multi-dimensional semantic space. In practice, use is identified with occurrence in text corpora, though there are some efforts to use corpora containing multi-modal information. In this paper we argue that the distributional hypothesis is intrinsically misguided as a self-supporting basis for semantics, as Firth was entirely aware. We mention philosophical arguments concerning the lack of normativity within DH data. Furthermore, we point out the shortcomings of DH as a model of learning, by discussing a variety of linguistic classes that cannot be learnt on a distributional basis, including indexicals, proper names, and wh-phrases. Instead of pursuing DH, we sketch an account of the problematic learning cases by integrating a rich, Firthian notion of dialogue context with interactive learning in signalling games backed by in probabilistic Type Theory with Records. We conclude that the success of the DH in computational semantics rests on a post hoc effect: DS presupposes a referential semantics on the basis of which utterances can be produced, comprehended and analysed in the first place.
Generating “commonsense’’ knowledge for intelligent understanding and reasoning is a difficult, long-standing problem, whose scale challenges the capacity of any approach driven primarily by human input. Furthermore, approaches based on mining statistically repetitive patterns fail to produce the rich representations humans acquire, and fall far short of human efficiency in inducing knowledge from text. The idea of our approach to this problem is to provide a learning system with a “head start” consisting of a semantic parser, some basic ontological knowledge, and most importantly, a small set of very general schemas about the kinds of patterns of events (often purposive, causal, or socially conventional) that even a one- or two-year-old could reasonably be presumed to possess. We match these initial schemas to simple children’s stories, obtaining concrete instances, and combining and abstracting these into new candidate schemas. Both the initial and generated schemas are specified using a rich, expressive logical form. While modern approaches to schema reasoning often only use slot-and-filler structures, this logical form allows us to specify complex relations and constraints over the slots. Though formal, the representations are language-like, and as such readily relatable to NL text. The agents, objects, and other roles in the schemas are represented by typed variables, and the event variables can be related through partial temporal ordering and causal relations. To match natural language stories with existing schemas, we first parse the stories into an underspecified variant of the logical form used by the schemas, which is suitable for most concrete stories. We include a walkthrough of matching a children’s story to these schemas and generating inferences from these matches.
Dependent Type Semantics (DTS; Bekki and Mineshima, 2017) is a proof-theoretic compositional dynamic semantics based on Dependent Type Theory. The semantic representations for declarative sentences in DTS are types, based on the propositions-as-types paradigm. While type-theoretic semantics for natural language based on dependent type theory has been developed by many authors, how to assign semantic representations to interrogative sentences has been a non-trivial problem. In this study, we show how to provide the semantics of interrogative sentences in DTS. The basic idea is to assign the same type to both declarative sentences and interrogative sentences, partly building on the recent proposal in Inquisitive Semantics. We use Combinatory Categorial Grammar (CCG) as a syntactic component of DTS and implement our compositional semantics for interrogative sentences using ccg2lambda, a semantic parsing platform based on CCG. Based on the idea that the relationship between questions and answers can be formulated as the task of Recognizing Textual Entailment (RTE), we implement our inference system using proof assistant Coq and show that our system can deal with a wide range of question-answer relationships discussed in the formal semantics literature, including those with polar questions, alternative questions, and wh-questions.
We outline a hyperintensional situation semantics in which hyperintensionality is modelled as a ‘side effect’, as this term has been understood in natural language semantics and in functional programming. We use monads from category theory in order to ‘upgrade’ an ordinary intensional semantics to a possible hyperintensional counterpart.
The paper presents the IWCS 2019 shared task on semantic parsing where the goal is to produce Discourse Representation Structures (DRSs) for English sentences. DRSs originate from Discourse Representation Theory and represent scoped meaning representations that capture the semantics of negation, modals, quantification, and presupposition triggers. Additionally, concepts and event-participants in DRSs are described with WordNet synsets and the thematic roles from VerbNet. To measure similarity between two DRSs, they are represented in a clausal form, i.e. as a set of tuples. Participant systems were expected to produce DRSs in this clausal form. Taking into account the rich lexical information, explicit scope marking, a high number of shared variables among clauses, and highly-constrained format of valid DRSs, all these makes the DRS parsing a challenging NLP task. The results of the shared task displayed improvements over the existing state-of-the-art parser.
We present our submission to the IWCS 2019 shared task on semantic parsing, a transition-based parser that uses explicit word-meaning pairings, but no explicit representation of syntax. Parsing decisions are made based on vector representations of parser states, encoded via stack-LSTMs (Ballesteros et al., 2017), as well as some heuristic rules. Our system reaches 70.88% f-score in the competition.
We describe the systems we developed for Discourse Representation Structure (DRS) parsing as part of the IWCS-2019 Shared Task of DRS Parsing.1 Our systems are based on sequence-to-sequence modeling. To implement our model, we use the open-source neural machine translation system implemented in PyTorch, OpenNMT-py. We experimented with a variety of encoder-decoder models based on recurrent neural networks and the Transformer model. We conduct experiments on the standard benchmark of the Parallel Meaning Bank (PMB 2.2). Our best system achieves a score of 84.8% F1 in the DRS parsing shared task.
This paper describes our participation in the shared task of Discourse Representation Structure parsing. It follows the work of Van Noord et al. (2018), who employed a neural sequence-to-sequence model to produce DRSs, also exploiting linguistic information with multiple encoders. We provide a detailed look in the performance of this model and show that (i) the benefit of the linguistic features is evident across a number of experiments which vary the amount of training data and (ii) the model can be improved by applying a number of postprocessing methods to fix ill-formed output. Our model ended up in second place in the competition, with an F-score of 84.5.
Social media sites like Facebook, Twitter, and other microblogging forums have emerged as a platform for people to express their opinions and views on different issues and events. It is often observed that people tend to take a stance; in favor, against or neutral towards a particular topic. The task of assessing the stance taken by the individual became significantly important with the emergence in the usage of online social platforms. Automatic stance detection system understands the user’s stance by analyzing the standalone texts against a target entity. Due to the limited contextual information a single sentence provides, it is challenging to solve this task effectively. In this paper, we introduce a Multi-Task Learning (MTL) based deep neural network architecture for automatically detecting stance present in the code-mixed corpus. We apply our approach on Hindi-English code-mixed corpus against the target entity - “Demonetisation.” Our best model achieved the result with a stance prediction accuracy of 63.2% which is a 4.5% overall accuracy improvement compared to the current supervised classification systems developed using the benchmark dataset for code-mixed data stance detection.
In this paper, we propose a soft label approach to target-level sentiment classification task, in which a history-based soft labeling model is proposed to measure the possibility of a context word as an opinion word. We also apply a convolution layer to extract local active features, and introduce positional weights to take relative distance information into consideration. In addition, we obtain more informative target representation by training with context tokens together to make deeper interaction between target and context tokens. We conduct experiments on SemEval 2014 datasets and the experimental results show that our approach significantly outperforms previous models and gives state-of-the-art results on these datasets.
We propose an attention-based neural network approach to detect abusive speech in online social networks. Our approach enables more effective modeling of context and the semantic relationships between words. We also empirically evaluate the value of text pre-processing techniques in addressing the challenge of out-of-vocabulary words in toxic content. Finally, we conduct extensive experiments on the Wikipedia Talk page datasets, showing improved predictive power over the previous state-of-the-art.
Adjective phrases like “a little bit surprised”, “completely shocked”, or “not stunned at all” are not handled properly by current state-of-the-art emotion classification and intensity prediction systems. Based on this finding, we analyze differences between embeddings used by these systems in regard to their capability of handling such cases and argue that intensifiers in context of emotion words need special treatment, as is established for sentiment polarity classification, but not for more fine-grained emotion prediction. To resolve this issue, we analyze different aspects of a post-processing pipeline which enriches the word representations of such phrases. This includes expansion of semantic spaces at the phrase level and sub-word level followed by retrofitting to emotion lexicons. We evaluate the impact of these steps with ‘A La Carte and Bag-of-Substrings extensions based on pretrained GloVe,Word2vec, and fastText embeddings against a crowd-sourced corpus of intensity annotations for tweets containing our focus phrases. We show that the fastText-based models do not gain from handling these specific phrases under inspection. For Word2vec embeddings, we show that our post-processing pipeline improves the results by up to 8% on a novel dataset densly populated with intensifiers while it does not decrease the performance on the established EmoInt dataset.
We investigate the relationship between basic principles of human morality and the expression of opinions in user-generated text data. We assume that people’s backgrounds, culture, and values are associated with their perceptions and expressions of everyday topics, and that people’s language use reflects these perceptions. While personal values and social effects are abstract and complex concepts, they have practical implications and are relevant for a wide range of NLP applications. To extract human values (in this paper, morality) and measure social effects (morality and stance), we empirically evaluate the usage of a morality lexicon that we expanded via a quality controlled, human in the loop process. As a result, we enhanced the Moral Foundations Dictionary in size (from 324 to 4,636 syntactically disambiguated entries) and scope. We used both lexica for feature-based and deep learning classification (SVM, RF, and LSTM) to test their usefulness for measuring social effects. We find that the enhancement of the original lexicon led to measurable improvements in prediction accuracy for the selected NLP tasks.
Twitter customer service interactions have recently emerged as an effective platform to respond and engage with customers. In this work, we explore the role of ”negation” in customer service interactions, particularly applied to sentiment analysis. We define rules to identify true negation cues and scope more suited to conversational data than existing general review data. Using semantic knowledge and syntactic structure from constituency parse trees, we propose an algorithm for scope detection that performs comparable to state of the art BiLSTM. We further investigate the results of negation scope detection for the sentiment prediction task on customer service conversation data using both a traditional SVM and a Neural Network. We propose an antonym dictionary based method for negation applied to a combination CNN-LSTM for sentiment analysis. Experimental results show that the antonym-based method outperforms the previous lexicon-based and Neural Network methods.
We propose bilingual word embeddings based on word2vec and fastText models (CBOW and Skip-gram) to address the problem of Humor detection in Hindi-English code-mixed tweets in combination with deep learning architectures. We focus on deep learning approaches which are not widely used on code-mixed data and analyzed their performance by experimenting with three different neural network models. We propose convolution neural network (CNN) and bidirectional long-short term memory (biLSTM) (with and without Attention) models which take the generated bilingual embeddings as input. We make use of Twitter data to create bilingual word embeddings. All our proposed architectures outperform the state-of-the-art results, and Attention-based bidirectional LSTM model achieved an accuracy of 73.6% which is an increment of more than 4% compared to the current state-of-the-art results.
In 2014, a chatty but immobile robot called hitchBOT set out to hitchhike across Canada. It similarly made its way across Germany and the Netherlands, and had begun a trip across the USA when it was destroyed by vandals. In this work, we analyze the emotions and sentiments associated with words in tweets posted before and after hitchBOT’s destruction to answer two questions: Were there any differences in the emotions expressed across the different countries visited by hitchBOT? And how did the public react to the demise of hitchBOT? Our analyses indicate that while there were few cross-cultural differences in sentiment towards hitchBOT, there was a significant negative emotional reaction to its destruction, suggesting that people had formed an emotional connection with hitchBOT and perceived its destruction as morally wrong. We discuss potential implications of anthropomorphism and emotional attachment to robots from the perspective of robot ethics.
Research in sarcasm detection spans almost a decade. However a particular form of sarcasm remains unexplored: sarcasm expressed through numbers, which we estimate, forms about 11% of the sarcastic tweets in our dataset. The sentence ‘Love waking up at 3 am’ is sarcastic because of the number. In this paper, we focus on detecting sarcasm in tweets arising out of numbers. Initially, to get an insight into the problem, we implement a rule-based and a statistical machine learning-based (ML) classifier. The rule-based classifier conveys the crux of the numerical sarcasm problem, namely, incongruity arising out of numbers. The statistical ML classifier uncovers the indicators i.e., features of such sarcasm. The actual system in place, however, are two deep learning (DL) models, CNN and attention network that obtains an F-score of 0.93 and 0.91 on our dataset of tweets containing numbers. To the best of our knowledge, this is the first line of research investigating the phenomenon of sarcasm arising out of numbers, culminating in a detector thereof.
Wide and universal changes in the web content due to the growth of web 2 applications increase the importance of user-generated content on the web. Therefore, the related research areas such as sentiment analysis, opinion mining and subjectivity detection receives much attention from the research community. Due to the diverse languages that web-users use to express their opinions and sentiments, research areas like subjectivity detection should present methods which are practicable on all languages. An important prerequisite to effectively achieve this aim is considering the limitations in resource-lean languages. In this paper, cross-lingual subjectivity detection on resource lean languages is investigated using two different approaches: a language-model based and a learning-to-rank approach. Experimental results show the impact of different factors on the performance of subjectivity detection methods using English resources to detect the subjectivity score of Persian documents. The experiments demonstrate that the proposed learning-to-rank method outperforms the baseline method in ranking documents based on their subjectivity degree.
In this work, we investigate the impact of incorporating emotion classes on the task of predicting emojis from Twitter texts. More specifically, we first show that there is a correlation between the emotion expressed in the text and the emoji choice of Twitter users. Based on this insight we propose a few simple methods to incorporate emotion information in traditional classifiers. Through automatic metrics, human evaluation, and error analysis, we show that the improvement obtained by incorporating emotion is significant and correlate better with human preferences compared to the baseline models. Through the human ratings that we obtained, we also argue for preference metric to better evaluate the usefulness of an emoji prediction system.
In this paper, we present the findings of the Third VarDial Evaluation Campaign organized as part of the sixth edition of the workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with NAACL 2019. This year, the campaign included five shared tasks, including one task re-run – German Dialect Identification (GDI) – and four new tasks – Cross-lingual Morphological Analysis (CMA), Discriminating between Mainland and Taiwan variation of Mandarin Chinese (DMT), Moldavian vs. Romanian Cross-dialect Topic identification (MRC), and Cuneiform Language Identification (CLI). A total of 22 teams submitted runs across the five shared tasks. After the end of the competition, we received 14 system description papers, which are published in the VarDial workshop proceedings and referred to in this report.
We describe the systems developed by the National Research Council Canada for the Cuneiform Language Identification (CLI) shared task at the 2019 VarDial evaluation campaign. We compare a state-of-the-art baseline relying on character n-grams and a traditional statistical classifier, a voting ensemble of classifiers, and a deep learning approach using a Transformer network. We describe how these systems were trained, and analyze the impact of some preprocessing and model estimation decisions. The deep neural network achieved 77% accuracy on the test data, which turned out to be the best performance at the CLI evaluation, establishing a new state-of-the-art for cuneiform language identification.
The conversion of romanized texts back to the native scripts is a challenging task because of the inconsistent romanization conventions and non-standard language use. This problem is compounded by code-mixing, i.e., using words from more than one language within the same discourse. In this paper, we propose a novel approach for handling these two problems together in a single system. Our approach combines three components: language identification, back-transliteration, and sequence prediction. The results of our experiments on Bengali and Hindi datasets establish the state of the art for the task of deromanization of code-mixed texts.
We explore how well a sequence labeling approach, namely, recurrent neural network, is suited for the task of resource-poor and POS tagging free word stress detection in the Russian, Ukranian, Belarusian languages. We present new datasets, annotated with the word stress, for the three languages and compare several RNN models trained on three languages and explore possible applications of the transfer learning for the task. We show that it is possible to train a model in a cross-lingual setting and that using additional languages improves the quality of the results.
This paper evaluates global-scale dialect identification for 14 national varieties of English on both web-crawled data and Twitter data. The paper makes three main contributions: (i) introducing data-driven language mapping as a method for selecting the inventory of national varieties to include in the task; (ii) producing a large and dynamic set of syntactic features using grammar induction rather than focusing on a few hand-selected features such as function words; and (iii) comparing models across both web corpora and social media corpora in order to measure the robustness of syntactic variation across registers.
This paper describes the work done by team tearsofjoy participating in the VarDial 2019 Evaluation Campaign. We developed two systems based on Support Vector Machines: SVM with a flat combination of features and SVM ensembles. We participated in all language/dialect identification tasks, as well as the Moldavian vs. Romanian cross-dialect topic identification (MRC) task. Our team achieved first place in German Dialect identification (GDI) and MRC subtasks 2 and 3, second place in the simplified variant of Discriminating between Mainland and Taiwan variation of Mandarin Chinese (DMT) as well as Cuneiform Language Identification (CLI), and third and fifth place in DMT traditional and MRC subtask 1 respectively. In most cases, the SVM with a flat combination of features performed better than SVM ensembles. Besides describing the systems and the results obtained by them, we provide a tentative comparison between the feature combination methods, and present additional experiments with a method of adaptation to the test set, which may indicate potential pitfalls with some of the data sets.
This paper deals with the automatic identification of literate and oral discourse in German texts. A range of linguistic features is selected and their role in distinguishing between literate- and oral-oriented registers is investigated, using a decision-tree classifier. It turns out that all of the investigated features are related in some way to oral conceptuality. Especially simple measures of complexity (average sentence and word length) are prominent indicators of oral and literate discourse. In addition, features of reference and deixis (realized by different types of pronouns) also prove to be very useful in determining the degree of orality of different registers.
This work explores neural machine translation between Myanmar (Burmese) and Rakhine (Arakanese). Rakhine is a language closely related to Myanmar, often considered a dialect. We implemented three prominent neural machine translation (NMT) systems: recurrent neural networks (RNN), transformer, and convolutional neural networks (CNN). The systems were evaluated on a Myanmar-Rakhine parallel text corpus developed by us. In addition, two types of word segmentation schemes for word embeddings were studied: Word-BPE and Syllable-BPE segmentation. Our experimental results clearly show that the highest quality NMT and statistical machine translation (SMT) performances are obtained with Syllable-BPE segmentation for both types of translations. If we focus on NMT, we find that the transformer with Word-BPE segmentation outperforms CNN and RNN for both Myanmar-Rakhine and Rakhine-Myanmar translation. However, CNN with Syllable-BPE segmentation obtains a higher score than the RNN and transformer.
This article introduces a corpus of cuneiform texts from which the dataset for the use of the Cuneiform Language Identification (CLI) 2019 shared task was derived as well as some preliminary language identification experiments conducted using that corpus. We also describe the CLI dataset and how it was derived from the corpus. In addition, we provide some baseline language identification results using the CLI dataset. To the best of our knowledge, the experiments detailed here represent the first time that automatic language identification methods have been used on cuneiform data.
Linguistic Code Switching (CS) is a phenomenon that occurs when multilingual speakers alternate between two or more languages/dialects within a single conversation. Processing CS data is especially challenging in intra-sentential data given state-of-the-art monolingual NLP technologies since such technologies are geared toward the processing of one language at a time. In this paper, we address the problem of Part-of-Speech tagging (POS) in the context of linguistic code switching (CS). We explore leveraging multiple neural network architectures to measure the impact of different pre-trained embeddings methods on POS tagging CS data. We investigate the landscape in four CS language pairs, Spanish-English, Hindi-English, Modern Standard Arabic- Egyptian Arabic dialect (MSA-EGY), and Modern Standard Arabic- Levantine Arabic dialect (MSA-LEV). Our results show that multilingual embedding (e.g., MSA-EGY and MSA-LEV) helps closely related languages (EGY/LEV) but adds noise to the languages that are distant (SPA/HIN). Finally, we show that our proposed models outperform state-of-the-art CS taggers for MSA-EGY language pair.
This paper presents a new approach to disentangling inter-dialectal and intra-dialectal relationships within one such group, the Indo-Aryan subgroup of Indo-European. We draw upon admixture models and deep generative models to tease apart historic language contact and language-specific behavior in the overall patterns of sound change displayed by Indo-Aryan languages. We show that a “deep” model of Indo-Aryan dialectology sheds some light on questions regarding inter-relationships among the Indo-Aryan languages, and performs better than a “shallow” model in terms of certain qualities of the posterior distribution (e.g., entropy of posterior distributions), and outline future pathways for model development.
Automatic dialect identification is a more challengingctask than language identification, as it requires the ability to discriminate between varieties of one language. In this paper, we propose an ensemble based system, which combines traditional machine learning models trained on bag of n-gram fetures, with deep learning models trained on word embeddings, to solve the Discriminating between Mainland and Taiwan Variation of Mandarin Chinese (DMT) shared task at VarDial 2019. Our experiments show that a character bigram-trigram combination based Naive Bayes is a very strong model for identifying varieties of Mandarin Chinense. Through further ensemble of Navie Bayes and BiLSTM, our system (team: itsalexyang) achived an macro-averaged F1 score of 0.8530 and 0.8687 in two tracks.
*This is a submission for the Third VarDial Evaluation Campaign* In this paper, we present a machine learning approach for the German Dialect Identification (GDI) Closed Shared Task of the DSL 2019 Challenge. The proposed approach combines deep and shallow models, by applying a voting scheme on the outputs resulted from a Character-level Convolutional Neural Networks (Char-CNN), a Long Short-Term Memory (LSTM) network, and a model based on String Kernels. The first model used is the Char-CNN model that merges multiple convolutions computed with kernels of different sizes. The second model is the LSTM network which applies a global max pooling over the returned sequences over time. Both models pass the activation maps to two fully-connected layers. The final model is based on String Kernels, computed on character p-grams extracted from speech transcripts. The model combines two blended kernel functions, one is the presence bits kernel, and the other is the intersection kernel. The empirical results obtained in the shared task prove that the approach can achieve good results. The system proposed in this paper obtained the fourth place with a macro-F1 score of 62.55%
This article presents the model that generated the runs submitted by the R2I_LIS team to the VarDial2019 evaluation campaign, more particularly, to the binary classification by dialect sub-task of the Moldavian vs. Romanian Cross-dialect Topic identification (MRC) task. The team proposed a majority vote-based model, between five supervised machine learning models, trained on forty manually-crafted features. One of the three submitted runs was ranked second at the binary classification sub-task, with a performance of 0.7963, in terms of macro-F1 measure. The other two runs were ranked third and fourth, respectively.
The paper describes initial experiments in data-driven cross-lingual morphological analysis of open-category words using a combination of unsupervised morpheme segmentation, annotation projection and an LSTM encoder-decoder model with attention. Our algorithm provides lemmatisation and morphological analysis generation for previously unseen low-resource language surface forms with only annotated data on the related languages given. Despite the inherently lossy annotation projection, we achieved the best lemmatisation F1-score in the VarDial 2019 Shared Task on Cross-Lingual Morphological Analysis for both Karachay-Balkar (Turkic languages, agglutinative morphology) and Sardinian (Romance languages, fusional morphology).
This paper describes Tübingen-Oslo team’s participation in the cross-lingual morphological analysis task in the VarDial 2019 evaluation campaign. We participated in the shared task with a standard neural network model. Our model achieved analysis F1-scores of 31.48 and 23.67 on test languages Karachay-Balkar (Turkic) and Sardinian (Romance) respectively. The scores are comparable to the scores obtained by the other participants in both language families, and the analysis score on the Romance data set was also the best result obtained in the shared task. Besides describing the system used in our shared task participation, we describe another, simpler, model based on linear classifiers, and present further analyses using both models. Our analyses, besides revealing some of the difficult cases, also confirm that the usefulness of a source language in this task is highly correlated with the similarity of source and target languages.
This paper describes the IUCL system at VarDial 2019 evaluation campaign for the task of discriminating between Mainland and Taiwan variation of mandarin Chinese. We first build several base classifiers, including a Naive Bayes classifier with word n-gram as features, SVMs with both character and syntactic features, and neural networks with pre-trained character/word embeddings. Then we adopt ensemble methods to combine output from base classifiers to make final predictions. Our ensemble models achieve the highest F1 score (0.893) in simplified Chinese track and the second highest (0.901) in traditional Chinese track. Our results demonstrate the effectiveness and robustness of the ensemble methods.
This paper describes our models for the Moldavian vs. Romanian Cross-Topic Identification (MRC) evaluation campaign, part of the VarDial 2019 workshop. We focus on the three subtasks for MRC: binary classification between the Moldavian (MD) and the Romanian (RO) dialects and two cross-dialect multi-class classification between six news topics, MD to RO and RO to MD. We propose several deep learning models based on long short-term memory cells, Bidirectional Gated Recurrent Unit (BiGRU) and Hierarchical Attention Networks (HAN). We also employ three word embedding models to represent the text as a low dimensional vector. Our official submission includes two runs of the BiGRU and HAN models for each of the three subtasks. The best submitted model obtained the following macro-averaged F1 scores: 0.708 for subtask 1, 0.481 for subtask 2 and 0.480 for the last one. Due to a read error caused by the quoting behaviour over the test file, our final submissions contained a smaller number of items than expected. More than 50% of the submission files were corrupted. Thus, we also present the results obtained with the corrected labels for which the HAN model achieves the following results: 0.930 for subtask 1, 0.590 for subtask 2 and 0.687 for the third one.
This paper describes the language identification systems used by the SUKI team in the Discriminating between the Mainland and Taiwan variation of Mandarin Chinese (DMT) and the German Dialect Identification (GDI) shared tasks which were held as part of the third VarDial Evaluation Campaign. The DMT shared task included two separate tracks, one for the simplified Chinese script and one for the traditional Chinese script. We submitted three runs on both tracks of the DMT task as well as on the GDI task. We won the traditional Chinese track using Naive Bayes with language model adaptation, came second on GDI with an adaptive version of the HeLI 2.0 method, and third on the simplified Chinese track using again the adaptive Naive Bayes.
Identification of the languages written using cuneiform symbols is a difficult task due to the lack of resources and the problem of tokenization. The Cuneiform Language Identification task in VarDial 2019 addresses the problem of identifying seven languages and dialects written in cuneiform; Sumerian and six dialects of Akkadian language: Old Babylonian, Middle Babylonian Peripheral, Standard Babylonian, Neo-Babylonian, Late Babylonian, and Neo-Assyrian. This paper describes the approaches taken by SharifCL team to this problem in VarDial 2019. The best result belongs to an ensemble of Support Vector Machines and a naive Bayes classifier, both working on character-level features, with macro-averaged F1-score of 72.10%.
We describe our approaches for the German Dialect Identification (GDI) and the Cuneiform Language Identification (CLI) tasks at the VarDial Evaluation Campaign 2019. The goal was to identify dialects of Swiss German in GDI and Sumerian and Akkadian in CLI. In GDI, the system should distinguish four dialects from the German-speaking part of Switzerland. Our system for GDI achieved third place out of 6 teams, with a macro averaged F-1 of 74.6%. In CLI, the system should distinguish seven languages written in cuneiform script. Our system achieved third place out of 8 teams, with a macro averaged F-1 of 74.7%.
This paper presents the solution proposed by DTeam in the VarDial 2019 Evaluation Campaign for the Moldavian vs. Romanian cross-topic identification task. The solution proposed is a Support Vector Machines (SVM) ensemble composed of a two character-level neural networks. The first network is a skip-gram classification model formed of an embedding layer, three convolutional layers and two fully-connected layers. The second network has a similar architecture, but is trained using the triplet loss function.
This paper presents methods to discriminate between languages and dialects written in Cuneiform script, one of the first writing systems in the world. We report the results obtained by the PZ team in the Cuneiform Language Identification (CLI) shared task organized within the scope of the VarDial Evaluation Campaign 2019. The task included two languages, Sumerian and Akkadian. The latter is divided into six dialects: Old Babylonian, Middle Babylonian peripheral, Standard Babylonian, Neo Babylonian, Late Babylonian, and Neo Assyrian. We approach the task using a meta-classifier trained on various SVM models and we show the effectiveness of the system for this task. Our submission achieved 0.738 F1 score in discriminating between the seven languages and dialects and it was ranked fourth in the competition among eight teams.
When translating diglossic languages such as Arabic, situations may arise where we would like to translate a text but do not know which dialect it is. A traditional approach to this problem is to design dialect identification systems and dialect-specific machine translation systems. However, under the recent paradigm of neural machine translation, shared multi-dialectal systems have become a natural alternative. Here we explore under which conditions it is beneficial to perform dialect identification for Arabic neural machine translation versus using a general system for all dialects.
We tackle the important task of part-of-speech tagging using a neural model in the zero-resource scenario, where we have no access to gold-standard POS training data. We compare this scenario with the low-resource scenario, where we have access to a small amount of gold-standard POS training data. Our experiments focus on Ukrainian as a representative of under-resourced languages. Russian is highly related to Ukrainian, so we exploit gold-standard Russian POS tags. We consider four techniques to perform Ukrainian POS tagging: zero-shot tagging and cross-lingual annotation projection (for the zero-resource scenario), and compare these with self-training and multilingual learning (for the low-resource scenario). We find that cross-lingual annotation projection works particularly well in the zero-resource scenario.
Stack Long Short-Term Memory (StackLSTM) is useful for various applications such as parsing and string-to-tree neural machine translation, but it is also known to be notoriously difficult to parallelize for GPU training due to the fact that the computations are dependent on discrete operations. In this paper, we tackle this problem by utilizing state access patterns of StackLSTM to homogenize computations with regard to different discrete operations. Our parsing experiments show that the method scales up almost linearly with increasing batch size, and our parallelized PyTorch implementation trains significantly faster compared to the Dynet C++ implementation.
Procedural text, which describes entities and their interactions as they undergo some process, depicts entities in a uniquely nuanced way. First, each entity may have some observable discrete attributes, such as its state or location; modeling these involves imposing global structure and enforcing consistency. Second, an entity may have properties which are not made explicit but can be effectively induced and tracked by neural networks. In this paper, we propose a structured neural architecture that reflects this dual nature of entity evolution. The model tracks each entity recurrently, updating its hidden continuous representation at each step to contain relevant state information. The global discrete state structure is explicitly modelled with a neural CRF over the changing hidden representation of the entity. This CRF can explicitly capture constraints on entity states over time, enforcing that, for example, an entity cannot move to a location after it is destroyed. We evaluate the performance of our proposed model on QA tasks over process paragraphs in the ProPara dataset and find that our model achieves state-of-the-art results.
We propose structured encoding as a novel approach to learning representations for relations and events in neural structured prediction. Our approach explicitly leverages the structure of available relation and event metadata to generate these representations, which are parameterized by both the attribute structure of the metadata as well as the learned representation of the arguments of the relations and events. We consider affine, biaffine, and recurrent operators for building hierarchical representations and modelling underlying features. We apply our approach to the second-order structured prediction task studied in the 2016/2017 Belief and Sentiment analysis evaluations (BeSt): given a document and its entities, relations, and events (including metadata and mentions), determine the sentiment of each entity towards every relation and event in the document. Without task-specific knowledge sources or domain engineering, we significantly improve over systems and baselines that neglect the available metadata or its hierarchical structure. We observe across-the-board improvements on the BeSt 2016/2017 sentiment analysis task of at least 2.3 (absolute) and 10.6% (relative) F-measure over the previous state-of-the-art.
We propose a lightly-supervised approach for information extraction, in particular named entity classification, which combines the benefits of traditional bootstrapping, i.e., use of limited annotations and interpretability of extraction patterns, with the robust learning approaches proposed in representation learning. Our algorithm iteratively learns custom embeddings for both the multi-word entities to be extracted and the patterns that match them from a few example entities per category. We demonstrate that this representation-based approach outperforms three other state-of-the-art bootstrapping approaches on two datasets: CoNLL-2003 and OntoNotes. Additionally, using these embeddings, our approach outputs a globally-interpretable model consisting of a decision list, by ranking patterns based on their proximity to the average entity embedding in a given class. We show that this interpretable model performs close to our complete bootstrapping model, proving that representation learning can be used to produce interpretable models with small loss in performance. This decision list can be edited by human experts to mitigate some of that loss and in some cases outperform the original model.
Generating a large amount of training data for information extraction (IE) is either costly (if annotations are created manually), or runs the risk of introducing noisy instances (if distant supervision is used). On the other hand, semi-supervised learning (SSL) is a cost-efficient solution to combat lack of training data. In this paper, we adapt Mean Teacher (Tarvainen and Valpola, 2017), a denoising SSL framework to extract semantic relations between pairs of entities. We explore the sweet spot of amount of supervision required for good performance on this binary relation extraction task. Additionally, different syntax representations are incorporated into our models to enhance the learned representation of sentences. We evaluate our approach on the Google-IISc Distant Supervision (GDS) dataset, which removes test data noise present in all previous distance supervision datasets, which makes it a reliable evaluation benchmark (Jat et al., 2017). Our results show that the SSL Mean Teacher approach nears the performance of fully-supervised approaches even with only 10% of the labeled corpus. Further, the syntax-aware model outperforms other syntax-free approaches across all levels of supervision.
As autonomous systems become more commonplace, we need a way to easily and naturally communicate to them our goals and collaboratively come up with a plan on how to achieve these goals. To this end, we conducted a Wizard of Oz study to gather data and investigate the way operators would collaboratively make plans via a conversational ‘planning assistant’ for remote autonomous systems. We present here a corpus of 22 dialogs from expert operators, which can be used to train such a system. Data analysis shows that multimodality is key to successful interaction, measured both quantitatively and qualitatively via user feedback.
In this paper we describe a multilingual grounded language learning system adapted from an English-only system. This system learns the meaning of words used in crowd-sourced descriptions by grounding them in the physical representations of the objects they are describing. Our work presents a framework to compare the performance of the system when applied to a new language and to identify modifications necessary to attain equal performance, with the goal of enhancing the ability of robots to learn language from a more diverse range of people. We then demonstrate this system with Spanish, through first analyzing the performance of translated Spanish, and then extending this analysis to a new corpus of crowd-sourced Spanish language data. We find that with small modifications, the system is able to learn color, object, and shape words with comparable performance between languages.
A Natural Language Understanding (NLU) pipeline integrated with a 3D physics-based scene is a flexible way to develop and test language-based human-robot interaction, by virtualizing people, robot hardware and the target 3D environment. Here, interaction means both controlling robots using language and conversing with them about the user’s physical environment and her daily life. Such a virtual development framework was initially developed for the Bot Colony videogame launched on Steam in June 2014, and has been undergoing improvements since. The framework is focused of developing intuitive verbal interaction with various types of robots. Key robot functions (robot vision and object recognition, path planning and obstacle avoidance, task planning and constraints, grabbing and inverse kinematics), the human participants in the interaction, and the impact of gravity and other forces on the environment are all simulated using commercial 3D tools. The framework can be used as a robotics testbed: the results of our simulations can be compared with the output of algorithms in real robots, to validate such algorithms. A novelty of our framework is support for social interaction with robots - enabling robots to converse about people and objects in the user’s environment, as well as learning about human needs and everyday life topics from their owner.
Human-robot interaction often occurs in the form of instructions given from a human to a robot. For a robot to successfully follow instructions, a common representation of the world and objects in it should be shared between humans and the robot so that the instructions can be grounded. Achieving this representation can be done via learning, where both the world representation and the language grounding are learned simultaneously. However, in robotics this can be a difficult task due to the cost and scarcity of data. In this paper, we tackle the problem by separately learning the world representation of the robot and the language grounding. While this approach can address the challenges in getting sufficient data, it may give rise to inconsistencies between both learned components. Therefore, we further propose Bayesian learning to resolve such inconsistencies between the natural language grounding and a robot’s world representation by exploiting spatio-relational information that is implicitly present in instructions given by a human. Moreover, we demonstrate the feasibility of our approach on a scenario involving a robotic arm in the physical world.
Vision-and-Language Navigation (VLN) is a natural language grounding task where agents have to interpret natural language instructions in the context of visual scenes in a dynamic environment to achieve prescribed navigation goals. Successful agents must have the ability to parse natural language of varying linguistic styles, ground them in potentially unfamiliar scenes, plan and react with ambiguous environmental feedback. Generalization ability is limited by the amount of human annotated data. In particular, paired vision-language sequence data is expensive to collect. We develop a discriminator that evaluates how well an instruction explains a given path in VLN task using multi-modal alignment. Our study reveals that only a small fraction of the high-quality augmented data from Fried et al., as scored by our discriminator, is useful for training VLN agents with similar performance. We also show that a VLN agent warm-started with pre-trained components from the discriminator outperforms the benchmark success rates of 35.5 by 10% relative measure.
It is important, for human-robot interaction, to endow the robot with the knowledge necessary to understand human needs and to be able to respond to them. We present a formalized and unified representation for indoor environments using an ontology devised for a route description task in which a robot must provide explanations to a person. We show that this representation can be used to choose a route to explain to a human as well as to verbalize it using a route perspective. Based on ontology, this representation has a strong possibility of evolution to adapt to many other applications. With it, we get the semantics of the environment elements while keeping a description of the known connectivity of the environment. This representation and the illustration algorithms, to find and verbalize a route, have been tested in two environments of different scales.
This paper introduces SpatialNet, a novel resource which links linguistic expressions to actual spatial configurations. SpatialNet is based on FrameNet (Ruppenhofer et al., 2016) and VigNet (Coyne et al., 2011), two resources which use frame semantics to encode lexical meaning. SpatialNet uses a deep semantic representation of spatial relations to provide a formal description of how a language expresses spatial information. This formal representation of the lexical semantics of spatial language also provides a consistent way to represent spatial meaning across multiple languages. In this paper, we describe the structure of SpatialNet, with examples from English and German. We also show how SpatialNet can be combined with other existing NLP tools to create a text-to-scene system for a language.
Understanding and generating spatial descriptions requires knowledge about what objects are related, their functional interactions, and where the objects are geometrically located. Different spatial relations have different functional and geometric bias. The wide usage of neural language models in different areas including generation of image description motivates the study of what kind of knowledge is encoded in neural language models about individual spatial relations. With the premise that the functional bias of relations is expressed in their word distributions, we construct multi-word distributional vector representations and show that these representations perform well on intrinsic semantic reasoning tasks, thus confirming our premise. A comparison of our vector representations to human semantic judgments indicates that different bias (functional or geometric) is captured in different data collection tasks which suggests that the contribution of the two meaning modalities is dynamic, related to the context of the task.
Participating in conversations can be difficult for people with hearing loss, especially in acoustically challenging environments. We studied the preferences the hearing impaired have for a personal conversation assistant based on automatic speech recognition (ASR) technology. We created two prototypes which were evaluated by hearing impaired test users. This paper qualitatively compares the two based on the feedback obtained from the tests. The first prototype was a proof-of-concept system running real-time ASR on a laptop. The second prototype was developed for a mobile device with the recognizer running on a separate server. In the mobile device, augmented reality (AR) was used to help the hearing impaired observe gestures and lip movements of the speaker simultaneously with the transcriptions. Several testers found the systems useful enough to use in their daily lives, with majority preferring the mobile AR version. The biggest concern of the testers was the accuracy of the transcriptions and the lack of speaker identification.
Prosodic cues in conversational speech aid listeners in discerning a message. We investigate whether acoustic cues in spoken dialogue can be used to identify the importance of individual words to the meaning of a conversation turn. Individuals who are Deaf and Hard of Hearing often rely on real-time captions in live meetings. Word error rate, a traditional metric for evaluating automatic speech recognition (ASR), fails to capture that some words are more important for a system to transcribe correctly than others. We present and evaluate neural architectures that use acoustic features for 3-class word importance prediction. Our model performs competitively against state-of-the-art text-based word-importance prediction models, and it demonstrates particular benefits when operating on imperfect ASR output.
Silent speech interfaces (SSIs) are devices that enable speech communication when audible speech is unavailable. Articulation-to-speech (ATS) synthesis is a software design in SSI that directly converts articulatory movement information into audible speech signals. Permanent magnetic articulograph (PMA) is a wireless articulator motion tracking technology that is similar to commercial, wired Electromagnetic Articulograph (EMA). PMA has shown great potential for practical SSI applications, because it is wireless. The ATS performance of PMA, however, is unknown when compared with current EMA. In this study, we compared the performance of ATS using a PMA we recently developed and a commercially available EMA (NDI Wave system). Datasets with same stimuli and size that were collected from tongue tip were used in the comparison. The experimental results indicated the performance of PMA was close to, although not as equally good as that of EMA. Furthermore, in PMA, converting the raw magnetic signals to positional signals did not significantly affect the performance of ATS, which support the future direction in PMA-based ATS can be focused on the use of positional signals to maximize the benefit of spatial analysis.
Amyotrophic Lateral Sclerosis (ALS) is a progressive neurological disease that leads to degeneration of motor neurons and, as a result, inhibits the ability of the brain to control muscle movements. Monitoring the progression of ALS is of fundamental importance due to the wide variability in disease outlook that exists across patients. This progression is typically tracked using the ALS functional rating scale - revised (ALSFRS-R), which is the current clinical assessment of a patient’s level of functional impairment including speech and other motor tasks. In this paper, we investigated automatic estimation of the ALSFRS-R bulbar subscore from acoustic and articulatory movement samples. Experimental results demonstrated the AFSFRS-R bulbar subscore can be predicted from speech samples, which has clinical implication for automatic monitoring of the disease progression of ALS using speech information.
Blissymbolics (Bliss) is a pictographic writing system that is used by people with communication disorders. Bliss attempts to create a writing system that makes words easier to distinguish by using pictographic symbols that encapsulate meaning rather than sound, as the English alphabet does for example. Users of Bliss rely on human interpreters to use Bliss. We created a translation system from Bliss to natural English with the hopes of decreasing the reliance on human interpreters by the Bliss community. We first discuss the basic rules of Blissymbolics. Then we point out some of the challenges associated with developing computer assisted tools for Blissymbolics. Next we talk about our ongoing work in developing a translation system, including current limitations, and future work. We conclude with a set of examples showing the current capabilities of our translation system.
Making good letter or word predictions can help accelerate the communication of users of high-tech AAC devices. This is particularly important for real-time person-to-person conversations. We investigate whether per forming speech recognition on the speaking-side of a conversation can improve language model based predictions. We compare the accuracy of three plausible microphone deployment options and the accuracy of two commercial speech recognition engines (Google and IBM Watson). We found that despite recognition word error rates of 7-16%, our ensemble of N-gram and recurrent neural network language models made predictions nearly as good as when they used the reference transcripts.
Language models have broad adoption in predictive typing tasks. When the typing history contains numerous errors, as in open-vocabulary predictive typing with brain-computer interface (BCI) systems, we observe significant performance degradation in both n-gram and recurrent neural network language models trained on clean text. In evaluations of ranking character predictions, training recurrent LMs on noisy text makes them much more robust to noisy histories, even when the error model is misspecified. We also propose an effective strategy for combining evidence from multiple ambiguous histories of BCI electroencephalogram measurements.
Visual question answering (VQA) models have been shown to over-rely on linguistic biases in VQA datasets, answering questions “blindly” without considering visual context. Adversarial regularization (AdvReg) aims to address this issue via an adversary sub-network that encourages the main model to learn a bias-free representation of the question. In this work, we investigate the strengths and shortcomings of AdvReg with the goal of better understanding how it affects inference in VQA models. Despite achieving a new state-of-the-art on VQA-CP, we find that AdvReg yields several undesirable side-effects, including unstable gradients and sharply reduced performance on in-domain examples. We demonstrate that gradual introduction of regularization during training helps to alleviate, but not completely solve, these issues. Through error analyses, we observe that AdvReg improves generalization to binary questions, but impairs performance on questions with heterogeneous answer distributions. Qualitatively, we also find that regularized models tend to over-rely on visual features, while ignoring important linguistic cues in the question. Our results suggest that AdvReg requires further refinement before it can be considered a viable bias mitigation technique for VQA.
This paper presents a new task, the grounding of spatio-temporal identifying descriptions in videos. Previous work suggests potential bias in existing datasets and emphasizes the need for a new data creation schema to better model linguistic structure. We introduce a new data collection scheme based on grammatical constraints for surface realization to enable us to investigate the problem of grounding spatio-temporal identifying descriptions in videos. We then propose a two-stream modular attention network that learns and grounds spatio-temporal identifying descriptions based on appearance and motion. We show that motion modules help to ground motion-related words and also help to learn in appearance modules because modular neural networks resolve task interference between modules. Finally, we propose a future challenge and a need for a robust system arising from replacing ground truth visual annotations with automatic video object detector and temporal event localization.
Image captioning applied to biomedical images can assist and accelerate the diagnosis process followed by clinicians. This article is the first survey of biomedical image captioning, discussing datasets, evaluation measures, and state of the art methods. Additionally, we suggest two baselines, a weak and a stronger one; the latter outperforms all current state of the art systems on one of the datasets.
We revisit a particular visual grounding method: the “Image Retrieval Using Scene Graphs” (IRSG) system of Johnson et al. Our experiments indicate that the system does not effectively use its learned object-relationship models. We also look closely at the IRSG dataset, as well as the widely used Visual Relationship Dataset (VRD) that is adapted from it. We find that these datasets exhibit bias that allows methods that ignore relationships to perform relatively well. We also describe several other problems with the IRSG dataset, and report on experiments using a subset of the dataset in which the biases and other problems are removed. Our studies contribute to a more general effort: that of better understanding what machine-learning methods that combine language and vision actually learn and what popular datasets actually test.
Visual storytelling is an intriguing and complex task that only recently entered the research arena. In this work, we survey relevant work to date, and conduct a thorough error analysis of three very recent approaches to visual storytelling. We categorize and provide examples of common types of errors, and identify key shortcomings in current work. Finally, we make recommendations for addressing these limitations in the future.
We study verbs in image–text corpora, contrasting caption corpora, where texts are explicitly written to characterize image content, with depiction corpora, where texts and images may stand in more general relations. Captions show a distinctively limited distribution of verbs, with strong preferences for specific tense, aspect, lexical aspect, and semantic field. These limitations, which appear in data elicited by a range of methods, restrict the utility of caption corpora to inform image retrieval, multimodal document generation, and perceptually-grounded semantic models. We suggest that these limitations reflect the discourse constraints in play when subjects write texts to accompany imagery, so we argue that future development of image–text corpora should work to increase the diversity of event descriptions, while looking explicitly at the different ways text and imagery can be coherently related.
There has been significant interest recently in learning multilingual word embeddings – in which semantically similar words across languages have similar embeddings. State-of-the-art approaches have relied on expensive labeled data, which is unavailable for low-resource languages, or have involved post-hoc unification of monolingual embeddings. In the present paper, we investigate the efficacy of multilingual embeddings learned from weakly-supervised image-text data. In particular, we propose methods for learning multilingual embeddings using image-text data, by enforcing similarity between the representations of the image and that of the text. Our experiments reveal that even without using any expensive labeled data, a bag-of-words-based embedding model trained on image-text data achieves performance comparable to the state-of-the-art on crosslingual semantic similarity tasks.
Recent work on visually grounded language learning has focused on broader applications of grounded representations, such as visual question answering and multimodal machine translation. In this paper we consider grounded word sense translation, i.e. the task of correctly translating an ambiguous source word given the corresponding textual and visual context. Our main objective is to investigate the extent to which images help improve word-level (lexical) translation quality. We do so by first studying the dataset for this task to understand the scope and challenges of the task. We then explore different data settings, image features, and ways of grounding to investigate the gain from using images in each of the combinations. We find that grounding on the image is specially beneficial in weaker unidirectional recurrent translation models. We observe that adding structured image information leads to stronger gains in lexical translation accuracy.
Crucial information about the practice of healthcare is recorded only in free-form text, which creates an enormous opportunity for high-impact NLP. However, annotated healthcare datasets tend to be small and expensive to obtain, which raises the question of how to make maximally efficient uses of the available data. To this end, we develop an LSTM-CRF model for combining unsupervised word representations and hand-built feature representations derived from publicly available healthcare ontologies. We show that this combined model yields superior performance on five datasets of diverse kinds of healthcare text (clinical, social, scientific, commercial). Each involves the labeling of complex, multi-word spans that pick out different healthcare concepts. We also introduce a new labeled dataset for identifying the treatment relations between drugs and diseases.
The shift to electronic medical records (EMRs) has engendered research into machine learning and natural language technologies to analyze patient records, and to predict from these clinical outcomes of interest. Two observations motivate our aims here. First, unstructured notes contained within EMR often contain key information, and hence should be exploited by models. Second, while strong predictive performance is important, interpretability of models is perhaps equally so for applications in this domain. Together, these points suggest that neural models for EMR may benefit from incorporation of attention over notes, which one may hope will both yield performance gains and afford transparency in predictions. In this work we perform experiments to explore this question using two EMR corpora and four different predictive tasks, that: (i) inclusion of attention mechanisms is critical for neural encoder modules that operate over notes fields in order to yield competitive performance, but, (ii) unfortunately, while these boost predictive performance, it is decidedly less clear whether they provide meaningful support for predictions.
In this paper we describe an evaluation of the potential of classical information extraction methods to extract drug-related attributes, including adverse drug events, and compare to more recently developed neural methods. We use the 2018 N2C2 shared task data as our gold standard data set for training. We train support vector machine classifiers to detect drug and drug attribute spans, and pair these detected entities as training instances for an SVM relation classifier, with both systems using standard features. We compare to baseline neural methods that use standard contextualized embedding representations for entity and relation extraction. The SVM-based system and a neural system obtain comparable results, with the SVM system doing better on concepts and the neural system performing better on relation extraction tasks. The neural system obtains surprisingly strong results compared to the system based on years of research in developing features for information extraction.
In the medical domain and other scientific areas, it is often important to recognize different levels of hierarchy in mentions, such as those related to specific symptoms or diseases associated with different anatomical regions. Unlike previous approaches, we build a transition-based parser that explicitly models an arbitrary number of hierarchical and nested mentions, and propose a loss that encourages correct predictions of higher-level mentions. We further introduce a set of modifier classes which introduces certain concepts that change the meaning of an entity, such as absence, or uncertainty about a given disease. Our proposed model achieves state-of-the-art results in medical entity recognition datasets, using both nested and hierarchical mentions.
Large-scale clinical data is invaluable to driving many computational scientific advances today. However, understandable concerns regarding patient privacy hinder the open dissemination of such data and give rise to suboptimal siloed research. De-identification methods attempt to address these concerns but were shown to be susceptible to adversarial attacks. In this work, we focus on the vast amounts of unstructured natural language data stored in clinical notes and propose to automatically generate synthetic clinical notes that are more amenable to sharing using generative models trained on real de-identified records. To evaluate the merit of such notes, we measure both their privacy preservation properties as well as utility in training clinical NLP models. Experiments using neural language models yield notes whose utility is close to that of the real ones in some clinical NLP tasks, yet leave ample room for future improvements.
While much data within a patient’s electronic health record (EHR) is coded, crucial information concerning the patient’s care and management remain buried in unstructured clinical notes, making it difficult and time-consuming for physicians to review during their usual clinical workflow. In this paper, we present our clinical note processing pipeline, which extends beyond basic medical natural language processing (NLP) with concept recognition and relation detection to also include components specific to EHR data, such as structured data associated with the encounter, sentence-level clinical aspects, and structures of the clinical notes. We report on the use of this pipeline in a disease-specific extractive text summarization task on clinical notes, focusing primarily on progress notes by physicians and nurse practitioners. We show how the addition of EHR-specific components to the pipeline resulted in an improvement in our overall system performance and discuss the potential impact of EHR-specific components on other higher-level clinical NLP tasks.
This paper details the development of a linguistic resource designed to improve temporal information extraction systems and to integrate aspectual values. After a brief review of recent works in temporal information extraction for the medical area, we discuss the linguistic notion of aspect and how it got a place in the NLP field. Then, we present our clinical data and describe the five-step approach adopted in this study. Finally, we represent the linguistic resource itself and explain how we elaborated it and which properties were selected for the creation of the tables.
Classic methods for clinical temporal relation extraction focus on relational candidates within a sentence. On the other hand, break-through Bidirectional Encoder Representations from Transformers (BERT) are trained on large quantities of arbitrary spans of contiguous text instead of sentences. In this study, we aim to build a sentence-agnostic framework for the task of CONTAINS temporal relation extraction. We establish a new state-of-the-art result for the task, 0.684F for in-domain (0.055-point improvement) and 0.565F for cross-domain (0.018-point improvement), by fine-tuning BERT and pre-training domain-specific BERT models on sentence-agnostic temporal relation instances with WordPiece-compatible encodings, and augmenting the labeled data with automatically generated “silver” instances.
Contextual word embedding models such as ELMo and BERT have dramatically improved performance for many natural language processing (NLP) tasks in recent months. However, these models have been minimally explored on specialty corpora, such as clinical text; moreover, in the clinical domain, no publicly-available pre-trained BERT models yet exist. In this work, we address this need by exploring and releasing BERT models for clinical text: one for generic clinical text and another for discharge summaries specifically. We demonstrate that using a domain-specific model yields performance improvements on 3/5 clinical NLP tasks, establishing a new state-of-the-art on the MedNLI dataset. We find that these domain-specific models are not as performant on 2 clinical de-identification tasks, and argue that this is a natural consequence of the differences between de-identified source text and synthetically non de-identified task text.
Knowledge discovery from text in natural language is a task usually aided by the manual construction of annotated corpora. Specifically in the clinical domain, several annotation models are used depending on the characteristics of the task to solve (e.g., named entity recognition, relation extraction, etc.). However, few general-purpose annotation models exist, that can support a broad range of knowledge extraction tasks. This paper presents an annotation model designed to capture a large portion of the semantics of natural language text. The structure of the annotation model is presented, with examples of annotated sentences and a brief description of each semantic role and relation defined. This research focuses on an application to clinical texts in the Spanish language. Nevertheless, the presented annotation model is extensible to other domains and languages. An example of annotated sentences, guidelines, and suitable configuration files for an annotation tool are also provided for the research community.
We explore the use of real-time clinical information, i.e., text messages sent between nurses and doctors regarding patient conditions in order to predict transfer to the intensive care unit(ICU). Preliminary results, in data from five hospitals, indicate that, despite being short and full of noise, text messages can augment other visit information to improve the performance of ICU transfer prediction.
Entity linking (or Normalization) is an essential task in text mining that maps the entity mentions in the medical text to standard entities in a given Knowledge Base (KB). This task is of great importance in the medical domain. It can also be used for merging different medical and clinical ontologies. In this paper, we center around the problem of disease linking or normalization. This task is executed in two phases: candidate generation and candidate scoring. In this paper, we present an approach to rank the candidate Knowledge Base entries based on their similarity with disease mention. We make use of the Triplet Network for candidate ranking. While the existing methods have used carefully generated sieves and external resources for candidate generation, we introduce a robust and portable candidate generation scheme that does not make use of the hand-crafted rules. Experimental results on the standard benchmark NCBI disease dataset demonstrate that our system outperforms the prior methods by a significant margin.
Many clinical information needs can be stated as why-questions. The answers to them represent important clinical reasoning and justification. Clinical notes are a rich source for such why-question answering (why-QA). However, there are few dedicated corpora, and little is known about the characteristics of clinical why-QA narratives. To address this gap, the study performed manual annotation of 277 sentences containing explicit why-QA cues and summarized their quantitative and qualitative properties. The contributions are: 1) sharing a seed corpus that can be used for various QA-related training purposes, 2) adding to our knowledge about the diversity and distribution of clinical why-QA contents.
Population age information is an essential characteristic of clinical trials. In this paper, we focus on extracting minimum and maximum (min/max) age values for the study samples from clinical research articles. Specifically, we investigate the use of a neural network model for question answering to address this information extraction task. The min/max age QA model is trained on the massive structured clinical study records from ClinicalTrials.gov. For each article, based on multiple min and max age values extracted from the QA model, we predict both actual min/max age values for the study samples and filter out non-factual age expressions. Our system improves the results over (i) a passage retrieval based IE system and (ii) a CRF-based system by a large margin when evaluated on an annotated dataset consisting of 50 research papers on smoking cessation.
Recently natural language processing (NLP) tools have been developed to identify and extract salient risk indicators in electronic health records (EHRs). Sentiment analysis, although widely used in non-medical areas for improving decision making, has been studied minimally in the clinical setting. In this study, we undertook, to our knowledge, the first domain adaptation of sentiment analysis to psychiatric EHRs by defining psychiatric clinical sentiment, performing an annotation project, and evaluating multiple sentence-level sentiment machine learning (ML) models. Results indicate that off-the-shelf sentiment analysis tools fail in identifying clinically positive or negative polarity, and that the definition of clinical sentiment that we provide is learnable with relatively small amounts of training data. This project is an initial step towards further refining sentiment analysis methods for clinical use. Our long-term objective is to incorporate the results of this project as part of a machine learning model that predicts inpatient readmission risk. We hope that this work will initiate a discussion concerning domain adaptation of sentiment analysis to the clinical setting.
Word embeddings are representations of words in a dense vector space. Although they are not recent phenomena in Natural Language Processing (NLP), they have gained momentum after the recent developments of neural methods and Word2Vec. Regarding their applications in medical and clinical NLP, they are invaluable resources when training in-domain named entity recognition systems, classifiers or taggers, for instance. Thus, the development of tailored word embeddings for medical NLP is of great interest. However, we identified a gap in the literature which we aim to fill in this paper: the availability of embeddings for medical NLP in Spanish, as well as a standardized form of intrinsic evaluation. Since most work has been done for English, some established datasets for intrinsic evaluation are already available. In this paper, we show the steps we employed to adapt such datasets for the first time to Spanish, of particular relevance due to the considerable volume of EHRs in this language, as well as the creation of in-domain medical word embeddings for the Spanish using the state-of-the-art FastText model. We performed intrinsic evaluation with our adapted datasets, as well as extrinsic evaluation with a named entity recognition systems using a baseline embedding of general-domain. Both experiments proved that our embeddings are suitable for use in medical NLP in the Spanish language, and are more accurate than general-domain ones.
Neural network models have shown promise in the temporal relation extraction task. In this paper, we present the attention based neural network model to extract the containment relations within sentences from clinical narratives. The attention mechanism used on top of GRU model outperforms the existing state-of-the-art neural network models on THYME corpus in intra-sentence temporal relation extraction.
Electronic health records (EHRs) are notorious for reducing the face-to-face time with patients while increasing the screen-time for clinicians leading to burnout. This is especially problematic for psychiatry care in which maintaining consistent eye-contact and non-verbal cues are just as important as the spoken words. In this ongoing work, we explore the feasibility of automatically generating psychiatric EHR case notes from digital transcripts of doctor-patient conversation using a two-step approach: (1) predicting semantic topics for segments of transcripts using supervised machine learning, and (2) generating formal text of those segments using natural language processing. Through a series of preliminary experimental results obtained through a collection of synthetic and real-life transcripts, we demonstrate the viability of this approach.
Past prescriptions constitute a central element in patient records. These are often written in an unstructured and brief form. Extracting information from such prescriptions enables the development of automated processes in the medical data mining field. This paper presents a Conditional Random Fields (CRFs) based approach to extract relevant information from prescriptions. We focus on Finnish language prescriptions and make use of Finnish language specific features. Our labeling accuracy is 95%, which compares favorably to the current state-of-the-art in English language prescriptions. This, to the best of our knowledge, is the first such work for the Finnish language.
Distributed word vector spaces are considered hard to interpret which hinders the understanding of natural language processing (NLP) models. In this work, we introduce a new method to interpret arbitrary samples from a word vector space. To this end, we train a neural model to conceptualize word vectors, which means that it activates higher order concepts it recognizes in a given vector. Contrary to prior approaches, our model operates in the original vector space and is capable of learning non-linear relations between word vectors and concepts. Furthermore, we show that it produces considerably less entropic concept activation profiles than the popular cosine similarity.
Analysis of word embedding properties to inform their use in downstream NLP tasks has largely been studied by assessing nearest neighbors. However, geometric properties of the continuous feature space contribute directly to the use of embedding features in downstream models, and are largely unexplored. We consider four properties of word embedding geometry, namely: position relative to the origin, distribution of features in the vector space, global pairwise distances, and local pairwise distances. We define a sequence of transformations to generate new embeddings that expose subsets of these properties to downstream models and evaluate change in task performance to understand the contribution of each property to NLP models. We transform publicly available pretrained embeddings from three popular toolkits (word2vec, GloVe, and FastText) and evaluate on a variety of intrinsic tasks, which model linguistic information in the vector space, and extrinsic tasks, which use vectors as input to machine learning models. We find that intrinsic evaluations are highly sensitive to absolute position, while extrinsic tasks rely primarily on local similarity. Our findings suggest that future embedding models and post-processing techniques should focus primarily on similarity to nearby points in vector space.
The stability of word embedding algorithms, i.e., the consistency of the word representations they reveal when trained repeatedly on the same data set, has recently raised concerns. We here compare word embedding algorithms on three corpora of different sizes, and evaluate both their stability and accuracy. We find strong evidence that down-sampling strategies (used as part of their training procedures) are particularly influential for the stability of SVD-PPMI-type embeddings. This finding seems to explain diverging reports on their stability and lead us to a simple modification which provides superior stability as well as accuracy on par with skip-gram embedding
In this paper, we apply various embedding methods on multiword expressions to study how well they capture the nuances of non-compositional data. Our results from a pool of word-, character-, and document-level embbedings suggest that Word2vec performs the best, followed by FastText and Infersent. Moreover, we find that recently-proposed contextualised embedding models such as Bert and ELMo are not adept at handling non-compositionality in multiword expressions.
In this paper, we investigate whether multilingual neural translation models learn stronger semantic abstractions of sentences than bilingual ones. We test this hypotheses by measuring the perplexity of such models when applied to paraphrases of the source language. The intuition is that an encoder produces better representations if a decoder is capable of recognizing synonymous sentences in the same language even though the model is never trained for that task. In our setup, we add 16 different auxiliary languages to a bidirectional bilingual baseline model (English-French) and test it with in-domain and out-of-domain paraphrases in English. The results show that the perplexity is significantly reduced in each of the cases, indicating that meaning can be grounded in translation. This is further supported by a study on paraphrase generation that we also include at the end of the paper.
Downstream evaluation of pretrained word embeddings is expensive, more so for tasks where current state of the art models are very large architectures. Intrinsic evaluation using word similarity or analogy datasets, on the other hand, suffers from several disadvantages. We propose a novel intrinsic evaluation task employing large word association datasets (particularly the Small World of Words dataset). We observe correlations not just between performances on SWOW-8500 and previously proposed intrinsic tasks of word similarity prediction, but also with downstream tasks (eg. Text Classification and Natural Language Inference). Most importantly, we report better confidence intervals for scores on our word association task, with no fall in correlation with downstream performance.
In clinical assessment of people with aphasia, impairment in the ability to recall and produce words for objects (anomia) is assessed using a confrontation naming task, where a target stimulus is viewed and a corresponding label is spoken by the participant. Vector space word embedding models have had inital results in assessing semantic similarity of target-production pairs in order to automate scoring of this task; however, the resulting models are also highly dependent upon training parameters. To select an optimal family of models, we fit a beta regression model to the distribution of performance metrics on a set of 2,880 grid search models and evaluate the resultant first- and second-order effects to explore how parameterization affects model performance. Comparing to SimLex-999, we show that clinical data can be used in an evaluation task with comparable optimal parameter settings as standard NLP evaluation datasets.
Commonsense reasoning is a critical AI capability, but it is difficult to construct challenging datasets that test common sense. Recent neural question answering systems, based on large pre-trained models of language, have already achieved near-human-level performance on commonsense knowledge benchmarks. These systems do not possess human-level common sense, but are able to exploit limitations of the datasets to achieve human-level scores. We introduce the CODAH dataset, an adversarially-constructed evaluation dataset for testing common sense. CODAH forms a challenging extension to the recently-proposed SWAG dataset, which tests commonsense knowledge using sentence-completion questions that describe situations observed in video. To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems. Workers are rewarded for submissions that models fail to answer correctly both before and after fine-tuning (in cross-validation). We create 2.8k questions via this procedure and evaluate the performance of multiple state-of-the-art question answering systems on our dataset. We observe a significant gap between human performance, which is 95.3%, and the performance of the best baseline accuracy of 65.3% by the OpenAI GPT model.
Nearest neighbors in word embedding models are commonly observed to be semantically similar, but the relations between them can vary greatly. We investigate the extent to which word embedding models preserve syntactic interchangeability, as reflected by distances between word vectors, and the effect of hyper-parameters—context window size in particular. We use part of speech (POS) as a proxy for syntactic interchangeability, as generally speaking, words with the same POS are syntactically valid in the same contexts. We also investigate the relationship between interchangeability and similarity as judged by commonly-used word similarity benchmarks, and correlate the result with the performance of word embedding models on these benchmarks. Our results will inform future research and applications in the selection of word embedding model, suggesting a principle for an appropriate selection of the context window size parameter depending on the use-case.
This paper evaluates morphology-based embeddings for English and Russian languages. Despite the interest and introduction of several morphology based word embedding models in the past and acclaimed performance improvements on word similarity and language modeling tasks, in our experiments, we did not observe any stable preference over two of our baseline models - SkipGram and FastText. The performance exhibited by morphological embeddings is the average of the two baselines mentioned above.
Contextualized word embeddings derived from pre-trained language models (LMs) show significant improvements on downstream NLP tasks. Pre-training on domain-specific corpora, such as biomedical articles, further improves their performance. In this paper, we conduct probing experiments to determine what additional information is carried intrinsically by the in-domain trained contextualized embeddings. For this we use the pre-trained LMs as fixed feature extractors and restrict the downstream task models to not have additional sequence modeling layers. We compare BERT (Devlin et al. 2018), ELMo (Peters et al., 2018), BioBERT (Lee et al., 2019) and BioELMo, a biomedical version of ELMo trained on 10M PubMed abstracts. Surprisingly, while fine-tuned BioBERT is better than BioELMo in biomedical NER and NLI tasks, as a fixed feature extractor BioELMo outperforms BioBERT in our probing tasks. We use visualization and nearest neighbor analysis to show that better encoding of entity-type and relational information leads to this superiority.
This paper explores modern word embeddings in the context of sound symbolism. Using basic properties of the representations space one can construct semantic axes. A method is proposed to measure if the presence of individual sounds in a given word shifts its semantics of that word along a specific axis. It is shown that, in accordance with several experimental and statistical results, word embeddings capture symbolism for certain sounds.
In this paper, we present a novel algorithm that combines multi-context term embeddings using a neural classifier and we test this approach on the use case of corpus-based term set expansion. In addition, we present a novel and unique dataset for intrinsic evaluation of corpus-based term set expansion algorithms. We show that, over this dataset, our algorithm provides up to 5 mean average precision points over the best baseline.
Naming and titling have been discussed in sociolinguistics as markers of status or solidarity. However, these functions have not been studied on a larger scale or for social media data. We collect a corpus of tweets mentioning presidents of six G20 countries by various naming forms. We show that naming variation relates to stance towards the president in a way that is suggestive of a framing effect mediated by respectfulness. This confirms sociolinguistic theory of naming and titling as markers of status.
Social scientists have recently turned to analyzing text using tools from natural language processing like word embeddings to measure concepts like ideology, bias, and affinity. However, word embeddings are difficult to use in the regression framework familiar to social scientists: embeddings are are neither identified, nor directly interpretable. I offer two advances on standard embedding models to remedy these problems. First, I develop Bayesian Word Embeddings with Automatic Relevance Determination priors, relaxing the assumption that all embedding dimensions have equal weight. Second, I apply work identifying latent variable models to anchor embeddings, identifying them, and making them interpretable and usable in a regression. I then apply this model and anchoring approach to two cases, the shift in internationalist rhetoric in the American presidents’ inaugural addresses, and the relationship between bellicosity in American foreign policy decision-makers’ deliberations. I find that inaugural addresses became less internationalist after 1945, which goes against the conventional wisdom, and that an increase in bellicosity is associated with an increase in hostile actions by the United States, showing that elite deliberations are not cheap talk, and helping confirm the validity of the model.
NLP naturally puts a primary focus on leveraging document language, occasionally considering user attributes as supplemental. However, as we tackle more social scientific tasks, it is possible user attributes might be of primary importance and the document supplemental. Here, we systematically investigate the predictive power of user-level features alone versus document-level features for document-level tasks. We first show user attributes can sometimes carry more task-related information than the document itself. For example, a tweet-level stance detection model using only 13 user-level attributes (i.e. features that did not depend on the specific tweet) was able to obtain a higher F1 than the top-performing SemEval participant. We then consider multiple tasks and a wider range of user attributes, showing the performance of strong document-only models can often be improved (as in stance, sentiment, and sarcasm) with user attributes, particularly benefiting tasks with stable “trait-like” outcomes (e.g. stance) most relative to frequently changing “state-like” outcomes (e.g. sentiment). These results not only support the growing work on integrating user factors into predictive systems, but that some of our NLP tasks might be better cast primarily as user-level (or human) tasks.
This work introduces a general method for automatically finding the locations where political events in text occurred. Using a novel set of 8,000 labeled sentences, I create a method to link automatically extracted events and locations in text. The model achieves human level performance on the annotation task and outperforms previous event geolocation systems. It can be applied to most event extraction systems across geographic contexts. I formalize the event–location linking task, describe the neural network model, describe the potential uses of such a system in political science, and demonstrate a workflow to answer an open question on the role of conventional military offensives in causing civilian casualties in the Syrian civil war.
Internet censorship imposes restrictions on what information can be publicized or viewed on the Internet. According to Freedom House’s annual Freedom on the Net report, more than half the world’s Internet users now live in a place where the Internet is censored or restricted. China has built the world’s most extensive and sophisticated online censorship system. In this paper, we describe a new corpus of censored and uncensored social media tweets from a Chinese microblogging website, Sina Weibo, collected by tracking posts that mention ‘sensitive’ topics or authored by ‘sensitive’ users. We use this corpus to build a neural network classifier to predict censorship. Our model performs with a 88.50% accuracy using only linguistic features. We discuss these features in detail and hypothesize that they could potentially be used for censorship circumvention.
Cognitive tests have traditionally resorted to standardizing testing materials in the name of equality and because of the onerous nature of creating test items. This approach ignores participants’ diverse language experiences that potentially significantly affect testing outcomes. Here, we seek to explain our prior finding of significant performance differences on two cognitive tests (reading span and SPiN) between clusters of participants based on their media consumption. Here, we model the language contained in these media sources using an LSTM trained on corpora of each cluster’s media sources to predict target words. We also model semantic similarity of test items with each cluster’s corpus using skip-thought vectors. We find robust, significant correlations between performance on the SPiN test and the LSTMs and skip-thought models we present here, but not the reading span test.
This paper proposes a method for identifying and studying viral moments or highlights during a political debate. Using a combined strategy of time series analysis and domain adapted word embeddings, this study provides an in-depth analysis of several key moments during the 2016 U.S. Presidential election. First, a time series outlier analysis is used to identify key moments during the debate. These moments had to result in a long-term shift in attention towards either Hillary Clinton or Donald Trump (i.e., a transient change outlier or an intervention, resulting in a permanent change in the time series). To assess whether these moments also resulted in a discursive shift, two corpora are produced for each potential viral moment (a pre-viral corpus and post-viral corpus). A domain adaptation layer learns weights to combine a generic and domain-specific (DS) word embedding into a domain adapted (DA) embedding. Words are then classified using a generic encoder+ classifier framework that relies on these word embeddings as inputs. Results suggest that both Clinton and Trump were able to induce discourse-shifting viral moments, though the former is much better at producing a topically-specific discursive shift.
In group decision-making, the nuanced process of conflict and resolution that leads to consensus formation is closely tied to the quality of decisions made. Behavioral scientists rarely have rich access to process variables, though, as unstructured discussion transcripts are difficult to analyze. Here, we define ways for NLP researchers to contribute to the study of groups and teams. We introduce three tasks alongside a large new corpus of over 400,000 group debates on Wikipedia. We describe the tasks and their importance, then provide baselines showing that BERT contextualized word embeddings consistently outperform other language representations.
Online social media platforms such as Facebook and Twitter are increasingly facing criticism for polarization of users. One particular aspect which has caught the attention of various critics is presence of users in echo chambers - a situation wherein users are exposed mostly to the opinions which are in sync with their own views. In this paper, we perform a sociolinguistic study by comparing the tweets of users in echo chambers with the tweets of users not in echo chambers with similar levels of polarity on a broad topic. Specifically, we carry out a comparative analysis of tweet structure, lexical choices, and focus issues, and provide possible explanations for the results.
News consumption exhibits an increasing shift towards online sources, which bring platforms such as YouTube more into focus. Thus, the distribution of politically loaded news is easier, receives more attention, but also raises the concern of forming isolated ideological communities. Understanding how such news is communicated and received is becoming increasingly important. To expand our understanding in this domain, we apply a linguistic temporal trajectory analysis to analyze sentiment patterns in English-language videos from news channels on YouTube. We examine transcripts from videos distributed through eight channels with pro-left and pro-right political leanings. Using unsupervised clustering, we identify seven different sentiment patterns in the transcripts. We found that the use of two sentiment patterns differed significantly depending on political leaning. Furthermore, we used predictive models to examine how different sentiment patterns relate to video popularity and if they differ depending on the channel’s political leaning. No clear relations between sentiment patterns and popularity were found. However, results indicate, that videos from pro-right news channels are more popular and that a negative sentiment further increases that popularity, when sentiments are averaged for each video.
Word embeddings trained on large-scale historical corpora can illuminate human biases and stereotypes that perpetuate social inequalities. These embeddings are often trained in separate vector space models defined according to different attributes of interest. In this paper, we introduce a single, unified dynamic embedding model that learns attribute-specific word embeddings and apply it to a novel dataset—talk radio shows from around the US—to analyze perceptions about refugees. We validate our model on a benchmark dataset and apply it to two corpora of talk radio shows averaging 117 million words produced over one month across 83 stations and 64 cities. Our findings suggest that dynamic word embeddings are capable of identifying nuanced differences in public discourse about contentious topics, suggesting their usefulness as a tool for better understanding how the public perceives and engages with different issues across time, geography, and other dimensions.
Political discourse on social media microblogs, specifically Twitter, has become an undeniable part of mainstream U.S. politics. Given the length constraint of tweets, politicians must carefully word their statements to ensure their message is understood by their intended audience. This constraint often eliminates the context of the tweet, making automatic analysis of social media political discourse a difficult task. To overcome this challenge, we propose simultaneous modeling of high-level abstractions of political language, such as political slogans and framing strategies, with abstractions of how politicians behave on Twitter. These behavioral abstractions can be further leveraged as forms of supervision in order to increase prediction accuracy, while reducing the burden of annotation. In this work, we use Probabilistic Soft Logic (PSL) to build relational models to capture the similarities in language and behavior that obfuscate political messages on Twitter. When combined, these descriptors reveal the moral foundations underlying the discourse of U.S. politicians online, across differing governing administrations, showing how party talking points remain cohesive or change over time.
Unilateral legal contracts, such as terms of service, play a substantial role in modern digital life. However, few read these documents before accepting the terms within, as they are too long and the language too complicated. We propose the task of summarizing such legal documents in plain English, which would enable users to have a better understanding of the terms they are accepting. We propose an initial dataset of legal text snippets paired with summaries written in plain English. We verify the quality of these summaries manually, and show that they involve heavy abstraction, compression, and simplification. Initial experiments show that unsupervised extractive summarization methods do not perform well on this task due to the level of abstraction and style differences. We conclude with a call for resource and technique development for simplification and style transfer for legal language.
Recent research has demonstrated that judicial and administrative decisions can be predicted by machine-learning models trained on prior decisions. However, to have any practical application, these predictions must be explainable, which in turn requires modeling a rich set of features. Such approaches face a roadblock if the knowledge engineering required to create these features is not scalable. We present an approach to developing a feature-rich corpus of administrative rulings about domain name disputes, an approach which leverages a small amount of manual annotation and prototypical patterns present in the case documents to automatically extend feature labels to the entire corpus. To demonstrate the feasibility of this approach, we report results from systems trained on this dataset.
Contract language is repetitive (Anderson and Manns, 2017), but so is all language (Zipf, 1949). In this paper, we measure the extent to which contract language in English is repetitive compared with the language of other English language corpora. Contracts have much smaller vocabulary sizes compared with similarly sized non-contract corpora across multiple contract types, contain 1/5th as many hapax legomena, pattern differently on a log-log plot, use fewer pronouns, and contain sentences that are about 20% more similar to one another than in other corpora. These suggest that the study of contracts in natural language processing controls for some linguistic phenomena and allows for more in depth study of others.
In this paper, we examined several algorithms to detect sentence boundaries in legal text. Legal text presents challenges for sentence tokenizers because of the variety of punctuations and syntax of legal text. Out-of-the-box algorithms perform poorly on legal text affecting further analysis of the text. A novel and domain-specific approach is needed to detect sentence boundaries to further analyze legal text. We present the results of our investigation in this paper.
This paper describes a dataset and baseline systems for linking paragraphs from court cases to clauses or amendments in the US Constitution. We implement a rule-based system, a linear model, and a neural architecture for matching pairs of paragraphs, taking training data from online databases in a distantly-supervised fashion. In experiments on a manually-annotated evaluation set, we find that our proposed neural system outperforms a rules-driven baseline. Qualitatively, this performance gap seems largest for abstract or indirect links between documents, which suggests that our system might be useful for answering political science and legal research questions or discovering novel links. We release the dataset along with the manually-annotated evaluation set to foster future work.
Dockets contain a wealth of information for planning a litigation strategy, but the information is locked up in semi-structured text. Manually deriving the outcomes for each party (e.g., settlement, verdict) would be very labor intensive. Having such information available for every past court case, however, would be very useful for developing a strategy because it potentially reveals tendencies and trends of judges and courts and the opposing counsel. We used Natural Language Processing (NLP) techniques and deep learning methods allowing us to scale the automatic analysis of millions of US federal court dockets. The automatically extracted information is fed into a Litigation Analytics tool that is used by lawyers to plan how they approach concrete litigations.
We present a portfolio of natural legal language processing and document curation services currently under development in a collaborative European project. First, we give an overview of the project and the different use cases, while, in the main part of the article, we focus upon the 13 different processing services that are being deployed in different prototype applications using a flexible and scalable microservices architecture. Their orchestration is operationalised using a content and document curation workflow manager.
This paper conducts a comparative study on the performance of various machine learning approaches for classifying judgments into legal areas. Using a novel dataset of 6,227 Singapore Supreme Court judgments, we investigate how state-of-the-art NLP methods compare against traditional statistical models when applied to a legal corpus that comprised few but lengthy documents. All approaches tested, including topic model, word embedding, and language model-based classifiers, performed well with as little as a few hundred judgments. However, more work needs to be done to optimize state-of-the-art methods for the legal domain.
We consider the task of Extreme Multi-Label Text Classification (XMTC) in the legal domain. We release a new dataset of 57k legislative documents from EURLEX, the European Union’s public document database, annotated with concepts from EUROVOC, a multidisciplinary thesaurus. The dataset is substantially larger than previous EURLEX datasets and suitable for XMTC, few-shot and zero-shot learning. Experimenting with several neural classifiers, we show that BIGRUs with self-attention outperform the current multi-label state-of-the-art methods, which employ label-wise attention. Replacing CNNs with BIGRUs in label-wise attention networks leads to the best overall performance.
In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to a multi-turn dialogue scenario by modifying the state-of-the-art hredGAN architecture to simultaneously capture utterance attributes such as speaker identity, dialogue topic, speaker sentiments and so on. The proposed system, phredGAN has a persona-based HRED generator (PHRED) and a conditional discriminator. We also explore two approaches to accomplish the conditional discriminator: (1) phredGANa, a system that passes the attribute representation as an additional input into a traditional adversarial discriminator, and (2) phredGANd, a dual discriminator system which in addition to the adversarial discriminator, collaboratively predicts the attribute(s) that generated the input utterance. To demonstrate the superior performance of phredGAN over the persona Seq2Seq model, we experiment with two conversational datasets, the Ubuntu Dialogue Corpus (UDC) and TV series transcripts from the Big Bang Theory and Friends. Performance comparison is made with respect to a variety of quantitative measures as well as crowd-sourced human evaluation. We also explore the trade-offs from using either variant of phredGAN on datasets with many but weak attribute modalities (such as with Big Bang Theory and Friends) and ones with few but strong attribute modalities (customer-agent interactions in Ubuntu dataset).
In open-domain dialogue systems, generative approaches have attracted much attention for response generation. However, existing methods are heavily plagued by generating safe responses and unnatural responses. To alleviate these two problems, we propose a novel framework named Dual Adversarial Learning(DAL) for high-quality response generation. DAL innovatively utilizes the duality between query generation and response generation to avoid safe responses and increase the diversity of the generated responses. Additionally, DAL uses adversarial learning to mimic human judges and guides the system to generate natural responses. Experimental results demonstrate that DAL effectively improves both diversity and overall quality of the generated responses. DAL outperforms state-of-the-art methods regarding automatic metrics and human evaluations.
We show that plain ROUGE F1 scores are not ideal for comparing current neural systems which on average produce different lengths. This is due to a non-linear pattern between ROUGE F1 and summary length. To alleviate the effect of length during evaluation, we have proposed a new method which normalizes the ROUGE F1 scores of a system by that of a random system with same average output length. A pilot human evaluation has shown that humans prefer short summaries in terms of the verbosity of a summary but overall consider longer summaries to be of higher quality. While human evaluations are more expensive in time and resources, it is clear that normalization, such as the one we proposed for automatic evaluation, will make human evaluations more meaningful.
We show that BERT (Devlin et al., 2018) is a Markov random field language model. This formulation gives way to a natural procedure to sample sentences from BERT. We generate from BERT and find that it can produce high quality, fluent generations. Compared to the generations of a traditional left-to-right language model, BERT generates sentences that are more diverse but of slightly worse quality.
Neural text simplification has gained increasing attention in the NLP community thanks to recent advancements in deep sequence-to-sequence learning. Most recent efforts with such a data-demanding paradigm have dealt with the English language, for which sizeable training datasets are currently available to deploy competitive models. Similar improvements on less resource-rich languages are conditioned either to intensive manual work to create training data, or to the design of effective automatic generation techniques to bypass the data acquisition bottleneck. Inspired by the machine translation field, in which synthetic parallel pairs generated from monolingual data yield significant improvements to neural models, in this paper we exploit large amounts of heterogeneous data to automatically select simple sentences, which are then used to create synthetic simplification pairs. We also evaluate other solutions, such as oversampling and the use of external word embeddings to be fed to the neural simplification system. Our approach is evaluated on Italian and Spanish, for which few thousand gold sentence pairs are available. The results show that these techniques yield performance improvements over a baseline sequence-to-sequence configuration.
Semi-supervised learning is an efficient way to improve performance for natural language processing systems. In this work, we propose Para-SSL, a scheme to generate candidate utterances using paraphrasing and methods from semi-supervised learning. In order to perform paraphrase generation in the context of a dialog system, we automatically extract paraphrase pairs to create a paraphrase corpus. Using this data, we build a paraphrase generation system and perform one-to-many generation, followed by a validation step to select only the utterances with good quality. The paraphrase-based semi-supervised learning is applied to five functionalities in a natural language understanding system. Our proposed method for semi-supervised learning using paraphrase generation does not require user utterances and can be applied prior to releasing a new functionality to a system. Experiments show that we can achieve up to 19% of relative slot error reduction without an access to user utterances, and up to 35% when leveraging live traffic utterances.
Latent space based GAN methods and attention based sequence to sequence models have achieved impressive results in text generation and unsupervised machine translation respectively. Leveraging the two domains, we propose an adversarial latent space based model capable of generating parallel sentences in two languages concurrently and translating bidirectionally. The bilingual generation goal is achieved by sampling from the latent space that is shared between both languages. First two denoising autoencoders are trained, with shared encoders and back-translation to enforce a shared latent state between the two languages. The decoder is shared for the two translation directions. Next, a GAN is trained to generate synthetic ‘code’ mimicking the languages’ shared latent space. This code is then fed into the decoder to generate text in either language. We perform our experiments on Europarl and Multi30k datasets, on the English-French language pair, and document our performance using both supervised and unsupervised machine translation.
Generated output from neural NLG systems often contain errors such as hallucination, repetition or contradiction. This work focuses on designing a symbolic intermediate representation to be used in multi-stage neural generation with the intention of reducing the frequency of failed outputs. We show that surface realization from this intermediate representation is of high quality and when the full system is applied to the E2E dataset it outperforms the winner of the E2E challenge. Furthermore, by breaking out the surface realization step from typically end-to-end neural systems, we also provide a framework for non-neural based content selection and planning systems to potentially take advantage of semi-supervised pretraining of neural surface realization models.
We introduce a simple method for text style transfer that frames style transfer as denoising: we synthesize a noisy corpus and treat the source style as a noisy version of the target style. To control for aspects such as preserving meaning while modifying style, we propose a reranking approach in the data synthesis phase. We evaluate our method on three novel style transfer tasks: transferring between British and American varieties, text genres (formal vs. casual), and lyrics from different musical genres. By measuring style transfer quality, meaning preservation, and the fluency of generated outputs, we demonstrate that our method is able both to produce high-quality output while maintaining the flexibility to suggest syntactically rich stylistic edits.
Despite advances in open-domain dialogue systems, automatic evaluation of such systems is still a challenging problem. Traditional reference-based metrics such as BLEU are ineffective because there could be many valid responses for a given context that share no common words with reference responses. A recent work proposed Referenced metric and Unreferenced metric Blended Evaluation Routine (RUBER) to combine a learning-based metric, which predicts relatedness between a generated response and a given query, with reference-based metric; it showed high correlation with human judgments. In this paper, we explore using contextualized word embeddings to compute more accurate relatedness scores, thus better evaluation metrics. Experiments show that our evaluation metrics outperform RUBER, which is trained on static embeddings.
Text generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text generation methods, each of them has its own shortcomings. The most widely used metrics such as BLEU only consider the quality of generated sentences and neglecting their diversity. For example, repeatedly generation of only one high quality sentence would result in a high BLEU score. On the other hand, the more recent metric introduced to evaluate the diversity of generated texts known as Self-BLEU ignores the quality of generated texts. In this paper, we propose metrics to evaluate both the quality and diversity simultaneously by approximating the distance of the learned generative model and the real data distribution. For this purpose, we first introduce a metric that approximates this distance using n-gram based measures. Then, a feature-based measure which is based on a recent highly deep model trained on a large text corpus called BERT is introduced. Finally, for oracle training mode in which the generatorʼs density can also be calculated, we propose to use the distance measures between the corresponding explicit distributions. Eventually, the most popular and recent text generation models are evaluated using both the existing and the proposed metrics and the preferences of the proposed metrics are determined.
Generating coherent and cohesive long-form texts is a challenging task. Previous works relied on large amounts of human-generated texts to train neural language models. However, few attempted to explicitly improve neural language models from the perspectives of coherence and cohesion. In this work, we propose a new neural language model that is equipped with two neural discriminators which provide feedback signals at the levels of sentence (cohesion) and paragraph (coherence). Our model is trained using a simple yet efficient variant of policy gradient, called ‘negative-critical sequence training’, which is proposed to eliminate the need of training a separate critic for estimating ‘baseline’. Results demonstrate the effectiveness of our approach, showing improvements over the strong baseline – recurrent attention-based bidirectional MLE-trained neural language model.
Characters are a key element of narrative and so character identification plays an important role in automatic narrative understanding. Unfortunately, most prior work that incorporates character identification is not built upon a clear, theoretically grounded concept of character. They either take character identification for granted (e.g., using simple heuristics on referring expressions), or rely on simplified definitions that do not capture important distinctions between characters and other referents in the story. Prior approaches have also been rather complicated, relying, for example, on predefined case bases or ontologies. In this paper we propose a narratologically grounded definition of character for discussion at the workshop, and also demonstrate a preliminary yet straightforward supervised machine learning model with a small set of features that performs well on two corpora. The most important of the two corpora is a set of 46 Russian folktales, on which the model achieves an F1 of 0.81. Error analysis suggests that features relevant to the plot will be necessary for further improvements in performance.
Early proposals for the deep understanding of natural language text advocated an approach of “interpretation as abduction,” where the meaning of a text was derived as an explanation that logically entailed the input words, given a knowledge base of lexical and commonsense axioms. While most subsequent NLP research has instead pursued statistical and data-driven methods, the approach of interpretation as abduction has seen steady advancements in both theory and software implementations. In this paper, we summarize advances in deriving the logical form of the text, encoding commonsense knowledge, and technologies for scalable abductive reasoning. We then explore the application of these advancements to the deep understanding of a paragraph of news text, where the subtle meaning of words and phrases are resolved by backward chaining on a knowledge base of 80 hand-authored axioms.
In this paper, we advocate the use of Message Sequence Chart (MSC) as a knowledge representation to capture and visualize multi-actor interactions and their temporal ordering. We propose algorithms to automatically extract an MSC from a history narrative. For a given narrative, we first identify verbs which indicate interactions and then use dependency parsing and Semantic Role Labelling based approaches to identify senders (initiating actors) and receivers (other actors involved) for these interaction verbs. As a final step in MSC extraction, we employ a state-of-the art algorithm to temporally re-order these interactions. Our evaluation on multiple publicly available narratives shows improvements over four baselines.
Story infilling involves predicting words to go into a missing span from a story. This challenging task has the potential to transform interactive tools for creative writing. However, state-of-the-art conditional language models have trouble balancing fluency and coherence with novelty and diversity. We address this limitation with a hierarchical model which first selects a set of rare words and then generates text conditioned on that set. By relegating the high entropy task of picking rare words to a word-sampling model, the second-stage model conditioned on those words can achieve high fluency and coherence by searching for likely sentences, without sacrificing diversity.
As with many text generation tasks, the focus of recent progress on story generation has been in producing texts that are perceived to “make sense” as a whole. There are few automated metrics that address this dimension of story quality even on a shallow lexical level. To initiate investigation into such metrics, we apply a simple approach to identifying word relations that contribute to the ‘narrative sense’ of a story. We use this approach to comparatively analyze the output of a few notable story generation systems in terms of these relations. We characterize differences in the distributions of relations according to their strength within each story.
To understand historical texts, we must be aware that language—including the emotional connotation attached to words—changes over time. In this paper, we aim at estimating the emotion which is associated with a given word in former language stages of English and German. Emotion is represented following the popular Valence-Arousal-Dominance (VAD) annotation scheme. While being more expressive than polarity alone, existing word emotion induction methods are typically not suited for addressing it. To overcome this limitation, we present adaptations of two popular algorithms to VAD. To measure their effectiveness in diachronic settings, we present the first gold standard for historical word emotions, which was created by scholars with proficiency in the respective language stages and covers both English and German. In contrast to claims in previous work, our findings indicate that hand-selecting small sets of seed words with supposedly stable emotional meaning is actually harm- rather than helpful.
This article focuses on the problem of identifying articles and recovering their text from within and across newspaper pages when OCR just delivers one text file per page. We frame the task as a segmentation plus clustering step. Our results on a sample of 1912 New York Tribune magazine shows that performing the clustering based on similarities computed with word embeddings outperforms a similarity measure based on character n-grams and words. Furthermore, the automatic segmentation based on the text results in low scores, due to the low quality of some OCRed documents.
Scholarly practices within the humanities have historically been perceived as distinct from the natural sciences. We look at literary studies, a discipline strongly anchored in the humanities, and hypothesize that over the past half-century literary studies has instead undergone a process of “scientization”, adopting linguistic behavior similar to the sciences. We test this using methods based on information theory, comparing a corpus of literary studies articles (around 63,400) with a corpus of standard English and scientific English respectively. We show evidence for “scientization” effects in literary studies, though at a more muted level than scientific English, suggesting that literary studies occupies a middle ground with respect to standard English in the larger space of academic disciplines. More generally, our methodology can be applied to investigate the social positioning and development of language use across different domains (e.g. scientific disciplines, language varieties, registers).
According to the literary theory of Mikhail Bakhtin, a dialogic novel is one in which characters speak in their own distinct voices, rather than serving as mouthpieces for their authors. We use text classification to determine which authors best achieve dialogism, looking at a corpus of plays from the late nineteenth and early twentieth centuries. We find that the SAGE model of text generation, which highlights deviations from a background lexical distribution, is an effective method of weighting the words of characters’ utterances. Our results show that it is indeed possible to distinguish characters by their speech in the plays of canonical writers such as George Bernard Shaw, whereas characters are clustered more closely in the works of lesser-known playwrights.
This paper presents a modular NLP pipeline for the creation of a parallel literature corpus, followed by annotation transfer from the source to the target language. The test case we use to evaluate our pipeline is the automatic transfer of quote and speaker mention annotations from English to German. We evaluate the different components of the pipeline and discuss challenges specific to literary texts. Our experiments show that after applying a reasonable amount of semi-automatic postprocessing we can obtain high-quality aligned and annotated resources for a new language.
The National Environmental Policy Act (NEPA) provides a trove of data on how environmental policy decisions have been made in the United States over the last 50 years. Unfortunately, there is no central database for this information and it is too voluminous to assess manually. We describe our efforts to enable systematic research over US environmental policy by extracting and organizing metadata from the text of NEPA documents. Our contributions include collecting more than 40,000 NEPA-related documents, and evaluating rule-based baselines that establish the difficulty of three important tasks: identifying lead agencies, aligning document versions, and detecting reused text.
Classification of texts by genre is an important application of natural language processing to literary corpora but remains understudied for premodern and non-English traditions. We develop a stylometric feature set for ancient Greek that enables identification of texts as prose or verse. The set contains over 20 primarily syntactic features, which are calculated according to custom, language-specific heuristics. Using these features, we classify almost all surviving classical Greek literature as prose or verse with >97% accuracy and F1 score, and further classify a selection of the verse texts into the traditional genres of epic and drama.
In the Humanities and Social Sciences, there is increasing interest in approaches to information extraction, prediction, intelligent linkage, and dimension reduction applicable to large text corpora. With approaches in these fields being grounded in traditional statistical techniques, the need arises for frameworks whereby advanced NLP techniques such as topic modelling may be incorporated within classical methodologies. This paper provides a classical, supervised, statistical learning framework for prediction from text, using topic models as a data reduction method and the topics themselves as predictors, alongside typical statistical tools for predictive modelling. We apply this framework in a Social Sciences context (applied animal behaviour) as well as a Humanities context (narrative analysis) as examples of this framework. The results show that topic regression models perform comparably to their much less efficient equivalents that use individual words as predictors.
This paper studies the use of NMT (neural machine translation) as a normalization method for an early English letter corpus. The corpus has previously been normalized so that only less frequent deviant forms are left out without normalization. This paper discusses different methods for improving the normalization of these deviant forms by using different approaches. Adding features to the training data is found to be unhelpful, but using a lexicographical resource to filter the top candidates produced by the NMT model together with lemmatization improves results.
This work considers a task from traditional literary criticism: annotating a structured, composite document with information about its sources. We take the Documentary Hypothesis, a prominent theory regarding the composition of the first five books of the Hebrew bible, extract stylistic features designed to avoid bias or overfitting, and train several classification models. Our main result is that the recently-introduced graph convolutional network architecture outperforms structurally-uninformed models. We also find that including information about the granularity of text spans is a crucial ingredient when employing hidden layers, in contrast to simple logistic regression. We perform error analysis at several levels, noting how some characteristic limitations of the models and simple features lead to misclassifications, and conclude with an overview of future work.
We have created two sets of labels for Hafez (1315-1390) poems, using unsupervised learning. Our labels are the only semantic clustering alternative to the previously existing, hand-labeled, gold-standard classification of Hafez poems, to be used for literary research. We have cross-referenced, measured and analyzed the agreements of our clustering labels with Houman’s chronological classes. Our features are based on topic modeling and word embeddings. We also introduced a similarity of similarities’ features, we called homothetic clustering approach that proved effective, in case of Hafez’s small corpus of ghazals2. Although all our experiments showed different clusters when compared with Houman’s classes, we think they were valid in their own right to have provided further insights, and have proved useful as a contrasting alternative to Houman’s classes. Our homothetic clusterer and its feature design and engineering framework can be used for further semantic analysis of Hafez’s poetry and other similar literary research.
We present Computational Linguistics Applications for Multimedia Services (CLAMS), a platform that provides access to computational content analysis tools for archival multimedia material that appear in different media, such as text, audio, image, and video. The primary goal of CLAMS is: (1) to develop an interchange format between multimodal metadata generation tools to ensure interoperability between tools; (2) to provide users with a portable, user-friendly workflow engine to chain selected tools to extract meaningful analyses; and (3) to create a public software development kit (SDK) for developers that eases deployment of analysis tools within the CLAMS platform. CLAMS is designed to help archives and libraries enrich the metadata associated with their mass-digitized multimedia collections, that would otherwise be largely unsearchable.
Whitespace errors are common to digitized archives. This paper describes a lightweight unsupervised technique for recovering the original whitespace. Our approach is based on count statistics from Google n-grams, which are converted into a likelihood ratio test computed from interpolated trigram and bigram probabilities. To evaluate this approach, we annotate a small corpus of whitespace errors in a digitized corpus of newspapers from the 19th century United States. Our technique identifies and corrects most whitespace errors while introducing a minimal amount of oversegmentation: it achieves 77% recall at a false positive rate of less than 1%, and 91% recall at a false positive rate of less than 3%.
The detection of allusive text reuse is particularly challenging due to the sparse evidence on which allusive references rely — commonly based on none or very few shared words. Arguably, lexical semantics can be resorted to since uncovering semantic relations between words has the potential to increase the support underlying the allusion and alleviate the lexical sparsity. A further obstacle is the lack of evaluation benchmark corpora, largely due to the highly interpretative character of the annotation process. In the present paper, we aim to elucidate the feasibility of automated allusion detection. We approach the matter from an Information Retrieval perspective in which referencing texts act as queries and referenced texts as relevant documents to be retrieved, and estimate the difficulty of benchmark corpus compilation by a novel inter-annotator agreement study on query segmentation. Furthermore, we investigate to what extent the integration of lexical semantic information derived from distributional models and ontologies can aid retrieving cases of allusive reuse. The results show that (i) despite low agreement scores, using manual queries considerably improves retrieval performance with respect to a windowing approach, and that (ii) retrieval performance can be moderately boosted with distributional semantics.
Linguistic code-switching (C-S) is common in oral bilingual vernacular speech. When used in literature, C-S becomes an artistic choice that can mirror the patterns of bilingual interactions. But it can also potentially exceed them. What are the limits of C-S? We model features of C-S in corpora of contemporary U.S. Spanish-English literary and conversational data to analyze why some critics view the ‘Spanglish’ texts of Ilan Stavans as deviating from a C-S norm.
We describe a first attempt at using techniques from computational linguistics to analyze the undeciphered proto-Elamite script. Using hierarchical clustering, n-gram frequencies, and LDA topic models, we both replicate results obtained by manual decipherment and reveal previously-unobserved relationships between signs. This demonstrates the utility of these techniques as an aid to manual decipherment.
The increased demand for structured scientific knowledge has attracted considerable attention in extracting scientific relation from the ever growing scientific publications. Distant supervision is widely applied approach to automatically generate large amounts of labelled data with low manual annotation cost. However, distant supervision inevitably accompanies the wrong labelling problem, which will negatively affect the performance of Relation Extraction (RE). To address this issue, (Han et al., 2018) proposes a novel framework for jointly training both RE model and Knowledge Graph Completion (KGC) model to extract structured knowledge from non-scientific dataset. In this work, we firstly investigate the feasibility of this framework on scientific dataset, specifically on biomedical dataset. Secondly, to achieve better performance on the biomedical dataset, we extend the framework with other competitive KGC models. Moreover, we proposed a new end-to-end KGC model to extend the framework. Experimental results not only show the feasibility of the framework on the biomedical dataset, but also indicate the effectiveness of our extensions, because our extended model achieves significant and consistent improvements on distant supervised RE as compared with baselines.
As scientific communities grow and evolve, there is a high demand for improved methods for finding relevant papers, comparing papers on similar topics and studying trends in the research community. All these tasks involve the common problem of extracting structured information from scientific articles. In this paper, we propose a novel, scalable, semi-supervised method for extracting relevant structured information from the vast available raw scientific literature. We extract the fundamental concepts of “aim”, ”method” and “result” from scientific articles and use them to construct a knowledge graph. Our algorithm makes use of domain-based word embedding and the bootstrap framework. Our experiments show that our system achieves precision and recall comparable to the state of the art. We also show the domain independence of our algorithm by analyzing the research trends of two distinct communities - computational linguistics and computer vision.
An important task in the machine reading of biochemical events expressed in biomedical texts is correctly reading the polarity, i.e., attributing whether the biochemical event is a promotion or an inhibition. Here we present a novel dataset for studying polarity attribution accuracy. We use this dataset to train and evaluate several deep learning models for polarity identification, and compare these to a linguistically-informed model. The best performing deep learning architecture achieves 0.968 average F1 performance in a five-fold cross-validation study, a considerable improvement over the linguistically informed model average F1 of 0.862.
Datasets are integral artifacts of empirical scientific research. However, due to natural language variation, their recognition can be difficult and even when identified, can often be inconsistently referred across and within publications. We report our approach to the Coleridge Initiative’s Rich Context Competition, which tasks participants with identifying dataset surface forms (dataset mention extraction) and associating the extracted mention to its referred dataset (dataset classification). In this work, we propose various neural baselines and evaluate these model on one-plus and zero-shot classification scenarios. We further explore various joint learning approaches - exploring the synergy between the tasks - and report the issues with such techniques.
This paper explores a task for extracting a technological expression and its pros/cons from computer science papers. We report ongoing efforts on an annotated corpus of pros/cons and an analysis of the nature of the automatic extraction task. Specifically, we show how to adapt the targeted sentiment analysis task for pros/cons extraction in computer science papers and conduct an annotation study. In order to identify the challenges of the automatic extraction task, we construct a strong baseline model and conduct an error analysis. The experiments show that pros/cons can be consistently annotated by several annotators, and that the task is challenging due to domain-specific knowledge. The annotated dataset is made publicly available for research purposes.
Standard paradigms for search do not work well in the medical context. Typical information needs, such as retrieving a full list of medical interventions for a given condition, or finding the reported efficacy of a particular treatment with respect to a specific outcome of interest cannot be straightforwardly posed in typical text-box search. Instead, we propose faceted-search in which a user specifies a condition and then can browse treatments and outcomes that have been evaluated. Choosing from these, they can access randomized control trials (RCTs) describing individual studies. Realizing such a view of the medical evidence requires information extraction techniques to identify the population, interventions, and outcome measures in an RCT. Patients, health practitioners, and biomedical librarians all stand to benefit from such innovation in search of medical evidence. We present an initial prototype of such an interface applied to pre-registered clinical studies. We also discuss pilot studies into the applicability of information extraction methods to allow for similar access to all published trial results.
Toponym detection in scientific papers is an open task and a key first step in place entity enrichment of documents. We examine three common neural architectures in NLP: 1) convolutional neural network, 2) multi-layer perceptron (both applied in a sliding window context) and 3) bidirectional LSTM and apply contextual and non-contextual word embedding layers to these models. We find that deep contextual word embeddings improve the performance of the bi-LSTM with CRF neural architecture achieving the best performance when multiple layers of deep contextual embeddings are concatenated. Our best performing model achieves an average F1 of 0.910 when evaluated on overlap macro exceeding previous state-of-the-art models in the toponym detection task.
Chinese idioms (Cheng Yu) have seen five thousand years’ history and culture of China, meanwhile they contain large number of scientific achievement of ancient China. However, existing Chinese online idiom dictionaries have limited function for scientific exploration. In this paper, we first construct a Chinese idiom knowledge graph by extracting domains and dynasties and associating them with idioms, and based on the idiom knowledge graph, we propose a Science Toolkit for Ancient China (STAC) aiming to support scientific exploration. In the STAC toolkit, idiom navigator helps users explore overall scientific progress from idiom perspective with visualization tools, and idiom card and idiom QA shorten action path and avoid thinking being interrupted while users are reading and writing. The current STAC toolkit is deployed at http://18.104.22.168:7476/demo/#/stac.
Understanding procedural text requires tracking entities, actions and effects as the narrative unfolds. We focus on the challenging real-world problem of action-graph extraction from materials science papers, where language is highly specialized and data annotation is expensive and scarce. We propose a novel approach, Text2Quest, where procedural text is interpreted as instructions for an interactive game. A learning agent completes the game by executing the procedure correctly in a text-based simulated lab environment. The framework can complement existing approaches and enables richer forms of learning compared to static texts. We discuss potential limitations and advantages of the approach, and release a prototype proof-of-concept, hoping to encourage research in this direction.
Mathematical expressions (ME) are widely used in scholar documents. In this paper we analyze characteristics of textual and visual MEs characteristics for the image-to-LaTeX translation task. While there are open data-sets of LaTeX files with MEs included it is very complicated to extract these MEs from a document and to compile the list of MEs. Therefore we release a corpus of open-access scholar documents with PDF and JATS-XML parallel files. The MEs in these documents are LaTeX encoded and are document independent. The data contains more than 1.2 million distinct annotated formulae and more than 80 million raw tokens of LaTeX MEs in more than 8 thousand documents. While the variety of textual lengths and visual sizes of MEs are not well defined we found that the task of analyzing MEs in scholar documents can be reduced to the subtask of a particular text length, image width and height bounds, and display MEs can be processed as arrays of partial MEs.
This overview summarizes the main contributions of the accepted papers at the 2019 workshop on Discourse Relation Parsing and Treebanking (DISRPT 2019). Co-located with NAACL 2019 in Minneapolis, the workshop’s aim was to bring together researchers working on corpus-based and computational approaches to discourse relations. In addition to an invited talk, eighteen papers outlined below were presented, four of which were submitted as part of a shared task on elementary discourse unit segmentation and connective detection.
In this exploratory study, we attempt to automatically induce PDTB-style relations from RST trees. We work with a German corpus of news commentary articles, annotated for RST trees and explicit PDTB-style relations and we focus on inducing the implicit relations in an automated way. Preliminary results look promising as a high-precision (but low-recall) way of finding implicit relations where there is no shallow structure annotated at all, but mapping proves more difficult in cases where EDUs and relation arguments overlap, yet do not seem to signal the same relation.
Implicit discourse relation classification is one of the most challenging and important tasks in discourse parsing, due to the lack of connectives as strong linguistic cues. A principle bottleneck to further improvement is the shortage of training data (ca. 18k instances in the Penn Discourse Treebank (PDTB)). Shi et al. (2017) proposed to acquire additional data by exploiting connectives in translation: human translators mark discourse relations which are implicit in the source language explicitly in the translation. Using back-translations of such explicitated connectives improves discourse relation parsing performance. This paper addresses the open question of whether the choice of the translation language matters, and whether multiple translations into different languages can be effectively used to improve the quality of the additional data.
The first step in discourse analysis involves dividing a text into segments. We annotate the first high-quality small-scale medical corpus in English with discourse segments and analyze how well news-trained segmenters perform on this domain. While we expectedly find a drop in performance, the nature of the segmentation errors suggests some problems can be addressed earlier in the pipeline, while others would require expanding the corpus to a trainable size to learn the nuances of the medical domain.
We investigate the relationship between the notion of nuclearity as proposed in Rhetorical Structure Theory (RST) and the signalling of coherence relations. RST relations are categorized as either mononuclear (comprising a nucleus and a satellite span) or multinuclear (comprising two or more nuclei spans). We examine how mononuclear relations (e.g., Antithesis, Condition) and multinuclear relations (e.g., Contrast, List) are indicated by relational signals, more particularly by discourse markers (e.g., because, however, if, therefore). We conduct a corpus study, examining the distribution of either type of relations in the RST Discourse Treebank (Carlson et al., 2002) and the distribution of discourse markers for those relations in the RST Signalling Corpus (Das et al., 2015). Our results show that discourse markers are used more often to signal multinuclear relations than mononuclear relations. The findings also suggest a complex relationship between the relation types and syntactic categories of discourse markers (subordinating and coordinating conjunctions).
The relational status of Attribution in Rhetorical Structure Theory has been a matter of ongoing debate. Although several researchers have weighed in on the topic, and although numerous studies have relied upon attributional structures for their analyses, nothing approaching consensus has emerged. This paper identifies three basic issues that must be resolved to determine the relational status of attributions. These are identified as the Discourse Units Issue, the Nuclearity Issue, and the Relation Identification Issue. These three issues are analyzed from the perspective of classical RST. A finding of this analysis is that the nuclearity and the relational identification of attribution structures are shown to depend on the writer’s intended effect, such that attributional relations cannot be considered as a single relation, but rather as attributional instances of other RST relations.
We introduce our pilot study applying PDTB-style annotation to Twitter conversations. Lexically grounded coherence annotation for Twitter threads will enable detailed investigations of the discourse structure of conversations on social media. Here, we present our corpus of 185 threads and annotation, including an inter-annotator agreement study. We discuss our observations as to how Twitter discourses differ from written news text wrt. discourse connectives and relations. We confirm our hypothesis that discourse relations in written social media conversations are expressed differently than in (news) text. We find that in Twitter, connective arguments frequently are not full syntactic clauses, and that a few general connectives expressing EXPANSION and CONTINGENCY make up the majority of the explicit relations in our data.
This paper presents a new system for open-ended discourse relation signal annotation in the framework of Rhetorical Structure Theory (RST), implemented on top of an online tool for RST annotation. We discuss existing projects annotating textual signals of discourse relations, which have so far not allowed simultaneously structuring and annotating words signaling hierarchical discourse trees, and demonstrate the design and applications of our interface by extending existing RST annotations in the freely available GUM corpus.
Development of discourse parsers to annotate the relational discourse structure of a text is crucial for many downstream tasks. However, most of the existing work focuses on English, assuming a quite large dataset. Discourse data have been annotated for Basque, but training a system on these data is challenging since the corpus is very small. In this paper, we create the first demonstrator based on RST for Basque, and we investigate the use of data in another language to improve the performance of a Basque discourse parser. More precisely, we build a monolingual system using the small set of data available and investigate the use of multilingual word embeddings to train a system for Basque using data annotated for another language. We found that our approach to building a system limited to the small set of data available for Basque allowed us to get an improvement over previous approaches making use of many data annotated in other languages. At best, we get 34.78 in F1 for the full discourse structure. More data annotation is necessary in order to improve the results obtained with these techniques. We also describe which relations match with the gold standard, in order to understand these results.
Recent research on discourse relations has found that they are cued not only by discourse markers (DMs) but also by other textual signals and that signaling information is indicative of genres. While several corpora exist with discourse relation signaling information such as the Penn Discourse Treebank (PDTB, Prasad et al. 2008) and the Rhetorical Structure Theory Signalling Corpus (RST-SC, Das and Taboada 2018), they both annotate the Wall Street Journal (WSJ) section of the Penn Treebank (PTB, Marcus et al. 1993), which is limited to the news domain. Thus, this paper adapts the signal identification and anchoring scheme (Liu and Zeldes, 2019) to three more genres, examines the distribution of signaling devices across relations and genres, and provides a taxonomy of indicative signals found in this dataset.
Results of the first experimental evaluation of machine learning models trained on Ru-RSTreebank – first Russian corpus annotated within RST framework – are presented. Various lexical, quantitative, morphological, and semantic features were used. In rhetorical relation classification, ensemble of CatBoost model with selected features and a linear SVM model provides the best score (macro F1 = 54.67 ± 0.38). We discover that most of the important features for rhetorical relation classification are related to discourse connectives derived from the connectives lexicon for Russian and from other sources.
This paper presents RST-Tace, a tool for automatic comparison and evaluation of RST trees. RST-Tace serves as an implementation of Iruskieta’s comparison method, which allows trees to be compared and evaluated without the influence of decisions at lower levels in a tree in terms of four factors: constituent, attachment point, nuclearity as well as relation. RST-Tace can be used regardless of the language or the size of rhetorical trees. This tool aims to measure the agreement between two annotators. The result is reflected by F-measure and inter-annotator agreement. Both the comparison table and the result of the evaluation can be obtained automatically.
In 2019, we organized the first iteration of a shared task dedicated to the underlying units used in discourse parsing across formalisms: the DISRPT Shared Task on Elementary Discourse Unit Segmentation and Connective Detection. In this paper we review the data included in the task, which cover 2.6 million manually annotated tokens from 15 datasets in 10 languages, survey and compare submitted systems and report on system performance on each task for both annotated and plain-tokenized versions of the data.
We describe a series of experiments applied to data sets from different languages and genres annotated for coherence relations according to different theoretical frameworks. Specifically, we investigate the feasibility of a unified (theory-neutral) approach toward discourse segmentation; a process which divides a text into minimal discourse units that are involved in s coherence relation. We apply a RandomForest and an LSTM based approach for all data sets, and we improve over a simple baseline assuming simple sentence or clause-like segmentation. Performance however varies a lot depending on language, and more importantly genre, with f-scores ranging from 73.00 to 94.47.
Segmentation is the first step in building practical discourse parsers, and is often neglected in discourse parsing studies. The goal is to identify the minimal spans of text to be linked by discourse relations, or to isolate explicit marking of discourse relations. Existing systems on English report F1 scores as high as 95%, but they generally assume gold sentence boundaries and are restricted to English newswire texts annotated within the RST framework. This article presents a generic approach and a system, ToNy, a discourse segmenter developed for the DisRPT shared task where multiple discourse representation schemes, languages and domains are represented. In our experiments, we found that a straightforward sequence prediction architecture with pretrained contextual embeddings is sufficient to reach performance levels comparable to existing systems, when separately trained on each corpus. We report performance between 81% and 96% in F1 score. We also observed that discourse segmentation models only display a moderate generalization capability, even within the same language and discourse representation scheme.
The DISPRT 2019 workshop has organized a shared task aiming to identify cross-formalism and multilingual discourse segments. Elementary Discourse Units (EDUs) are quite similar across different theories. Segmentation is the very first stage on the way of rhetorical annotation. Still, each annotation project adopted several decisions with consequences not only on the annotation of the relational discourse structure but also at the segmentation stage. In this shared task, we have employed pre-trained word embeddings, neural networks (BiLSTM+CRF) to perform the segmentation. We report F1 results for 6 languages: Basque (0.853), English (0.919), French (0.907), German (0.913), Portuguese (0.926) and Spanish (0.868 and 0.769). Finally, we also pursued an error analysis based on clause typology for Basque and Spanish, in order to understand the performance of the segmenter.
In this paper we present GumDrop, Georgetown University’s entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.
Discourse information is crucial for a better understanding of the text structure and it is also necessary to describe which part of an opinionated text is more relevant or to decide how a text span can change the polarity (strengthen or weaken) of other span by means of coherence relations. This work presents the first results on the annotation of the Basque Opinion Corpus using Rhetorical Structure Theory (RST). Our evaluation results and analysis show us the main avenues to improve on a future annotation process. We have also extracted the subjectivity of several rhetorical relations and the results show the effect of sentiment words in relations and the influence of each relation in the semantic orientation value.
This study aims to model the discourse structure of spontaneous spoken responses within the context of an assessment of English speaking proficiency for non-native speakers. Rhetorical Structure Theory (RST) has been commonly used in the analysis of discourse organization of written texts; however, limited research has been conducted to date on RST annotation and parsing of spoken language, in particular, non-native spontaneous speech. Due to the fact that the measurement of discourse coherence is typically a key metric in human scoring rubrics for assessments of spoken language, we conducted research to obtain RST annotations on non-native spoken responses from a standardized assessment of academic English proficiency. Subsequently, automatic parsers were trained on these annotations to process non-native spontaneous speech. Finally, a set of features were extracted from automatically generated RST trees to evaluate the discourse structure of non-native spontaneous speech, which were then employed to further improve the validity of an automated speech scoring system.
We present a package of annotation resources, including annotation guideline, flowchart, and an Intelligent Tutoring System for training human annotators. These resources can be used to apply Rhetorical Structure Theory (RST) to essays written by students in K-12 schools. Furthermore, we highlight the great potential of using RST to provide automated feedback for improving writing quality across genres.
In many NLP applications like search and information extraction for named entities, it is necessary to find all the mentions of a named entity, some of which appear as pronouns (she, his, etc.) or nominals (the professor, the German chancellor, etc.). It is therefore important that coreference resolution systems are able to link these different types of mentions to the correct entity name. We evaluate state-of-the-art coreference resolution systems for the task of resolving all mentions to named entities. Our analysis reveals that standard coreference metrics do not reflect adequately the requirements in this task: they do not penalize systems for not identifying any mentions by name to an entity and they reward systems even if systems find correctly mentions to the same entity but fail to link these to a proper name (she–the student–no name). We introduce new metrics for evaluating named entity coreference that address these discrepancies and show that for the comparisons of competitive systems, standard coreference evaluations could give misleading results for this task. We are, however, able to confirm that the state-of-the art system according to traditional evaluations also performs vastly better than other systems on the named entity coreference task.
We propose an end-to-end coreference resolution system obtained by adapting neural models that have recently improved the state-of-the-art on the OntoNotes benchmark to make them applicable to other paradigms for this task. We report the performances of our system on ANCOR, a corpus of transcribed oral French, for which it constitutes a new baseline with proper evaluation.
We explore different approaches to explicit entity modelling in language models (LM). We independently replicate two existing models in a controlled setup, introduce a simplified variant of one of the models and analyze their performance in direct comparison. Our results suggest that today’s models are limited as several stochastic variables make learning difficult. We show that the most challenging point in the systems is the decision if the next token is an entity token. The low precision and recall for this variable will lead to severe cascading errors. Our own simplified approach dispenses with the need for latent variables and improves the performance in the entity yes/no decision. A standard well-tuned baseline RNN-LM with a larger number of hidden units outperforms all entity-enabled LMs in terms of perplexity.
Clustering unlinkable entity mentions across documents in multiple languages (cross-lingual NIL Clustering) is an important task as part of Entity Discovery and Linking (EDL). This task has been largely neglected by the EDL community because it is challenging to outperform simple edit distance or other heuristics based baselines. We propose a novel approach based on encoding the orthographic similarity of the mentions using a Recurrent Neural Network (RNN) architecture. Our model adapts a training procedure from the one-shot facial recognition literature in order to achieve this. We also perform several exploratory probing tasks on our name encodings in order to determine what specific types of information are likely to be encoded by our model. Experiments show our approach provides up to a 6.6% absolute CEAFm F-Score improvement over state-of-the-art methods and successfully captures phonological relations across languages.
In the present paper, we deal with incongruences in English-German multilingual coreference annotation and present automated methods to discover them. More specifically, we automatically detect full coreference chains in parallel texts and analyse discrepancies in their annotations. In doing so, we wish to find out whether the discrepancies rather derive from language typological constraints, from the translation or the actual annotation process. The results of our study contribute to the referential analysis of similarities and differences across languages and support evaluation of cross-lingual coreference annotation. They are also useful for cross-lingual coreference resolution systems and contrastive linguistic studies.
In this paper, we present a cross-lingual neural coreference resolution system for a less-resourced language such as Basque. To begin with, we build the first neural coreference resolution system for Basque, training it with the relatively small EPEC-KORREF corpus (45,000 words). Next, a cross-lingual coreference resolution system is designed. With this approach, the system learns from a bigger English corpus, using cross-lingual embeddings, to perform the coreference resolution for Basque. The cross-lingual system obtains slightly better results (40.93 F1 CoNLL) than the monolingual system (39.12 F1 CoNLL), without using any Basque language corpus to train it.
Recent psycholinguistic evidence suggests that human parsing of moved elements is ‘active’, and perhaps even ‘hyper-active’: it seems that a leftward-moved object is related to a verbal position rapidly, perhaps even before the transitivity information associated with the verb is available to the listener. This paper presents a formal, sound and complete parser for Minimalist Grammars whose search space contains branching points that we can identify as the locus of the decision to perform this kind of active gap-finding. This brings formal models of parsing into closer contact with recent psycholinguistic theorizing than was previously possible.
The word “to” that precedes verbs in English infinitives is optional in at least two environments: in what Wasow et al. (2015) previously called the “do-be” construction, and in the complement of “help”, which we explore in the present work. In the “do-be” construction, Wasow et al. found that a preceding infinitival “to” increases the use of following optional “to”, but the use of “to” in the complement of help is reduced following “to help”. We examine two hypotheses regarding why the same function word is primed by prior use in one construction and inhibited in another. We then test predictions made by the two hypotheses, finding support for one of them.
Multilingual speakers are able to switch from one language to the other (“code-switch”) between or within sentences. Because the underlying cognitive mechanisms are not well understood, in this study we use computational cognitive modeling to shed light on the process of code-switching. We employed the Bilingual Dual-path model, a Recurrent Neural Network of bilingual sentence production (Tsoukala et al., 2017), and simulated sentence production in simultaneous Spanish-English bilinguals. Our first goal was to investigate whether the model would code-switch without being exposed to code-switched training input. The model indeed produced code-switches even without any exposure to such input and the patterns of code-switches are in line with earlier linguistic work (Poplack,1980). The second goal of this study was to investigate an auxiliary phrase asymmetry that exists in Spanish-English code-switched production. Using this cognitive model, we examined a possible cause for this asymmetry. To our knowledge, this is the first computational cognitive model that aims to simulate code-switched sentence production.
Based on the Production-Distribution-Comprehension (PDC) account of language processing, we formulate two distinct hypotheses about case marking, word order choices and processing in Hindi. Our first hypothesis is that Hindi tends to optimize for processing efficiency at both lexical and syntactic levels. We quantify the role of case markers in this process. For the task of predicting the reference sentence occurring in a corpus (amidst meaning-equivalent grammatical variants) using a machine learning model, surprisal estimates from an artificial version of the language (i.e., Hindi without any case markers) result in lower prediction accuracy compared to natural Hindi. Our second hypothesis is that Hindi tends to minimize interference due to case markers while ordering preverbal constituents. We show that Hindi tends to avoid placing next to each other constituents whose heads are marked by identical case inflections. Our findings adhere to PDC assumptions and we discuss their implications for language production, learning and universals.
Sentences are represented as hierarchical syntactic structures, which have been successfully modeled in sentence processing. In contrast, despite the theoretical agreement on hierarchical syntactic structures within words, words have been argued to be computationally less complex than sentences and implemented by finite-state models as linear strings of morphemes, and even the psychological reality of morphemes has been denied. In this paper, extending the computational models employed in sentence processing to morphological processing, we performed a computational simulation experiment where, given incremental surprisal as a linking hypothesis, five computational models with different representational assumptions were evaluated against human reaction times in visual lexical decision experiments available from the English Lexicon Project (ELP), a “shared task” in the morphological processing literature. The simulation experiment demonstrated that (i) “amorphous” models without morpheme units underperformed relative to “morphous” models, (ii) a computational model with hierarchical syntactic structures, Probabilistic Context-Free Grammar (PCFG), most accurately explained human reaction times, and (iii) this performance was achieved on top of surface frequency effects. These results strongly suggest that morphological processing tracks morphemes incrementally from left to right and parses them into hierarchical syntactic structures, contrary to “amorphous” and finite-state models of morphological processing.
Processing difficulty in online language comprehension has been explained in terms of surprisal and entropy reduction. Although both hypotheses have been supported by experimental data, we do not fully understand their relative contributions on processing difficulty. To develop a better understanding, we propose a mechanistic model of perceptual decision making that interacts with a simulated task environment with temporal dynamics. The proposed model collects noisy bottom-up evidence over multiple timesteps, integrates it with its top-down expectation, and makes perceptual decisions, producing processing time data directly without relying on any linking hypothesis. Temporal dynamics in the task environment was determined by a simple finite-state grammar, which was designed to create the situations where the surprisal and entropy reduction hypotheses predict different patterns. After the model was trained to maximize rewards, the model developed an adaptive policy and both surprisal and entropy effects were observed especially in a measure reflecting earlier processing.
Cues to linguistic categories are distributed across the speech signal. Optimal categorization thus requires that listeners maintain gradient representations of incoming input in order to integrate that information with later cues. There is now evidence that listeners can and do integrate cues that occur far apart in time. Computational models of this integration have however been lacking. We take a first step at addressing this gap by mathematically formalizing four models of how listeners may maintain and use cue information during spoken language understanding and test them on two perception experiments. In one experiment, we find support for rational integration of cues at long distances. In a second, more memory and attention-taxing experiment, we find evidence in favor of a switching model that avoids maintaining detailed representations of cues in memory. These results are a first step in understanding what kinds of mechanisms listeners use for cue integration under different memory and attentional constraints.
This paper presents the first results of a multidisciplinary project, the “Evolex” project, gathering researchers in Psycholinguistics, Neuropsychology, Computer Science, Natural Language Processing and Linguistics. The Evolex project aims at proposing a new data-based inductive method for automatically characterising the relation between pairs of french words collected in psycholinguistics experiments on lexical access. This method takes advantage of several complementary computational measures of semantic similarity. We show that some measures are more correlated than others with the frequency of lexical associations, and that they also differ in the way they capture different semantic relations. This allows us to consider building a multidimensional lexical similarity to automate the classification of lexical associations.
Backward saccades during reading have been hypothesized to be involved in structural reanalysis, or to be related to the level of text difficulty. We test the hypothesis that backward saccades are involved in online syntactic analysis. If this is the case we expect that saccades will coincide, at least partially, with the edges of the relations computed by a dependency parser. In order to test this, we analyzed a large eye-tracking dataset collected while 102 participants read three short narrative texts. Our results show a relation between backward saccades and the syntactic structure of sentences.
We propose a novel framework for modeling event-related potentials (ERPs) collected during reading that couples pre-trained convolutional decoders with a language model. Using this framework, we compare the abilities of a variety of existing and novel sentence processing models to reconstruct ERPs. We find that modern contextual word embeddings underperform surprisal-based models but that, combined, the two outperform either on its own.
Stabler’s (2013) top-down parser for Minimalist grammars has been used to account for off-line processing preferences across a variety of seemingly unrelated phenomena cross-linguistically, via complexity metrics measuring “memory burden”. This paper extends the empirical coverage of the model by looking at the processing asymmetries of Italian relative clauses, as I discuss the relevance of these constructions in evaluating plausible structure-driven models of processing difficulty.
Inspired by the literature on multisensory integration, we develop a computational model to ground quantifiers in perception. The model learns to pick, out of nine quantifiers (‘few’, ‘many’, ‘all’, etc.), the one that is more likely to describe the percent of animals in a visual-auditory input containing both animals and artifacts. We show that relying on concurrent sensory inputs increases model performance on the quantification task. Moreover, we evaluate the model in a situation in which only the auditory modality is given, while the visual one is ‘hallucinanted’ either from the auditory input itself or from a linguistic caption describing the quantity of entities in the auditory input. This way, the model exploits prior associations between modalities. We show that the model profits from the prior knowledge and outperforms the auditory-only setting.
A usage-based Construction Grammar (CxG) posits that slot-constraints generalize from common exemplar constructions. But what is the best model of constraint generalization? This paper evaluates competing frequency-based and association-based models across eight languages using a metric derived from the Minimum Description Length paradigm. The experiments show that association-based models produce better generalizations across all languages by a significant margin.
How do children learn abstract concepts such as animal vs. artifact? Previous research has suggested that such concepts can partly be derived using cues from the language children hear around them. Following this suggestion, we propose a model where we represent the children’ developing lexicon as an evolving network. The nodes of this network are based on vocabulary knowledge as reported by parents, and the edges between pairs of nodes are based on the probability of their co-occurrence in a corpus of child-directed speech. We found that several abstract categories can be identified as the dense regions in such networks. In addition, our simulations suggest that these categories develop simultaneously, rather than sequentially, thanks to the children’s word learning trajectory which favors the exploration of the global conceptual space.
Sentences like “Every child climbed a tree” have at least two interpretations depending on the precedence order of the universal quantifier and the indefinite. Previous experimental work explores the role that different mechanisms such as semantic reanalysis and world knowledge may have in enabling each interpretation. This paper discusses a web-based task that uses the verb-second characteristic of German main clauses to estimate the influence of word order variation over world knowledge.
How are the meanings of linguistic expressions related to their use in concrete cognitive tasks? Visual identification tasks show human speakers can exhibit considerable variation in their understanding, representation and verification of certain quantifiers. This paper initiates an investigation into neural models of these psycho-semantic tasks. We trained two types of network – a convolutional neural network (CNN) model and a recurrent model of visual attention (RAM) – on the “most” verification task from Pietroski2009, manipulating the visual scene and novel notions of task duration. Our results qualitatively mirror certain features of human performance (such as sensitivity to the ratio of set sizes, indicating a reliance on approximate number) while differing in interesting ways (such as exhibiting a subtly different pattern for the effect of image type). We conclude by discussing the prospects for using neural models as cognitive models of this and other psychosemantic tasks.
In this study, we address the problem of part-of-speech (or syntactic category) learning during language acquisition through distributional analysis of utterances. A model based on Redington et al.’s (1998) distributional learner is used to investigate the informativeness of distributional information in Brazilian Portuguese (BP). The data provided to the learner comes from two publicly available corpora of child directed speech. We present preliminary results from two experiments. The first one investigates the effects of different assumptions about utterance boundaries when presenting the input data to the learner. The second experiment compares the learner’s performance when counting contextual words’ frequencies versus just acknowledging their co-occurrence with a given target word. In general, our results indicate that explicit boundaries are more informative, frequencies are important, and that distributional information is useful to the child as a source of categorial information. These results are in accordance with Redington et al.’s findings for English.
The fields of cognitive science and philosophy have proposed many different theories for how humans represent “concepts”. Multiple such theories are compatible with state-of-the-art NLP methods, and could in principle be operationalized using neural networks. We focus on two particularly prominent theories–Classical Theory and Prototype Theory–in the context of visually-grounded lexical representations. We compare when and how the behavior of models based on these theories differs in terms of categorization and entailment tasks. Our preliminary results suggest that Classical-based representations perform better for entailment and Prototype-based representations perform better for categorization. We discuss plans for additional experiments needed to confirm these initial observations.
A fundamental challenge when training counselors is presenting novices with the opportunity to practice counseling distressed individuals without exacerbating a situation. Rather than replacing human empathy with an automated counselor, we propose simulating an individual in crisis so that human counselors in training can practice crisis counseling in a low-risk environment. Towards this end, we collect a dataset of suicide prevention counselor role-play transcripts and make initial steps towards constructing a CRISISbot for humans to counsel while in training. In this data-constrained setting, we evaluate the potential for message retrieval to construct a coherent chat agent in light of recent advances with text embedding methods. Our results show that embeddings can considerably improve retrieval approaches to make them competitive with generative models. By coherently retrieving messages, we can help counselors practice chatting in a low-risk environment.
While conversation in therapy sessions can vary widely in both topic and style, an understanding of the underlying techniques used by therapists can provide valuable insights into how therapists best help clients of different types. Dialogue act classification aims to identify the conversational “action” each speaker takes at each utterance, such as sympathizing, problem-solving or assumption checking. We propose to apply dialogue act classification to therapy transcripts, using a therapy-specific labeling scheme, in order to gain a high-level understanding of the flow of conversation in therapy sessions. We present a novel annotation scheme that spans multiple psychotherapeutic approaches, apply it to a large and diverse corpus of psychotherapy transcripts, and present and discuss classification results obtained using both SVM and neural network-based models. The results indicate that identifying the structure and flow of therapeutic actions is an obtainable goal, opening up the opportunity in the future to provide therapeutic recommendations tailored to specific client situations.
The shared task for the 2019 Workshop on Computational Linguistics and Clinical Psychology (CLPsych’19) introduced an assessment of suicide risk based on social media postings, using data from Reddit to identify users at no, low, moderate, or severe risk. Two variations of the task focused on users whose posts to the r/SuicideWatch subreddit indicated they might be at risk; a third task looked at screening users based only on their more everyday (non-SuicideWatch) posts. We received submissions from 15 different teams, and the results provide progress and insight into the value of language signal in helping to predict risk level.
This paper summarizes our participation to the CLPsych 2019 shared task, under the name CLaC. The goal of the shared task was to detect and assess suicide risk based on a collection of online posts. For our participation, we used an ensemble method which utilizes 8 neural sub-models to extract neural features and predict class probabilities, which are then used by an SVM classifier. Our team ranked first in 2 out of the 3 tasks (tasks A and C).
Mental health predictive systems typically model language as if from a single context (e.g. Twitter posts, status updates, or forum posts) and often limited to a single level of analysis (e.g. either the message-level or user-level). Here, we bring these pieces together to explore the use of open-vocabulary (BERT embeddings, topics) and theoretical features (emotional expression lexica, personality) for the task of suicide risk assessment on support forums (the CLPsych-2019 Shared Task). We used dual context based approaches (modeling content from suicide forums separate from other content), built over both traditional ML models as well as a novel dual RNN architecture with user-factor adaptation. We find that while affect from the suicide context distinguishes with no-risk from those with “any-risk”, personality factors from the non-suicide contexts provide distinction of the levels of risk: low, medium, and high risk. Within the shared task, our dual-context approach (listed as SBU-HLAB in the official results) achieved state-of-the-art performance predicting suicide risk using a combination of suicide-context and non-suicide posts (Task B), achieving an F1 score of 0.50 over hidden test set labels.
Spoken language ability is highly heterogeneous in Autism Spectrum Disorder (ASD), which complicates efforts to identify linguistic markers for use in diagnostic classification, clinical characterization, and for research and clinical outcome measurement. Machine learning techniques that harness the power of multivariate statistics and non-linear data analysis hold promise for modeling this heterogeneity, but many models require enormous datasets, which are unavailable for most psychiatric conditions (including ASD). In lieu of such datasets, good models can still be built by leveraging domain knowledge. In this study, we compare two machine learning approaches: the first approach incorporates prior knowledge about language variation across middle childhood, adolescence, and adulthood to classify 6-minute naturalistic conversation samples from 140 age- and IQ-matched participants (81 with ASD), while the other approach treats all ages the same. We found that individual age-informed models were significantly more accurate than a single model tasked with building a common algorithm across age groups. Furthermore, predictive linguistic features differed significantly by age group, confirming the importance of considering age-related changes in language use when classifying ASD. Our results suggest that limitations imposed by heterogeneity inherent to ASD and from developmental change with age can be (at least partially) overcome using domain knowledge, such as understanding spoken language development from childhood through adulthood.
Increased access to large datasets has driven progress in NLP. However, most computational studies of clinically-validated, patient-generated speech and language involve very few datapoints, as such data are difficult (and expensive) to collect. In this position paper, we argue that we must find ways to promote data sharing across research groups, in order to build datasets of a more appropriate size for NLP and machine learning analysis. We review the benefits and challenges of sharing clinical language data, and suggest several concrete actions by both clinical and NLP researchers to encourage multi-site and multi-disciplinary data sharing. We also propose the creation of a collaborative data sharing platform, to allow NLP researchers to take a more active responsibility for data transcription, annotation, and curation.
Depression is characterized by a self-focused negative attentional bias, which is often reflected in everyday language use. In a prospective writing study, we explored whether the association between depressive symptoms and negative, self-focused language varies across social contexts. College students (N = 243) wrote about a recent interaction with a person they care deeply about. Depression symptoms positively correlated with negative emotion words and first-person singular pronouns (or negative self-focus) when writing about a recent interaction with romantic partners or, to a lesser extent, friends, but not family members. The pattern of results was more pronounced when participants perceived greater self-other overlap (i.e., interpersonal closeness) with their romantic partner. Findings regarding how the linguistic profile of depression differs by type of relationship may inform more effective methods of clinical diagnosis and treatment.
We explore linguistic indicators of schizophrenia in Reddit discussion forums. Schizophrenia (SZ) is a chronic mental disorder that affects a person’s thoughts and behaviors. Identifying and detecting signs of SZ is difficult given that SZ is relatively uncommon, affecting approximately 1% of the US population, and people suffering with SZ often believe that they do not have the disorder. Linguistic abnormalities are a hallmark of SZ and many of the illness’s symptoms are manifested through language. In this paper we leverage the vast amount of data available from social media and use statistical and machine learning approaches to study linguistic characteristics of SZ. We collected and analyzed a large corpus of Reddit posts from users claiming to have received a formal diagnosis of SZ and identified several linguistic features that differentiated these users from a control (CTL) group. We compared these results to other findings on social media linguistic analysis and SZ. We also developed a machine learning classifier to automatically identify self-identified users with SZ on Reddit.
Natural language processing tools are used to automatically detect disturbances in transcribed speech of schizophrenia inpatients who speak Hebrew. We measure topic mutation over time and show that controls maintain more cohesive speech than inpatients. We also examine differences in how inpatients and controls use adjectives and adverbs to describe content words and show that the ones used by controls are more common than the those of inpatients. We provide experimental results and show their potential for automatically detecting schizophrenia in patients by means only of their speech patterns.
Computational linguistics holds promise for improving scientific integrity in clinical psychology, and for reducing longstanding inequities in healthcare access and quality. This paper describes how computational linguistics approaches could address the “reproducibility crisis” facing social science, particularly with regards to reliable diagnosis of neurodevelopmental and psychiatric conditions including autism spectrum disorder (ASD). It is argued that these improvements in scientific integrity are poised to naturally reduce persistent healthcare inequities in neglected subpopulations, such as verbally fluent girls and women with ASD, but that concerted attention to this issue is necessary to avoid reproducing biases built into training data. Finally, it is suggested that computational linguistics is just one component of an emergent digital phenotyping toolkit that could ultimately be used for clinical decision support, to improve clinical care via precision medicine (i.e., personalized intervention planning), granular treatment response monitoring (including remotely), and for gene-brain-behavior studies aiming to pinpoint the underlying biological etiology of otherwise behaviorally-defined conditions like ASD.
The Semantic Verbal Fluency (SVF) task is a classical neuropsychological assessment where persons are asked to produce words belonging to a semantic category (e.g., animals) in a given time. This paper introduces a novel method of temporal analysis for SVF tasks utilizing time intervals and applies it to a corpus of elderly Swedish subjects (mild cognitive impairment, subjective cognitive impairment and healthy controls). A general decline in word count and lexical frequency over the course of the task is revealed, as well as an increase in word transition times. Persons with subjective cognitive impairment had a higher word count during the last intervals, but produced words of the same lexical frequencies. Persons with MCI had a steeper decline in both word count and lexical frequencies during the third interval. Additional correlations with neuropsychological scores suggest these findings are linked to a person’s overall vocabulary size and processing speed, respectively. Classification results improved when adding the novel features (AUC=0.72), supporting their diagnostic value.
The ability to track mental health conditions via social media opened the doors for large-scale, automated, mental health surveillance. However, inferring accurate population-level trends requires representative samples of the underlying population, which can be challenging given the biases inherent in social media data. While previous work has adjusted samples based on demographic estimates, the populations were selected based on specific outcomes, e.g. specific mental health conditions. We depart from these methods, by conducting analyses over demographically representative digital cohorts of social media users. To validated this approach, we constructed a cohort of US based Twitter users to measure the prevalence of depression and PTSD, and investigate how these illnesses manifest across demographic subpopulations. The analysis demonstrates that cohort-based studies can help control for sampling biases, contextualize outcomes, and provide deeper insights into the data.
Implicit motives allow for the characterization of behavior, subsequent success and long-term development. While this has been operationalized in the operant motive test, research on motives has declined mainly due to labor-intensive and costly human annotation. In this study, we analyze over 200,000 labeled data items from 40,000 participants and utilize them for engineering features for training a logistic model tree machine learning model. It captures manually assigned motives well with an F-score of 80%, coming close to the pairwise annotator intraclass correlation coefficient of r = .85. In addition, we found a significant correlation of r = .2 between subsequent academic success and data automatically labeled with our model in an extrinsic evaluation.
Incoherent discourse in schizophrenia has long been recognized as a dominant symptom of the mental disorder (Bleuler, 1911/1950). Recent studies have used modern sentence and word embeddings to compute coherence metrics for spontaneous speech in schizophrenia. While clinical ratings always have a subjective element, computational linguistic methodology allows quantification of speech abnormalities. Clinical and empirical knowledge from psychiatry provide the theoretical and conceptual basis for modelling. Our study is an interdisciplinary attempt at improving coherence models in schizophrenia. Speech samples were obtained from healthy controls and patients with a diagnosis of schizophrenia or schizoaffective disorder and different severity of positive formal thought disorder. Interviews were transcribed and coherence metrics derived from different embeddings. One model found higher coherence metrics for controls than patients. All other models remained non-significant. More detailed analysis of the data motivates different approaches to improving coherence models in schizophrenia, e.g. by assessing referential abnormalities.
Verbal memory is affected by numerous clinical conditions and most neuropsychological and clinical examinations evaluate it. However, a bottleneck exists in such endeavors because traditional methods require expert human review, and usually only a couple of test versions exist, thus limiting the frequency of administration and clinical applications. The present study overcomes this bottleneck by automating the administration, transcription, analysis and scoring of story recall. A large group of healthy participants (n = 120) and patients with mental illness (n = 105) interacted with a mobile application that administered a wide range of assessments, including verbal memory. The resulting speech generated by participants when retelling stories from the memory task was transcribed using automatic speech recognition tools, which was compared with human transcriptions (overall word error rate = 21%). An assortment of surface-level and semantic language-based features were extracted from the verbal recalls. A final set of three features were used to both predict expert human ratings with a ridge regression model (r = 0.88) and to differentiate patients from healthy individuals with an ensemble of logistic regression classifiers (accuracy = 76%). This is the first ‘outside of the laboratory’ study to showcase the viability of the complete pipeline of automated assessment of verbal memory in naturalistic settings.
In this paper we describe the UniOvi-WESO classification systems proposed for the 2019 Computational Linguistics and Clinical Psychology (CLPsych) Shared Task. We explore the use of two systems trained with ReachOut data from the 2016 CLPsych task, and compare them to a baseline system trained with the data provided for this task. All the classifiers were trained with features extracted just from the text of each post, without using any other metadata. We found out that the baseline system performs slightly better than the pretrained systems, mainly due to the differences in labeling between the two tasks. However, they still work reasonably well and can detect if a user is at risk of suicide or not.
This paper describes our system submission for the CLPsych 2019 shared task B on suicide risk assessment. We approached the problem with three separate models: a behaviour model; a language model and a hybrid model. For the behavioral model approach, we model each user’s behaviour and thoughts with four groups of features: posting behaviour, sentiment, motivation, and content of the user’s posting. We use these features as an input in a support vector machine (SVM). For the language model approach, we trained a language model for each risk level using all the posts from the users as the training corpora. Then, we computed the perplexity of each user’s posts to determine how likely his/her posts were to belong to each risk level. Finally, we built a hybrid model that combines both the language model and the behavioral model, which demonstrates the best performance in detecting the suicide risk level.
This paper describes IDLab’s text classification systems submitted to Task A as part of the CLPsych 2019 shared task. The aim of this shared task was to develop automated systems that predict the degree of suicide risk of people based on their posts on Reddit. Bag-of-words features, emotion features and post level predictions are used to derive user-level predictions. Linear models and ensembles of these models are used to predict final scores. We find that predicting fine-grained risk levels is much more difficult than flagging potentially at-risk users. Furthermore, we do not find clear added value from building richer ensembles compared to simple baselines, given the available training data and the nature of the prediction task.
We aimed to predict an individual suicide risk level from longitudinal posts on Reddit discussion forums. Through participating in a shared task competition hosted by CLPsych2019, we received two annotated datasets: a training dataset with 496 users (31,553 posts) and a test dataset with 125 users (9610 posts). We submitted results from our three best-performing machine-learning models: SVM, Naïve Bayes, and an ensemble model. Each model provided a user’s suicide risk level in four categories, i.e., no risk, low risk, moderate risk, and severe risk. Among the three models, the ensemble model had the best macro-averaged F1 score 0.379 when tested on the holdout test dataset. The NB model had the best performance in two additional binary-classification tasks, i.e., no risk vs. flagged risk (any risk level other than no risk) with F1 score 0.836 and no or low risk vs. urgent risk (moderate or severe risk) with F1 score 0.736. We conclude that the NB model may serve as a tool for identifying users with flagged or urgent suicide risk based on longitudinal posts on Reddit discussion forums.
This paper describes the participation of the USI-UPF team at the shared task of the 2019 Computational Linguistics and Clinical Psychology Workshop (CLPsych2019). The goal is to assess the degree of suicide risk of social media users given a labelled dataset with their posts. An appropriate suicide risk assessment, with the usage of automated methods, can assist experts on the detection of people at risk and eventually contribute to prevent suicide. We propose a set of machine learning models with features based on lexicons, word embeddings, word level n-grams, and statistics extracted from users’ posts. The results show that the most effective models for the tasks are obtained integrating lexicon-based features, a selected set of n-grams, and statistical measures.
Social media posts may yield clues to the subject’s (usually, the writer’s) suicide risk and intent, which can be used for timely intervention. This research, motivated by the CLPsych 2019 shared task, developed neural network-based methods for analyzing posts in one or more Reddit forums to assess the subject’s suicide risk. One of the technical challenges this task poses is the large amount of text from multiple posts of a single user. Our neural network models use the advanced multi-headed Attention-based autoencoder architecture, called Bidirectional Encoder Representations from Transformers (BERT). Our system achieved the 2nd best performance of 0.477 macro averaged F measure on Task A of the challenge. Among the three different alternatives we developed for the challenge, the single BERT model that processed all of a user’s posts performed the best on all three Tasks.
This work aims to infer mental health status from public text for early detection of suicide risk. It contributes to Shared Task A in the 2019 CLPsych workshop by predicting users’ suicide risk given posts in the Reddit subforum r/SuicideWatch. We use a convolutional neural network to incorporate LIWC information at the Reddit post level about topics discussed, first-person focus, emotional experience, grammatical choices, and thematic style. In sorting users into one of four risk categories, our best system’s macro-averaged F1 score was 0.50 on the withheld test set. The work demonstrates the predictive power of the Linguistic Inquiry and Word Count dictionary, in conjunction with a convolutional network and holistic consideration of each post and user.
In this summary, we discuss our approach to the CLPsych Shared Task and its initial results. For our predictions in each task, we used a recursive partitioning algorithm (decision trees) to select from our set of features, which were primarily dictionary scores and counts of individual words. We focused primarily on Task A, which aimed to predict suicide risk, as rated by a team of expert clinicians (Shing et al., 2018), based on language used in SuicideWatch posts on Reddit. Category-level findings highlight the potential importance of social and moral language categories. Word-level correlates of risk levels underline the value of fine-grained data-driven approaches, revealing both theory-consistent and potentially novel correlates of suicide risk that may motivate future research.
We propose a formal model for translating unranked syntactic trees, such as dependency trees, into semantic graphs. These tree-to-graph transducers can serve as a formal basis of transition systems for semantic parsing which recently have been shown to perform very well, yet hitherto lack formalization. Our model features “extended” rules and an arc-factored normal form, comes with an efficient translation algorithm, and can be equipped with weights in a straightforward manner.
In finite-state language processing pipelines, a lexicon is often a key component. It needs to be comprehensive to ensure accuracy, reducing out-of-vocabulary misses. However, in memory-constrained environments (e.g., mobile phones), the size of the component automata must be kept small. Indeed, a delicate balance between comprehensiveness, speed, and memory must be struck to conform to device requirements while providing a good user experience.In this paper, we describe a compression scheme for lexicons when represented as finite-state transducers. We efficiently encode the graph of the transducer while storing transition labels separately. The graph encoding scheme is based on the LOUDS (Level Order Unary Degree Sequence) tree representation, which has constant time tree traversal for queries while being information-theoretically optimal in space. We find that our encoding is near the theoretical lower bound for such graphs and substantially outperforms more traditional representations in space while remaining competitive in latency benchmarks.
Tests added to Kleene algebra (by Kozen and others) are considered within Monadic Second Order logic over strings, where they are likened to statives in natural language. Reducts are formed over tests and non-tests alike, specifying what is observable. Notions of temporal granularity are based on observable change, under the assumption that a finite set bounds what is observable (with the possibility of stretching such bounds by moving to a larger finite set). String projections at different granularities are conjoined by superpositions that provide another variant of concatenation for Booleans.
The research on machine learning of morphology often involves formulating morphological descriptions directly on surface forms of words. As the established two-level morphology paradigm requires the knowledge of the underlying structure, it is not widely used in such settings. In this paper, we propose a formalism describing structural relationships between words based on theories of morphology that reject the notions of internal word structure and morpheme. The formalism covers a wide variety of morphological phenomena (including non-concatenative ones like stem vowel alternation) without the need of workarounds and extensions. Furthermore, we show that morphological rules formulated in such way can be easily translated to FSTs, which enables us to derive performant approaches to morphological analysis, generation and automatic rule discovery.
We develop a general framework for weighted parsing which is built on top of grammar-based language models and employs flexible weight algebras. It generalizes previous work in that area (semiring parsing, weighted deductive parsing) and also covers applications outside the classical scope of parsing, e.g., algebraic dynamic programming. We show an algorithm which terminates and is correct for a large class of weighted grammar-based language models.
We show that regular transductions for which the input part is generated by some multiple context-free grammar can be simulated by synchronous multiple context-free grammars. We prove that synchronous multiple context-free grammars are strictly more powerful than this combination of regular transductions and multiple context-free grammars.
We present a broad coverage model of Turkish morphology and an open-source morphological analyzer that implements it. The model captures intricacies of Turkish morphology-syntax interface, thus could be used as a baseline that guides language model development. It introduces a novel fine part-of-speech tagset, a fine-grained affix inventory and represents morphotactics without zero-derivations. The morphological analyzer is freely available. It consists of modular reusable components of human-annotated gold standard lexicons, implements Turkish morphotactics as finite-state transducers using OpenFst and morphophonemic processes as Thrax grammars.
This paper describes a new and larger coverage Finite-State Morphological Analyser (FSM) and Generator for the Dravidian language Tamil. The FSM has been developed in the context of computational grammar engineering, adhering to the standards of the ParGram effort. Tamil is a morphologically rich language and the interaction between linguistic analysis and formal implementation is complex, resulting in a challenging task. In order to allow the development of the FSM to focus more on the linguistic analysis and less on the formal details, we have developed a system of meta-morph(ology) rules along with a script which translates these rules into FSM processable representations. The introduction of meta-morph rules makes it possible for computationally naive linguists to interact with the system and to expand it in future work. We found that the meta-morph rules help to express linguistic generalisations and reduce the manual effort of writing lexical classes for morphological analysis. Our Tamil FSM currently handles mainly the inflectional morphology of 3,300 verb roots and their 260 forms. Further, it also has a lexicon of approximately 100,000 nouns along with a guesser to handle out-of-vocabulary items. Although the Tamil FSM was primarily developed to be part of a computational grammar, it can also be used as a web or stand-alone application for other NLP tasks, as per general ParGram practice.
Weighted finite automata (WFA) are often used to represent probabilistic models, such as n-gram language models, since they are efficient for recognition tasks in time and space. The probabilistic source to be represented as a WFA, however, may come in many forms. Given a generic probabilistic model over sequences, we propose an algorithm to approximate it as a weighted finite automaton such that the Kullback-Leibler divergence between the source model and the WFA target model is minimized. The proposed algorithm involves a counting step and a difference of convex optimization, both of which can be performed efficiently. We demonstrate the usefulness of our approach on some tasks including distilling n-gram models from neural models.
Modeling sequence data using probabilistic finite state machines (PFSMs) is a technique that analyzes the underlying dynamics in sequences of symbols. Hidden semi-Markov models (HSMMs) and hierarchical hidden Markov models (HHMMs) are PFSMs that have been successfully applied to a wide variety of applications by extending HMMs to make the extracted patterns easier to interpret. However, these models are independently developed with their own training algorithm, so that we cannot combine multiple kinds of structures to build a PFSM for a specific application. In this paper, we prove that silent hidden Markov models (silent HMMs) are flexible models that have more expressive power than HSMMs and HHMMs. Silent HMMs are HMMs that contain silent states, which do not emit any observations. We show that we can obtain silent HMM equivalent to given HSMMs and HHMMs. We believe that these results form a firm foundation to use silent HMMs as a unified representation for PFSM modeling.
The use of the Latin script for text entry of South Asian languages is common, even though there is no standard orthography for these languages in the script. We explore several compact finite-state architectures that permit variable spellings of words during mobile text entry. We find that approaches making use of transliteration transducers provide large accuracy improvements over baselines, but that simpler approaches involving a compact representation of many attested alternatives yields much of the accuracy gain. This is particularly important when operating under constraints on model size (e.g., on inexpensive mobile devices with limited storage and memory for keyboard models), and on speed of inference, since people typing on mobile keyboards expect no perceptual delay in keyboard responsiveness.
Transition-based parsing of natural language uses transition systems to build directed annotation graphs (digraphs) for sentences. In this paper, we define, for an arbitrary ordered digraph, a unique decomposition and a corresponding linear encoding that are associated bijectively with each other via a new transition system. These results give us an efficient and succinct representation for digraphs and sets of digraphs. Based on the system and our analysis of its syntactic properties, we give structural bounds under which the set of encoded digraphs is restricted and becomes a context-free or a regular string language. The context-free restriction is essentially a superset of the encodings used previously to characterize properties of noncrossing digraphs and to solve maximal subgraphs problems. The regular restriction with a tight bound is shown to capture the Universal Dependencies v2.4 treebanks in linguistics.
Claims database and electronic health records database do not usually capture kinship or family relationship information, which is imperative for genetic research. We identify online obituaries as a new data source and propose a special named entity recognition and relation extraction solution to extract names and kinships from online obituaries. Built on 1,809 annotated obituaries and a novel tagging scheme, our joint neural model achieved macro-averaged precision, recall and F measure of 72.69%, 78.54% and 74.93%, and micro-averaged precision, recall and F measure of 95.74%, 98.25% and 96.98% using 57 kinships with 10 or more examples in a 10-fold cross-validation experiment. The model performance improved dramatically when trained with 34 kinships with 50 or more examples. Leveraging additional information such as age, death date, birth date and residence mentioned by obituaries, we foresee a promising future of supplementing EHR databases with comprehensive and accurate kinship information for genetic research.
In the medical domain, user-generated social media text is increasingly used as a valuable complementary knowledge source to scientific medical literature. The extraction of this knowledge is complicated by colloquial language use and misspellings. Yet, lexical normalization of such data has not been addressed properly. This paper presents an unsupervised, data-driven spelling correction module for medical social media. Our method outperforms state-of-the-art spelling correction and can detect mistakes with an F0.5 of 0.888. Additionally, we present a novel corpus for spelling mistake detection and correction on a medical patient forum.
The number of users of social media continues to grow, with nearly half of adults worldwide and two-thirds of all American adults using social networking. Advances in automated data processing, machine learning and NLP present the possibility of utilizing this massive data source for biomedical and public health applications, if researchers address the methodological challenges unique to this media. We present the Social Media Mining for Health Shared Tasks collocated with the ACL at Florence in 2019, which address these challenges for health monitoring and surveillance, utilizing state of the art techniques for processing noisy, real-world, and substantially creative language expressions from social media users. For the fourth execution of this challenge, we proposed four different tasks. Task 1 asked participants to distinguish tweets reporting an adverse drug reaction (ADR) from those that do not. Task 2, a follow-up to Task 1, asked participants to identify the span of text in tweets reporting ADRs. Task 3 is an end-to-end task where the goal was to first detect tweets mentioning an ADR and then map the extracted colloquial mentions of ADRs in the tweets to their corresponding standard concept IDs in the MedDRA vocabulary. Finally, Task 4 asked participants to classify whether a tweet contains a personal mention of one’s health, a more general discussion of the health issue, or is an unrelated mention. A total of 34 teams from around the world registered and 19 teams from 12 countries submitted a system run. We summarize here the corpora for this challenge which are freely available at https://competitions.codalab.org/competitions/22521, and present an overview of the methods and the results of the competing systems.
The medical concept normalisation task aims to map textual descriptions to standard terminologies such as SNOMED-CT or MedDRA. Existing publicly available datasets annotated using different terminologies cannot be simply merged and utilised, and therefore become less valuable when developing machine learning-based concept normalisation systems. To address that, we designed a data harmonisation pipeline and engineered a corpus of 27,979 textual descriptions simultaneously mapped to both MedDRA and SNOMED-CT, sourced from five publicly available datasets across biomedical and social media domains. The pipeline can be used in the future to integrate new datasets into the corpus and also could be applied in relevant data curation tasks. We also described a method to merge different terminologies into a single concept graph preserving their relations and demonstrated that representation learning approach based on random walks on a graph can efficiently encode both hierarchical and equivalent relations and capture semantic similarities not only between concepts inside a given terminology but also between concepts from different terminologies. We believe that making a corpus and embeddings for cross-terminology medical concept normalisation available to the research community would contribute to a better understanding of the task.
Depression and anxiety are the two most prevalent mental health disorders worldwide, impacting the lives of millions of people each year. In this work, we develop and evaluate a multilabel, multidimensional deep neural network designed to predict PHQ-4 scores based on individuals written text. Our system outperforms random baseline metrics and provides a novel approach to how we can predict psychometric scores from written text. Additionally, we explore how this architecture can be applied to analyse social media data.
This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fourth Social Media Mining for Health Applications (SMM4H) shared task in 2019. The two subtasks are automatic classification and extraction of adverse effect mentions in tweets. The systems for the two subtasks are based on bidirectional encoder representations from transformers (BERT), and achieves promising results. Among the systems we developed for subtask1, the best F1-score was 0.6457, for subtask2, the best relaxed F1-score and the best strict F1-score were 0.614 and 0.407 respectively. Our system ranks first among all systems on subtask1.
This paper describes a system developed for the Social Media Mining for Health (SMM4H) 2019 shared tasks. Specifically, we participated in three tasks. The goals of the first two tasks are to classify whether a tweet contains mentions of adverse drug reactions (ADR) and extract these mentions, respectively. The objective of the third task is to build an end-to-end solution: first, detect ADR mentions and then map these entities to concepts in a controlled vocabulary. We investigate the use of a language representation model BERT trained to obtain semantic representations of social media texts. Our experiments on a dataset of user reviews showed that BERT is superior to state-of-the-art models based on recurrent neural networks. The BERT-based system for Task 1 obtained an F1 of 57.38%, with improvements up to +7.19% F1 over a score averaged across all 43 submissions. The ensemble of neural networks with a voting scheme for named entity recognition ranked first among 9 teams at the SMM4H 2019 Task 2 and obtained a relaxed F1 of 65.8%. The end-to-end model based on BERT for ADR normalization ranked first at the SMM4H 2019 Task 3 and obtained a relaxed F1 of 43.2%.
We describe our submissions to the 4th edition of the Social Media Mining for Health Applications (SMM4H) shared task. Our team (UZH) participated in two sub-tasks: Automatic classifications of adverse effects mentions in tweets (Task 1) and Generalizable identification of personal health experience mentions (Task 4). For our submissions, we exploited ensembles based on a pre-trained language representation with a neural transformer architecture (BERT) (Tasks 1 and 4) and a CNN-BiLSTM(-CRF) network within a multi-task learning scenario (Task 1). These systems are placed on top of a carefully crafted pipeline of domain-specific preprocessing steps.
Identifying mentions of medical concepts in social media is challenging because of high variability in free text. In this paper, we propose a novel neural network architecture, the Collocated LSTM with Attentive Pooling and Aggregated representation (CLAPA), that integrates a bidirectional LSTM model with attention and pooling strategy and utilizes the collocation information from training data to improve the representation of medical concepts. The collocation and aggregation layers improve the model performance on the task of identifying mentions of adverse drug events (ADE) in tweets. Using the dataset made available as part of the workshop shared task, we show that careful selection of neighborhood contexts can help uncover useful local information and improve the overall medical concept representation.
We study how language on social media is linked to mortal diseases such as atherosclerotic heart disease (AHD), diabetes and various types of cancer. Our proposed model leverages state-of-the-art sentence embeddings, followed by a regression model and clustering, without the need of additional labelled data. It allows to predict community-level medical outcomes from language, and thereby potentially translate these to the individual level. The method is applicable to a wide range of target variables and allows us to discover known and potentially novel correlations of medical outcomes with life-style aspects and other socioeconomic risk factors.
The increase in the prevalence of mental health problems has coincided with a growing popularity of health related social networking sites. Regardless of their therapeutic potential, on-line support groups (OSGs) can also have negative effects on patients. In this work we propose a novel methodology to automatically verify the presence of therapeutic factors in social networking websites by using Natural Language Processing (NLP) techniques. The methodology is evaluated on on-line asynchronous multi-party conversations collected from an OSG and Twitter. The results of the analysis indicate that therapeutic factors occur more frequently in OSG conversations than in Twitter conversations. Moreover, the analysis of OSG conversations reveals that the users of that platform are supportive, and interactions are likely to lead to the improvement of their emotional state. We believe that our method provides a stepping stone towards automatic analysis of emotional states of users of online platforms. Possible applications of the method include provision of guidelines that highlight potential implications of using such platforms on users’ mental health, and/or support in the analysis of their impact on specific individuals.
Transfer learning is promising for many NLP applications, especially in tasks with limited labeled data. This paper describes the methods developed by team TMRLeiden for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task. Our methods use state-of-the-art transfer learning methods to classify, extract and normalise adverse drug effects (ADRs) and to classify personal health mentions from health-related tweets. The code and fine-tuned models are publicly available.
This paper describes a system for automatically classifying adverse effects mentions in tweets developed for the task 1 at Social Media Mining for Health Applications (SMM4H) Shared Task 2019. We have developed a system based on LSTM neural networks inspired by the excellent results obtained by deep learning classifiers in the last edition of this task. The network is trained along with Twitter GloVe pre-trained word embeddings.
This paper describes our system for the first and second shared tasks of the fourth Social Media Mining for Health Applications (SMM4H) workshop. We enhance tweet representation with a language model and distinguish the importance of different words with Multi-Head Self-Attention. In addition, transfer learning is exploited to make up for the data shortage. Our system achieved competitive results on both tasks with an F1-score of 0.5718 for task 1 and 0.653 (overlap) / 0.357 (strict) for task 2.
Social Media Mining for Health Applications (SMM4H) Adverse Effect Mentions Shared Task challenges participants to accurately identify spans of text within a tweet that correspond to Adverse Effects (AEs) resulting from medication usage (Weissenbacher et al., 2019). This task features a training data set of 2,367 tweets, in addition to a 1,000 tweet evaluation data set. The solution presented here features a bidirectional Long Short-term Memory Network (bi-LSTM) for the generation of character-level embeddings. It uses a second bi-LSTM trained on both character and token level embeddings to feed a Conditional Random Field (CRF) which provides the final classification. This paper further discusses the deep learning algorithms used in our solution.
Over time the use of social networks is becoming very popular platforms for sharing health related information. Social Media Mining for Health Applications (SMM4H) provides tasks such as those described in this document to help manage information in the health domain. This document shows the first participation of the SINAI group. We study approaches based on machine learning and deep learning to extract adverse drug reaction mentions from Twitter. The results obtained in the tasks are encouraging, we are close to the average of all participants and even above in some cases.
We participated in Task 1 of the Social Media Mining for Health Applications (SMM4H) 2019 Shared Tasks on detecting mentions of adverse drug events (ADEs) in tweets. Our approach relied on a text processing pipeline for tweets, and training traditional machine learning and deep learning models. Our submitted runs performed above average for the task.
This paper describes the system developed by team ASU-NLP for the Social Media Mining for Health Applications(SMM4H) shared task 4. We extract feature embeddings from the BioBERT (Lee et al., 2019) model which has been fine-tuned on the training dataset and use that as inputs to a dense fully connected neural network. We achieve above average scores among the participant systems with the overall F1-score, accuracy, precision, recall as 0.8036, 0.8456, 0.9783, 0.6818 respectively.
This paper describes the system that team MYTOMORROWS-TU DELFT developed for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task 3, for the end-to-end normalization of ADR tweet mentions to their corresponding MEDDRA codes. For the first two steps, we reuse a state-of-the art approach, focusing our contribution on the final entity-linking step. For that we propose a simple Few-Shot learning approach, based on pre-trained word embeddings and data from the UMLS, combined with the provided training data. Our system (relaxed F1: 0.337-0.345) outperforms the average (relaxed F1 0.2972) of the participants in this task, demonstrating the potential feasibility of few-shot learning in the context of medical text normalization.
In this study, we describe our methods to automatically classify Twitter posts conveying events of adverse drug reaction (ADR). Based on our previous experience in tackling the ADR classification task, we empirically applied the vote-based under-sampling ensemble approach along with linear support vector machine (SVM) to develop our classifiers as part of our participation in ACL 2019 Social Media Mining for Health Applications (SMM4H) shared task 1. The best-performed model on the test sets were trained on a merged corpus consisting of the datasets released by SMM4H 2017 and 2019. By using VUE, the corpus was randomly under-sampled with 2:1 ratio between the negative and positive classes to create an ensemble using the linear kernel trained with features including bag-of-word, domain knowledge, negation and word embedding. The best performing model achieved an F-measure of 0.551 which is about 5% higher than the average F-scores of 16 teams.
This paper describes the models used by our team in SMM4H 2019 shared task. We submitted results for subtasks 1 and 2. For task 1 which aims to detect tweets with Adverse Drug Reaction (ADR) mentions we used ELMo embeddings which is a deep contextualized word representation able to capture both syntactic and semantic characteristics. For task 2, which focuses on extraction of ADR mentions, first the same architecture as task 1 was used to identify whether or not a tweet contains ADR. Then, for tweets positively classified as mentioning ADR, the relevant text span was identified by similarity matching with 3 different lexicon sets.
CLaC labs participated in Task 1 and 4 of SMM4H 2019. We pursed two main objectives in our submission. First we tried to use some textual features in a deep net framework, and second, the potential use of more than one word embedding was tested. The results seem positively affected by the proposed architectures.
In this paper, we present our approach and the system description for the Social Media Mining for Health Applications (SMM4H) Shared Task 1,2 and 4 (2019). Our main contribution is to show the effectiveness of Transfer Learning approaches like BERT and ULMFiT, and how they generalize for the classification tasks like identification of adverse drug reaction mentions and reporting of personal health problems in tweets. We show the use of stacked embeddings combined with BLSTM+CRF tagger for identifying spans mentioning adverse drug reactions in tweets. We also show that these approaches perform well even with imbalanced dataset in comparison to undersampling and oversampling.
This paper details our approach to the task of detecting reportage of adverse drug reaction in tweets as part of the 2019 social media mining for healthcare applications shared task. We employed a combination of three types of word representations as input to a LSTM model. With this approach, we achieved an F1 score of 0.5209.
Analyzing social media posts can offer insights into a wide range of topics that are commonly discussed online, providing valuable information for studying various health-related phenomena reported online. The outcome of this work can offer insights into pharmacovigilance research to monitor the adverse effects of medications. This research specifically looks into mentions of adverse drug reactions (ADRs) in Twitter data through the Social Media Mining for Health Applications (SMM4H) Shared Task 2019. Adverse drug reactions are undesired harmful effects which can arise from medication or other methods of treatment. The goal of this research is to build accurate models using natural language processing techniques to detect reports of adverse drug reactions in Twitter data and extract these words or phrases.
Developers of cross-linguistic semantic annotation schemes face a number of issues not encountered in monolingual annotation. This paper discusses four such issues, related to the establishment of annotation labels, and the treatment of languages with more fine-grained, more coarse-grained, and cross-cutting categories. We propose that a lattice-like architecture of the annotation categories can adequately handle all four issues, and at the same time remain both intuitive for annotators and faithful to typological insights. This position is supported by a brief annotation experiment.
Meaning banking—creating a semantically annotated corpus for the purpose of semantic parsing or generation—is a challenging task. It is quite simple to come up with a complex meaning representation, but it is hard to design a simple meaning representation that captures many nuances of meaning. This paper lists some lessons learned in nearly ten years of meaning annotation during the development of the Groningen Meaning Bank (Bos et al., 2017) and the Parallel Meaning Bank (Abzianidze et al., 2017). The paper’s format is rather unconventional: there is no explicit related work, no methodology section, no results, and no discussion (and the current snippet is not an abstract but actually an introductory preface). Instead, its structure is inspired by work of Traum (2000) and Bender (2013). The list starts with a brief overview of the existing meaning banks (Section 1) and the rest of the items are roughly divided into three groups: corpus collection (Section 2 and 3, annotation methods (Section 4–11), and design of meaning representations (Section 12–30). We hope this overview will give inspiration and guidance in creating improved meaning banks in the future
In this paper, we propose an extension to Abstract Meaning Representations (AMRs) to encode scope information of quantifiers and negation, in a way that overcomes the semantic gaps of the schema while maintaining its cognitive simplicity. Specifically, we address three phenomena not previously part of the AMR specification: quantification, negation (generally), and modality. The resulting representation, which we call “Uniform Meaning Representation” (UMR), adopts the predicative core of AMR and embeds it under a “scope” graph when appropriate. UMR representations differ from other treatments of quantification and modal scope phenomena in two ways: (a) they are more transparent; and (b) they specify default scope when possible.‘
The parsing accuracy varies a great deal for different meaning representations. In this paper, we compare the parsing performances between Abstract Meaning Representation (AMR) and Minimal Recursion Semantics (MRS), and provide an in-depth analysis of what factors contributed to the discrepancy in their parsing accuracy. By crystalizing the trade-off between representation expressiveness and ease of automatic parsing, we hope our results can help inform the design of the next-generation meaning representations.
Three broad approaches have been attempted to combine distributional and structural/symbolic aspects to construct meaning representations: a) injecting linguistic features into distributional representations, b) injecting distributional features into symbolic representations or c) combining structural and distributional features in the final representation. This work focuses on an example of the third and less studied approach: it extends the Graphical Knowledge Representation (GKR) to include distributional features and proposes a division of semantic labour between the distributional and structural/symbolic features. We propose two extensions of GKR that clearly show this division and empirically test one of the proposals on an NLI dataset with hard compositional pairs.
Abstract Unscoped episodic logical form (ULF) is a semantic representation capturing the predicate-argument structure of English within the episodic logic formalism in relation to the syntactic structure, while leaving scope, word sense, and anaphora unresolved. We describe how ULF can be used to generate natural language inferences that are grounded in the semantic and syntactic structure through a small set of rules defined over interpretable predicates and transformations on ULFs. The semantic restrictions placed by ULF semantic types enables us to ensure that the inferred structures are semantically coherent while the nearness to syntax enables accurate mapping to English. We demonstrate these inferences on four classes of conversationally-oriented inferences in a mixed genre dataset with 68.5% precision from human judgments.
The view that the representation of information structure (IS) should be a part of (any type of) representation of meaning is based on the fact that IS is a semantically relevant phenomenon. In the contribution, three arguments supporting this view are briefly summarized, namely, the relation of IS to the interpretation of negation and presupposition, the relevance of IS to the understanding of discourse connectivity and for the establishment and interpretation of coreference relations. Afterwards, possible integration of the description of the main ingredient of IS into a meaning representation is illustrated.
It is known that discourse connectives are the most salient indicators of discourse relations. State-of-the-art parsers being developed to predict explicit discourse connectives exploit annotated discourse corpora but a lexicon of discourse connectives is also needed to enable further research in discourse structure and support the development of language technologies that use these structures for text understanding. This paper presents a lexicon of Turkish discourse connectives built by automatic means. The lexicon has the format of the German connective lexicon, DiMLex, where for each discourse connective, information about the connective‘s orthographic variants, syntactic category and senses are provided along with sample relations. In this paper, we describe the data sources we used and the development steps of the lexicon.
This paper presents a new task-oriented meaning representation called meta-semantics, that is designed to detect patients with early symptoms of Alzheimer’s disease by analyzing their language beyond a syntactic or semantic level. Meta-semantic representation consists of three parts, entities, predicate argument structures, and discourse attributes, that derive rich knowledge graphs. For this study, 50 controls and 50 patients with mild cognitive impairment (MCI) are selected, and meta-semantic representation is annotated on their speeches transcribed in text. Inter-annotator agreement scores of 88%, 82%, and 89% are achieved for the three types of annotation, respectively. Five analyses are made using this annotation, depicting clear distinctions between the control and MCI groups. Finally, a neural model is trained on features extracted from those analyses to classify MCI patients from normal controls, showing a high accuracy of 82% that is very promising.
Ellipsis is very common in language. It’s necessary for natural language processing to restore the elided elements in a sentence. However, there’s only a few corpora annotating the ellipsis, which draws back the automatic detection and recovery of the ellipsis. This paper introduces the annotation of ellipsis in Chinese sentences, using a novel graph-based representation Abstract Meaning Representation (AMR), which has a good mechanism to restore the elided elements manually. We annotate 5,000 sentences selected from Chinese TreeBank (CTB). We find that 54.98% of sentences have ellipses. 92% of the ellipses are restored by copying the antecedents’ concepts. and 12.9% of them are the new added concepts. In addition, we find that the elided element is a word or phrase in most cases, but sometimes only the head of a phrase or parts of a phrase, which is rather hard for the automatic recovery of ellipsis.
This paper proposes a novel representation of event structure by separating verbal semantics and the meaning of argument structure constructions that verbs occur in. Our model demonstrates how the two meaning representations interact. Our model thus effectively deals with various verb construals in different argument structure constructions, unlike purely verb-based approaches. However, unlike many constructionally-based approaches, we also provide a richer representation of the event structure evoked by the verb meaning.
In this paper, we propose a new type of semantic representation of Construction Grammar that combines constructions with the vector representations used in Distributional Semantics. We introduce a new framework, Distributional Construction Grammar, where grammar and meaning are systematically modeled from language use, and finally, we discuss the kind of contributions that distributional models can provide to CxG representation from a linguistic and cognitive perspective.
Humans have the unique ability to infer information about participants in a scene, even if they are not mentioned in a text about that scene. Computer systems cannot do so without explicit information about those participants. This paper addresses the linguistic phenomenon of null-instantiated frame elements, i.e., implicit semantic roles, and their representation in FrameNet (FN). It motivates FN’s annotation practice, and illustrates three types of null-instantiated arguments that FrameNet tracks, noting that other lexical resources do not record such semantic-pragmatic information, despite its need in natural language understanding (NLU), and the elaborate efforts to create new datasets. It challenges the community to appeal to FN data to develop more sophisticated techniques for recognizing implicit semantic roles, and creating needed datasets. Although the annotation of null-instantiated roles was lexicographically motivated, FN provides useful information for text processing, and therefore must be considered in the design of any meaning representation for natural language understanding.
This paper concerns the application of Abstract Meaning Representation (AMR) to Korean. In this regard, it focuses on the copula construction and its negation and the case-stacking phenomenon thereof. To illustrate this clearly, we reviewed the :domain annotation scheme from various perspectives. In this process, the existing annotation guidelines were improved to devise annotation schemes for each issue under the principle of pursuing consistency and efficiency of annotation without distorting the characteristics of Korean.
This paper proposes using a Bidirectional LSTM-CRF model in order to identify the tense and aspect of verbs. The information that this classifier outputs can be useful for ordering events and can provide a pre-processing step to improve efficiency of annotating this type of information. This neural network architecture has been successfully employed for other sequential labeling tasks, and we show that it significantly outperforms the rule-based tool TMV-annotator on the Propbank I dataset.
Research on adpositions and possessives in multiple languages has led to a small inventory of general-purpose meaning classes that disambiguate tokens. Importantly, that work has argued for a principled separation of the semantic role in a scene from the function coded by morphosyntax. Here, we ask whether this approach can be generalized beyond adpositions and possessives to cover all scene participants—including subjects and objects—directly, without reference to a frame lexicon. We present new guidelines for English and the results of an interannotator agreement study.
This paper presents a case study on meaning representation for Vietnamese. Having introduced several existing semantic representation schemes for different languages, we select as basis for our work on Vietnamese AMR (Abstract Meaning Representation). From it, we define a meaning representation label set by adapting the English schema and taking into account the specific characteristics of Vietnamese.
This paper announces the release of a new version of the English lexical resource VerbNet with substantially revised semantic representations designed to facilitate computer planning and reasoning based on human language. We use the transfer of possession and transfer of information event representations to illustrate both the general framework of the representations and the types of nuances the new representations can capture. These representations use a Generative Lexicon-inspired subevent structure to track attributes of event participants across time, highlighting oppositions and temporal and causal relations among the subevents.
We propose a coreference annotation scheme as a layer on top of the Universal Conceptual Cognitive Annotation foundational layer, treating units in predicate-argument structure as a basis for entity and event mentions. We argue that this allows coreference annotators to sidestep some of the challenges faced in other schemes, which do not enforce consistency with predicate-argument structure and vary widely in what kinds of mentions they annotate and how. The proposed approach is examined with a pilot annotation study and compared with annotations from other schemes.
Natural language understanding at the semantic level and independent of language variations is of great practical value. Existing approaches such as semantic role labeling (SRL) and abstract meaning representation (AMR) still have features related to the peculiarities of the particular language. In this work we describe various challenges and possible solutions in designing a semantic representation that is universal across a variety of languages.
This paper presents an annotation scheme for modality that employs a dependency structure. Events and sources (here, conceivers) are represented as nodes and epistemic strength relations characterize the edges. The epistemic strength values are largely based on Saurí and Pustejovsky’s (2009) FactBank, while the dependency structure mirrors Zhang and Xue’s (2018b) approach to temporal relations. Six documents containing 377 events have been annotated by two expert annotators with high levels of agreement.
We detail refinements made to Abstract Meaning Representation (AMR) that make the representation more suitable for supporting a situated dialogue system, where a human remotely controls a robot for purposes of search and rescue and reconnaissance. We propose 36 augmented AMRs that capture speech acts, tense and aspect, and spatial information. This linguistic information is vital for representing important distinctions, for example whether the robot has moved, is moving, or will move. We evaluate two existing AMR parsers for their performance on dialogue data. We also outline a model for graph-to-graph conversion, in which output from AMR parsers is converted into our refined AMRs. The design scheme presented here, though task-specific, is extendable for broad coverage of speech acts using AMR in future task-independent work.
Pictures can enrich storytelling experiences. We propose a framework that can automatically compose a picture book by understanding story text and visualizing it with painting elements, i.e., characters and backgrounds. For story understanding, we extract key information from a story on both sentence level and paragraph level, including characters, scenes and actions. These concepts are organized and visualized in a way that depicts the development of a story. We collect a set of Chinese stories for children and apply our approach to compose pictures for stories. Extensive experiments are conducted towards story event extraction for visualization to demonstrate the effectiveness of our method.
Visual storytelling is the task of generating stories based on a sequence of images. Inspired by the recent works in neural generation focusing on controlling the form of text, this paper explores the idea of generating these stories in different personas. However, one of the main challenges of performing this task is the lack of a dataset of visual stories in different personas. Having said that, there are independent datasets for both visual storytelling and annotated sentences for various persona. In this paper we describe an approach to overcome this by getting labelled persona data from a different task and leveraging those annotations to perform persona based story generation. We inspect various ways of incorporating personality in both the encoder and the decoder representations to steer the generation in the target direction. To this end, we propose five models which are incremental extensions to the baseline model to perform the task at hand. In our experiments we use five different personas to guide the generation process. We find that the models based on our hypotheses perform better at capturing words while generating stories in the target persona.
We propose a novel take on understanding narratives in social media, focusing on learning ”functional story schemas”, which consist of sets of stereotypical functional structures. We develop an unsupervised pipeline to extract schemas and apply our method to Reddit posts to detect schematic structures that are characteristic of different subreddits. We validate our schemas through human interpretation and evaluate their utility via a text classification task. Our experiments show that extracted schemas capture distinctive structural patterns in different subreddits, improving classification performance of several models by 2.4% on average. We also observe that these schemas serve as lenses that reveal community norms.
Automatically generating globally coherent stories is a challenging problem. Neural text generation models have been shown to perform well at generating fluent sentences from data, but they usually fail to keep track of the overall coherence of the story after a couple of sentences. Existing work that incorporates a text planning module succeeded in generating recipes and dialogues, but appears quite data-demanding. We propose a novel story generation approach that generates globally coherent stories from a fairly small corpus. The model exploits a symbolic text planning module to produce text plans, thus reducing the demand of data; a neural surface realization module then generates fluent text conditioned on the text plan. Human evaluation showed that our model outperforms various baselines by a wide margin and generates stories which are fluent as well as globally coherent.
Neural network based approaches to automated story plot generation attempt to learn how to generate novel plots from a corpus of natural language plot summaries. Prior work has shown that a semantic abstraction of sentences called events improves neural plot generation and and allows one to decompose the problem into: (1) the generation of a sequence of events (event-to-event) and (2) the transformation of these events into natural language sentences (event-to-sentence). However, typical neural language generation approaches to event-to-sentence can ignore the event details and produce grammatically-correct but semantically-unrelated sentences. We present an ensemble-based model that generates natural language guided by events. Our method outperforms the baseline sequence-to-sequence model. Additionally, we provide results for a full end-to-end automated story generation system, demonstrating how our model works with existing systems designed for the event-to-event problem.
Centrality of emotion for the stories told by humans is underpinned by numerous studies in literature and psychology. The research in automatic storytelling has recently turned towards emotional storytelling, in which characters’ emotions play an important role in the plot development (Theune et al., 2004; y Perez, 2007; Mendez et al., 2016). However, these studies mainly use emotion to generate propositional statements in the form “A feels affection towards B” or “A confronts B”. At the same time, emotional behavior does not boil down to such propositional descriptions, as humans display complex and highly variable patterns in communicating their emotions, both verbally and non-verbally. In this paper, we analyze how emotions are expressed non-verbally in a corpus of fan fiction short stories. Our analysis shows that stories written by humans convey character emotions along various non-verbal channels. We find that some non-verbal channels, such as facial expressions and voice characteristics of the characters, are more strongly associated with joy, while gestures and body postures are more likely to occur with trust. Based on our analysis, we argue that automatic storytelling systems should take variability of emotion into account when generating descriptions of characters’ emotions.
In text generation, generating long stories is still a challenge. Coherence tends to decrease rapidly as the output length increases. Especially for generated stories, coherence of the narrative is an important quality aspect of the output text. In this paper we examine how narrative coherence is attained in the submissions of NaNoGenMo 2018, an online text generation event where participants are challenged to generate a 50,000 word novel. We list the main approaches that were used to generate coherent narratives and link them to scientific literature. Finally, we give recommendations on when to use which approach.
This study explores the relation between lexical concreteness and narrative text quality. We present a methodology to quantitatively measure lexical concreteness of a text. We apply it to a corpus of student stories, scored according to writing evaluation rubrics. Lexical concreteness is weakly-to-moderately related to story quality, depending on story-type. The relation is mostly borne by adjectives and nouns, but also found for adverbs and verbs.
In this work, we deploy a logistic regression classifier to ascertain whether a given document belongs to the fiction or non-fiction genre. For genre identification, previous work had proposed three classes of features, viz., low-level (character-level and token counts), high-level (lexical and syntactic information) and derived features (type-token ratio, average word length or average sentence length). Using the Recursive feature elimination with cross-validation (RFECV) algorithm, we perform feature selection experiments on an exhaustive set of nineteen features (belonging to all the classes mentioned above) extracted from Brown corpus text. As a result, two simple features viz., the ratio of the number of adverbs to adjectives and the number of adjectives to pronouns turn out to be the most significant. Subsequently, our classification experiments aimed towards genre identification of documents from the Brown and Baby BNC corpora demonstrate that the performance of a classifier containing just the two aforementioned features is at par with that of a classifier containing the exhaustive feature set.
Script knowledge consists of detailed information on everyday activities. Such information is often taken for granted in text and needs to be inferred by readers. Therefore, script knowledge is a central component to language comprehension. Previous work on representing scripts is mostly based on extensive manual work or limited to scenarios that can be found with sufficient redundancy in large corpora. We introduce the task of scenario detection, in which we identify references to scripts. In this task, we address a wide range of different scripts (200 scenarios) and we attempt to identify all references to them in a collection of narrative texts. We present a first benchmark data set and a baseline model that tackles scenario detection using techniques from topic segmentation and text classification.
Interesting stories often are built around interesting characters. Finding and detailing what makes an interesting character is a real challenge, but certainly a significant cue is the character personality traits. Our exploratory work tests the adaptability of the current personality traits theories to literal characters, focusing on the analysis of utterances in theatre scripts. And, at the opposite, we try to find significant traits for interesting characters. The preliminary results demonstrate that our approach is reasonable. Using machine learning for gaining insight into the personality traits of fictional characters can make sense.
Pre-scheduled events, such as TV shows and sports games, usually garner considerable attention from the public. Twitter captures large volumes of discussions and messages related to these events, in real-time. Twitter streams related to pre-scheduled events are characterized by the following: (1) spikes in the volume of published tweets reflect the highlights of the event and (2) some of the published tweets make reference to the characters involved in the event, in the context in which they are currently portrayed in a subevent. In this paper, we take advantage of these characteristics to identify the highlights of pre-scheduled events from tweet streams and we demonstrate a method to summarize these highlights. We evaluate our algorithm on tweets collected around 2 episodes of a popular TV show, Game of Thrones, Season 7.
We study the problem of generating interesting endings for stories. Neural generative models have shown promising results for various text generation problems. Sequence to Sequence (Seq2Seq) models are typically trained to generate a single output sequence for a given input sequence. However, in the context of a story, multiple endings are possible. Seq2Seq models tend to ignore the context and generate generic and dull responses. Very few works have studied generating diverse and interesting story endings for the same story context. In this paper, we propose models which generate more diverse and interesting outputs by 1) training models to focus attention on important keyphrases of the story, and 2) promoting generating nongeneric words. We show that the combination of the two leads to more interesting endings.
As the size of investment for movie production grows bigger, the need for predicting a movie’s success in early stages has increased. To address this need, various approaches have been proposed, mostly relying on movie reviews, trailer movie clips, and SNS postings. However, all of these are available only after a movie is produced and released. To enable a more earlier prediction of a movie’s performance, we propose a deep-learning based approach to predict the success of a movie using only its plot summary text. This paper reports the results evaluating the efficacy of the proposed method and concludes with discussions and future work.
The presence of toxic content has become a major problem for many online communities. Moderators try to limit this problem by implementing more and more refined comment filters, but toxic users are constantly finding new ways to circumvent them. Our hypothesis is that while modifying toxic content and keywords to fool filters can be easy, hiding sentiment is harder. In this paper, we explore various aspects of sentiment detection and their correlation to toxicity, and use our results to implement a toxicity detection tool. We then test how adding the sentiment information helps detect toxicity in three different real-world datasets, and incorporate subversion to these datasets to simulate a user trying to circumvent the system. Our results show sentiment information has a positive impact on toxicity detection.
Interactions among users on social network platforms are usually positive, constructive and insightful. However, sometimes people also get exposed to objectionable content such as hate speech, bullying, and verbal abuse etc. Most social platforms have explicit policy against hate speech because it creates an environment of intimidation and exclusion, and in some cases may promote real-world violence. As users’ interactions on today’s social networks involve multiple modalities, such as texts, images and videos, in this paper we explore the challenge of automatically identifying hate speech with deep multimodal technologies, extending previous research which mostly focuses on the text signal alone. We present a number of fusion approaches to integrate text and photo signals. We show that augmenting text with image embedding information immediately leads to a boost in performance, while applying additional attention fusion methods brings further improvement.
We developed a machine-learning-based method to detect video game players that harass teammates or opponents in chat earlier in the conversation. This real-time technology would allow gaming companies to intervene during games, such as issue warnings or muting or banning a player. In a proof-of-concept experiment on League of Legends data we compute and visualize evaluation metrics for a machine learning classifier as conversations unfold, and observe that the optimal precision and recall of detecting toxic players at each moment in the conversation depends on the confidence threshold of the classifier: the threshold should start low, and increase as the conversation unfolds. How fast this sliding threshold should increase depends on the training set size.
Technologies for abusive language detection are being developed and applied with little consideration of their potential biases. We examine racial bias in five different sets of Twitter data annotated for hate speech and abusive language. We train classifiers on these datasets and compare the predictions of these classifiers on tweets written in African-American English with those written in Standard American English. The results show evidence of systematic racial bias in all datasets, as classifiers trained on them tend to predict that tweets written in African-American English are abusive at substantially higher rates. If these abusive language detection systems are used in the field they will therefore have a disproportionate negative impact on African-American social media users. Consequently, these systems may discriminate against the groups who are often the targets of the abuse we are trying to detect.
Discussion forum participation represents one of the crucial factors for learning and often the only way of supporting social interactions in online settings. However, as much as sharing new ideas or asking thoughtful questions contributes learning, verbally abusive behaviors, such as expressing negative emotions in online discussions, could have disproportionate detrimental effects. To provide means for mitigating the potential negative effects on course participation and learning, we developed an automated classifier for identifying communication that show linguistic patterns associated with hostility in online forums. In so doing, we employ several well-established automated text analysis tools and build on the common practices for handling highly imbalanced datasets and reducing the sensitivity to overfitting. Although still in its infancy, our approach shows promising results (ROC AUC .73) towards establishing a robust detector of abusive behaviors. We further provide an overview of the classification (linguistic and contextual) features most indicative of online aggression.
Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflict between citizen. Moreover, hate speech has a target, category, and level that also needs to be detected to help the authority in prioritizing which hate speech must be addressed immediately. This research discusses multi-label text classification for abusive language and hate speech detection including detecting the target, category, and level of hate speech in Indonesian Twitter using machine learning approach with Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) classifier and Binary Relevance (BR), Label Power-set (LP), and Classifier Chains (CC) as the data transformation method. We used several kinds of feature extractions which are term frequency, orthography, and lexicon features. Our experiment results show that in general RFDT classifier using LP as the transformation method gives the best accuracy with fast computational time.
Recent concerns over abusive behavior on their platforms have pressured social media companies to strengthen their content moderation policies. However, user opinions on these policies have been relatively understudied. In this paper, we present an analysis of user responses to a September 27, 2018 announcement about the quarantine policy on Reddit as a case study of to what extent the discourse on content moderation is polarized by users’ ideological viewpoint. We introduce a novel partitioning approach for characterizing user polarization based on their distribution of participation across interest subreddits. We then use automated techniques for capturing framing to examine how users with different viewpoints discuss moderation issues, finding that right-leaning users invoked censorship while left-leaning users highlighted inconsistencies on how content policies are applied. Overall, we argue for a more nuanced approach to moderation by highlighting the intersection of behavior and ideology in considering how abusive language is defined and regulated.
The goal of any social media platform is to facilitate healthy and meaningful interactions among its users. But more often than not, it has been found that it becomes an avenue for wanton attacks. We propose an experimental study that has three aims: 1) to provide us with a deeper understanding of current data sets that focus on different types of abusive language, which are sometimes overlapping (racism, sexism, hate speech, offensive language, and personal attacks); 2) to investigate what type of attention mechanism (contextual vs. self-attention) is better for abusive language detection using deep learning architectures; and 3) to investigate whether stacked architectures provide an advantage over simple architectures for this task.
Online abusive content detection is an inherently difficult task. It has received considerable attention from academia, particularly within the computational linguistics community, and performance appears to have improved as the field has matured. However, considerable challenges and unaddressed frontiers remain, spanning technical, social and ethical dimensions. These issues constrain the performance, efficiency and generalizability of abusive content detection systems. In this article we delineate and clarify the main challenges and frontiers in the field, critically evaluate their implications and discuss potential solutions. We also highlight ways in which social scientific insights can advance research. We discuss the lack of support given to researchers working with abusive content and provide guidelines for ethical research.
Over the past years, the amount of online offensive speech has been growing steadily. To successfully cope with it, machine learning are applied. However, ML-based techniques require sufficiently large annotated datasets. In the last years, different datasets were published, mainly for English. In this paper, we present a new dataset for Portuguese, which has not been in focus so far. The dataset is composed of 5,668 tweets. For its annotation, we defined two different schemes used by annotators with different levels of expertise. Firstly, non-experts annotated the tweets with binary labels (‘hate’ vs. ‘no-hate’). Secondly, expert annotators classified the tweets following a fine-grained hierarchical multiple label scheme with 81 hate speech categories in total. The inter-annotator agreement varied from category to category, which reflects the insight that some types of hate speech are more subtle than others and that their detection depends on personal perception. This hierarchical annotation scheme is the main contribution of the presented work, as it facilitates the identification of different types of hate speech and their intersections. To demonstrate the usefulness of our dataset, we carried a baseline classification experiment with pre-trained word embeddings and LSTM on the binary classified data, with a state-of-the-art outcome.
Social media platforms like Twitter and Instagram face a surge in cyberbullying phenomena against young users and need to develop scalable computational methods to limit the negative consequences of this kind of abuse. Despite the number of approaches recently proposed in the Natural Language Processing (NLP) research area for detecting different forms of abusive language, the issue of identifying cyberbullying phenomena at scale is still an unsolved problem. This is because of the need to couple abusive language detection on textual message with network analysis, so that repeated attacks against the same person can be identified. In this paper, we present a system to monitor cyberbullying phenomena by combining message classification and social network analysis. We evaluate the classification module on a data set built on Instagram messages, and we describe the cyberbullying monitoring user interface.
Hate speech and abusive language have become a common phenomenon on Arabic social media. Automatic hate speech and abusive detection systems can facilitate the prohibition of toxic textual contents. The complexity, informality and ambiguity of the Arabic dialects hindered the provision of the needed resources for Arabic abusive/hate speech detection research. In this paper, we introduce the first publicly-available Levantine Hate Speech and Abusive (L-HSAB) Twitter dataset with the objective to be a benchmark dataset for automatic detection of online Levantine toxic contents. We, further, provide a detailed review of the data collection steps and how we design the annotation guidelines such that a reliable dataset annotation is guaranteed. This has been later emphasized through the comprehensive evaluation of the annotations as the annotation agreement metrics of Cohen’s Kappa (k) and Krippendorff’s alpha (α) indicated the consistency of the annotations.
In this paper, we describe a workflow for the data-driven acquisition and semantic scaling of a lexicon that covers lexical items from the lower end of the German language register—terms typically considered as rough, vulgar or obscene. Since the fine semantic representation of grades of obscenity can only inadequately be captured at the categorical level (e.g., obscene vs. non-obscene, or rough vs. vulgar), our main contribution lies in applying best-worst scaling, a rating methodology that has already been shown to be useful for emotional language, to capture the relative strength of obscenity of lexical items. We describe the empirical foundations for bootstrapping such a low-end lexicon for German by starting from manually supplied lexicographic categorizations of a small seed set of rough and vulgar lexical items and automatically enlarging this set by means of distributional semantics. We then determine the degrees of obscenity for the full set of all acquired lexical items by letting crowdworkers comparatively assess their pejorative grade using best-worst scaling. This semi-automatically enriched lexicon already comprises 3,300 lexical items and incorporates 33,000 vulgarity ratings. Using it as a seed lexicon for fully automatic lexical acquisition, we were able to raise its coverage up to slightly more than 11,000 entries.
We address the task of automatically detecting toxic content in user generated texts. We fo cus on exploring the potential for preemptive moderation, i.e., predicting whether a particular conversation thread will, in the future, incite a toxic comment. Moreover, we perform preliminary investigation of whether a model that jointly considers all comments in a conversation thread outperforms a model that considers only individual comments. Using an existing dataset of conversations among Wikipedia contributors as a starting point, we compile a new large-scale dataset for this task consisting of labeled comments and comments from their conversation threads.
The text we see in social media suffers from lots of undesired characterstics like hatespeech, abusive language, insults etc. The nature of this text is also very different compared to the traditional text we see in news with lots of obfuscated words, intended typos. This poses several robustness challenges to many natural language processing (NLP) techniques developed for traditional text. Many techniques proposed in the recent times such as charecter encoding models, subword models, byte pair encoding to extract subwords can aid in dealing with few of these nuances. In our work, we analyze the effectiveness of each of the above techniques, compare and contrast various word decomposition techniques when used in combination with others. We experiment with recent advances of finetuning pretrained language models, and demonstrate their robustness to domain shift. We also show our approaches achieve state of the art performance on Wikipedia attack, toxicity datasets, and Twitter hatespeech dataset.
Hate speech detectors must be applicable across a multitude of services and platforms, and there is hence a need for detection approaches that do not depend on any information specific to a given platform. For instance, the information stored about the text’s author may differ between services, and so using such data would reduce a system’s general applicability. The paper thus focuses on using exclusively text-based input in the detection, in an optimised architecture combining Convolutional Neural Networks and Long Short-Term Memory-networks. The hate speech detector merges two strands with character n-grams and word embeddings to produce the final classification, and is shown to outperform comparable previous approaches.
In the era of social media, hate speech, trolling and verbal abuse have become a common issue. We present an approach to automatically classify such statements, using a new deep learning architecture. Our model comprises of a Multi Dimension Capsule Network that generates the representation of sentences which we use for classification. We further provide an analysis of our model’s interpretation of such statements. We compare the results of our model with state-of-art classification algorithms and demonstrate our model’s ability. It also has the capability to handle comments that are written in both Hindi and English, which are provided in the TRAC dataset. We also compare results on Kaggle’s Toxic comment classification dataset.
The paper proposes an investigation on the role of populist themes and rhetoric in an Italian Twitter corpus of hate speech against immigrants. The corpus had been annotated with four new layers of analysis: Nominal Utterances, that can be seen as consistent with populist rhetoric; In-out-group rhetoric, a very common populist strategy to polarize public opinion; Slogan-like nominal utterances, that may convey the call for severe illiberal policies against immigrants; News, to recognize the role of newspapers (headlines or reference to articles) in the Twitter political discourse on immigration featured by hate speech.
The disciplines of Gender Studies and Data Science are incompatible. This is conventional wisdom, supported by how many computational studies simplify gender into an immutable binary categorization that appears crude to the critical social researcher. I argue that the characterization of gender norms is context specific and may prove valuable in constructing useful models. I show how gender can be framed in computational studies as a stylized repetition of acts mediated by a social structure, and not a possessed biological category. By conducting a review of existing work, I show how gender should be explored in multiplicity in computational research through clustering techniques, and layout how this is being achieved in a study in progress on gender hostility on Stack Overflow.
The present paper introduces a theoretical model for explaining aggressive online comments from a sociological perspective. It is innovative as it combines individual, situational, and social-structural determinants of online aggression and tries to theoretically derive their interplay. Moreover, the paper suggests an empirical strategy for testing the model. The main contribution will be to match online commenting data with survey data containing rich background data of non- /aggressive online commentators.
There are publicly available general purpose sentiment lexicons in some high resource languages but very few exist in the low resource languages. This makes it difficult to directly perform sentiment analysis tasks in such languages. The objective of this work is to create a general purpose sentiment lexicon for Igbo language that can determine the sentiment of documents written in Igbo language without having to translate it to English language. The material used was an automatically translated Liu’s lexicon and manual addition of Igbo native words. The result of this work is a general purpose lexicon – IgboSentilex. The performance was tested on the BBC Igbo news channel. It returned an average polarity agreement of 95% with other general purpose sentiment lexicons.
Currently, there is a lack of computational grammar resources for many under-resourced languages which limits the ability to develop Natural Language Processing (NLP) tools and applications such as Multilingual Document Authoring, Computer-Assisted Language Learning (CALL) and Low-Coverage Machine Translation (MT) for these languages. In this paper, we present our attempt to formalise the grammar of two such languages: Runyankore and Rukiga. For this formalisation we use the Grammatical Framework (GF) and its Resource Grammar Library (GF-RGL).
This work presents a speech recognition model for Tigrinya language .The Deep Neural Network is used to make the recognition model. The Long Short-Term Memory Network (LSTM), which is a special kind of Recurrent Neural Network composed of Long Short-Term Memory blocks, is the primary layer of our neural network model. The 40-dimensional features are MFCC-LDA-MLLT-fMLLR with CMN were used. The acoustic models are trained on features that are obtained by projecting down to 40 dimensions using linear discriminant analysis (LDA). Moreover, speaker adaptive training (SAT) is done using a single feature-space maximum likelihood linear regression (FMLLR) transform estimated per speaker. We train and compare LSTM and DNN models at various numbers of parameters and configurations. We show that LSTM models converge quickly and give state of the art speech recognition performance for relatively small sized models. Finally, the accuracy of the model is evaluated based on the recognition rate.
In this paper, we presented a WSD system that uses LDA topics for semantic expansion of document words. Our system also uses sense frequency information from SemCor to give higher priority to the senses which are more probable to happen.
We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items. It is based on aspect extraction with neural networks and combines the advantages of deep learning and topic modeling. It is mainly designed for recommendations, but an important secondary goal of AspeRa is to discover coherent aspects of reviews that can be used to explain predictions or for user profiling. We conduct a comprehensive empirical study of AspeRa, showing that it outperforms state-of-the-art models in terms of recommendation quality and produces interpretable aspects. This paper is an abridged version of our work (Nikolenko et al., 2019)
Recognizing the arrow of time in the context of paragraphs in short stories is a challenging task. i.e., given only two paragraphs (excerpted from a random position in a short story), determining which comes first and which comes next is a difficult task even for humans. In this paper, we have collected and curated a novel dataset for tackling this challenging task. We have shown that a pre-trained BERT architecture achieves reasonable accuracy on the task, and outperforms RNN-based architectures.
The significance of computers and handheld devices are not deniable in the modern world of today. Texts are entered to these devices using word processing programs as well as other techniques and word prediction is one of the techniques. Word Prediction is the action of guessing or forecasting what word comes after, based on some current information, and it is the main focus of this study. Even though Amharic is used by a large number of populations, no significant work is done on the topic of word sequence prediction. In this study, Amharic word sequence prediction model is developed with statistical methods using Hidden Markov Model by incorporating detailed Part of speech tag. Evaluation of the model is performed using developed prototype and keystroke savings (KSS) as a metrics. According to our experiment, prediction result using a bi-gram with detailed Part of Speech tag model has higher KSS and it is better compared to tri-gram model and better than those without Part of Speech tag. Therefore, statistical approach with Detailed POS has quite good potential on word sequence prediction for Amharic language. This research deals with designing word sequence prediction model in Amharic language. It is a language that is spoken in eastern Africa. One of the needs for Amharic word sequence prediction for mobile use and other digital devices is in order to facilitate data entry and communication in our language. Word sequence prediction is a challenging task for inflected languages. (Arora, 2007) These kinds of languages are morphologically rich and have enormous word forms. i.e. one word can have different forms. As Amharic language is highly inflected language and morphologically rich it shares this problem. (prediction, 2008) This problem makes word prediction system much more difficult and results poor performance. Due to this reason storing all forms in dictionary won’t solve the problem as in English and other less inflected languages. But considering other techniques that could help the predictor to suggest the next word like a POS based prediction should be used. Previous researches used dictionary approach with no consideration of context information. Hence storing all forms of words in dictionary for inflected languages such as Amharic language has been less effective. The main goal of this thesis is to implement Amharic word prediction model that works with better prediction speed and with narrowed search space as much as possible. We introduced two models; tags and words and linear interpolation that use part of speech tag information in addition to word n-grams in order to maximize the likelihood of syntactic appropriateness of the suggestions. We believe the results found reflect this. Amharic word sequence prediction using bi-gram model with higher POS weight and detailed Part of speech tag gave better keystroke savings in all scenarios of our experiment. The study followed Design Science Research Methodology (DSRM). Since DSRM includes approaches, techniques, tools, algorithms and evaluation mechanisms in the process, we followed statistical approach with statistical language modeling and built Amharic prediction model based on information from Part of Speech tagger. The statistics included in the systems varies from single word frequencies to part-of-speech tag n-grams. That means it included the statistics of Word frequencies, Word sequence frequencies, Part-of-speech sequence frequencies and other important information. Later on the system was evaluated using Keystroke Savings. (Lindh, 011). Linux mint was used as the main Operation System during the frame work design. We used corpus of 680,000 tagged words that has 31 tag sets, python programming language and its libraries for both the part of speech tagger and the predictor module. Other Tool that was used is the SRILIM (The SRI language modeling toolkit) in order to generate unigram bigram and trigram count as an input for the language model. SRILIM is toolkit that uses to build and apply statistical language modeling. This thesis presented Amharic word sequence prediction model using the statistical approach. We described a combined statistical and lexical word prediction system for handling inflected languages by making use of POS tags to build the language model. We developed Amharic language models of bigram and trigram for the training purpose. We obtained 29% of KSS using bigram model with detailed part ofspeech tag. Hence, Based on the experiments carried out for this study and the results obtained, the following conclusions were made. We concluded that employing syntactic information in the form of Part-of-Speech (POS) n-grams promises more effective predictions. We also can conclude data quantity, performance of POS tagger and data quality highly affects the keystroke savings. Here in our study the tests were done on a small collection of 100 phrases. According to our evaluation better Keystroke saving (KSS) is achieved when using bi-gram model than the tri-gram models. We believe the results obtained using the experiment of detailed Part of speech tags were effective Since speed and search space are the basic issues in word sequence prediction
In this work, we aim to build a unifying framework for relation extraction (RE), applying this on 3 highly used datasets with the ability to be extendable to new datasets. At the moment, the domain suffers from lack of reproducibility as well as a lack of consensus on generalizable techniques. Our framework will be open-sourced and will aid in performing systematic exploration on the effect of different modeling techniques, pre-processing, training methodologies and evaluation metrics on the 3 datasets to help establish a consensus.
Sexism is very common in social media and makes the boundaries of free speech tighter for female users. Automatically flagging and removing sexist content requires niche identification and description of the categories. In this study, inspired by social science work, we propose three categories of sexism toward women as follows: “Indirect sexism”, “Sexual sexism” and “Physical sexism”. We build classifiers such as Convolutional Neural Network (CNN) to automatically detect different types of sexism and address problems of annotation. Even though inherent non-interpretability of CNN is a challenge for users who detect sexism, as the reason classifying a given speech instance with regard to sexism is difficult to glance from a CNN. However, recent research developed interpretable CNN filters for text data. In a CNN, filters followed by different activation patterns along with global max-pooling can help us tease apart the most important ngrams from the rest. In this paper, we interpret a CNN model trained to classify sexism in order to understand different categories of sexism by detecting semantic categories of ngrams and clustering them. Then, these ngrams in each category are used to improve the performance of the classification task. It is a preliminary work using machine learning and natural language techniques to learn the concept of sexism and distinguishes itself by looking at more precise categories of sexism in social media along with an in-depth investigation of CNN’s filters.
We present a study in which we train neural models that approximate rules that assess the quality of English sentences. We modeled five rules using deep LSTMs trained over a dataset of sentences whose quality is evaluated under such rules. Preliminary results suggest the neural architecture can model such rules to high accuracy.
In this paper, we describe an attempt towards the development of parallel corpora for English and Ethiopian Languages, such as Amharic, Tigrigna, Afan-Oromo, Wolaytta and Ge’ez. The corpora are used for conducting bi-directional SMT experiments. The BLEU scores of the bi-directional SMT systems show a promising result. The morphological richness of the Ethiopian languages has a great impact on the performance of SMT especially when the targets are Ethiopian languages.
This paper describes an automatic discourse relation alignment experiment as an empirical justification of the planned annotation projection approach to enlarge the 3600-word multilingual corpus of TED Multilingual Discourse Bank (TED-MDB). The experiment is carried out on a single language pair (English-Turkish) included in TED-MDB. The paper first describes the creation of a large corpus of English-Turkish bi-sentences, then it presents a sense-based experiment that automatically aligns the relations in the English sentences of TED-MDB with the Turkish sentences. The results are very close to the results obtained from an earlier semi-automatic post-annotation alignment experiment validated by human annotators and are encouraging for future annotation projection tasks.
The paper describes the development a corpus of an English variety, i.e. China English, in or-der to provide a linguistic resource for researchers in the field of China English. The Corpus of China English (CCE) was built with due consideration given to its representativeness and authenticity. It was composed of more than 13,962,102 tokens in 15,333 texts evenly divided between the following four genres: newspapers, magazines, fiction and academic writings. The texts cover a wide range of domains, such as news, financial, politics, environment, social, culture, technology, sports, education, philosophy, literary, etc. It is a helpful resource for research on China English, computational linguistics, natural language processing, corpus linguistics and English language education.
In this paper, we present an effort to generate a joint Urdu, Roman Urdu and English trilingual lexicon using automated methods. We make a case for using statistical machine translation approaches and parallel corpora for dictionary creation. To this purpose, we use word alignment tools on the corpus and evaluate translations using human evaluators. Despite different writing script and considerable noise in the corpus our results show promise with over 85% accuracy of Roman Urdu–Urdu and 45% English–Urdu pairs.
Prepositional Phrase (PP) attachment is a classical problem in NLP for languages like English, which suffer from structural ambiguity. In this work, we solve this problem with the help of another language free from such ambiguities, using the parse tree of the parallel sentence in the other language, and word alignments. We formulate an optimization framework that encourages agreement between the parse trees for two languages, and solve it using a novel Dual Decomposition (DD) based algorithm. Experiments on the English-Hindi language pair show promising improvements over the baseline.
Social media posts reflect the emotions, intentions and mental state of the users. Twitter users who harass famous female figures may do so with different intentions and intensities. Recent studies have published datasets focusing on different types of online harassment, vulgar language, and emotional intensities. We trained, validate and test our proposed model, attention-based bidirectional neural network, on the three datasets:”online harassment”, “vulgar language” and “valance” and achieved state of the art performance in two of the datasets. We report F1 score for each dataset separately along with the final precision, recall and macro-averaged F1 score. In addition, we identify ten female figures from different professions and racial backgrounds who have experienced harassment on Twitter. We tested the trained models on ten collected corpuses each related to one famous female figure to predict the type of harassing language, the type of vulgar language and the degree of intensity of language occurring on their social platforms. Interestingly, the achieved results show different patterns of linguistic use targeting different racial background and occupations. The contribution of this study is two-fold. From the technical perspective, our proposed methodology is shown to be effective with a good margin in comparison to the previous state-of-the-art results on one of the two available datasets. From the social perspective, we introduce a methodology which can unlock facts about the nature of offensive language targeting women on online social platforms. The collected dataset will be shared publicly for further investigation.
Abstract The analysis of sentiments is imperative to make a decision for individuals, organizations, and governments. Due to the rapid growth of Awngi (Agew) text on the web, there is no available corpus annotated for sentiment analysis. In this paper, we present a SA model for the Awngi language spoken in Ethiopia, by using a supervised machine learning approach. We developed our corpus by collecting around 1500 posts from online sources. This research is begun to build and evaluate the model for opinionated Awngi music reviews. Thus, pre-processing techniques have been employed to clean the data, to convert transliterations to the native Ethiopic script for accessibility and convenience to typing and to change the words to their base form by removing the inflectional morphemes. After pre-processing, the corpus is manually annotated by three the language professional for giving polarity, and rate, their level of confidence in their selection and sentiment intensity scale values. To improve the calculation method of feature selection and weighting and proposed a more suitable SA algorithm for feature extraction named CHI and weight calculation named TF IDF, increasing the proportion and weight of sentiment words in the feature words. We employed Support Vector Machines (SVM), Naïve Bayes (NB) and Maximum Entropy (MxEn) machine learning algorithms. Generally, the results are encouraging, despite the morphological challenge in Awngi, the data cleanness and small size of data. We are believed that the results could improve further with a larger corpus.
We present a research on compositional treatment of questions in neo-davidsonian event semantics style. Our work is based on (Champollion, 2011) where only declarative sentences were considered. Our research is based on complex formal examples, paving the way towards further research in this domain and further testing on real-life corpora.
Unlike comprehension-style questions, clarification questions look for some missing information in a given context. However, without guidance, neural models for question generation, similar to dialog generation models, lead to generic and bland questions that cannot elicit useful information. We argue that controlling the level of specificity of the generated questions can have useful applications and propose a neural clarification question generation model for the same. We first train a classifier that annotates a clarification question with its level of specificity (generic or specific) to the given context. Our results on the Amazon questions dataset demonstrate that training a clarification question generation model on specificity annotated data can generate questions with varied levels of specificity to the given context.
Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation. Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree. Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy’s own preferences. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order while achieving competitive performance with conventional left-to-right generation.
Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society, causing serious concern. Several recent works tackle this problem, and propose methods for significantly reducing this gender bias in word embeddings, demonstrating convincing results. However, we argue that this removal is superficial. While the bias is indeed substantially reduced according to the provided bias definition, the actual effect is mostly hiding the bias, not removing it. The gender bias information is still reflected in the distances between “gender-neutralized” words in the debiased embeddings, and can be recovered from them. We present a series of experiments to support this claim, for two debiasing methods. We conclude that existing bias removal techniques are insufficient, and should not be trusted for providing gender-neutral modeling.
Many natural languages assign grammatical gender also to inanimate nouns in the language. In such languages, words that relate to the gender-marked nouns are inflected to agree with the noun’s gender. We show that this affects the word representations of inanimate nouns, resulting in nouns with the same gender being closer to each other than nouns with different gender. While “embedding debiasing” methods fail to remove the effect, we demonstrate that a careful application of methods that neutralize grammatical gender signals from the words’ context when training word embeddings is effective in removing it. Fixing the grammatical gender bias results in a positive effect on the quality of the resulting word embeddings, both in monolingual and cross lingual settings. We note that successfully removing gender signals, while achievable, is not trivial to do and that a language-specific morphological analyzer, together with careful usage of it, are essential for achieving good results.
The North American Product Classification System (NAPCS) is a comprehensive, hierarchical classification system for products (goods and services) that is consistent across the three North American countries. Beginning in 2017, the Economic Census will use NAPCS to produce economy-wide product tabulations. Respondents are asked to report data from a long, pre-specified list of potential products in a given industry, with some lists containing more than 50 potential products. Businesses have expressed the desire to alternatively supply Universal Product Codes (UPC) to the U. S. Census Bureau. Much work has been done around the categorization of products using product descriptions. No study has applied these efforts for the calculation of official statistics (statistics published by government agencies) using only the text of UPC product descriptions. The question we address in this paper is: Given UPC codes and their associated product descriptions, can we accurately predict NAPCS? We tested the feasibility of businesses submitting a spreadsheet with Universal Product Codes and their associated text descriptions. This novel strategy classified text with very high accuracy rates, all of our algorithms surpassed over 90 percent.
In this paper, we propose a novel online topic tracking framework, named IEDL, for tracking the topic changes related to deep learning techniques on Stack Exchange and automatically interpreting each identified topic. The proposed framework combines the prior topic distributions in a time window during inferring the topics in current time slice, and introduces a new ranking scheme to select most representative phrases and sentences for the inferred topics. Experiments on 7,076 Stack Exchange posts show the effectiveness of IEDL in tracking topic changes.
This paper presents ongoing work on the construction and alignment of predicate entailment graphs in English and German. We extract predicate-argument pairs from large corpora of monolingual English and German news text and construct monolingual paraphrase clusters and entailment graphs. We use an aligned subset of entities to derive the bilingual alignment of entities and relations, and achieve better than baseline results on a translated subset of a predicate entailment data set (Levy and Dagan, 2016) and the German portion of XNLI (Conneau et al., 2018).
We perform the natural language generation (NLG) task by mapping sets of Resource Description Framework (RDF) triples into text. First we investigate the impact of increasing the number of entity types in delexicalisaiton on the generation quality. Second we conduct different experiments to evaluate two widely applied language generation systems, encoder-decoder with attention and the Transformer model on a large benchmark dataset. We evaluate different models on automatic metrics, as well as the training time. To our knowledge, we are the first to apply Transformer model to this task.
We investigate English pronunciation patterns in Singaporean children in relation to their American and British counterparts by conducting archetypal analysis on selected vowel pairs. Given that Singapore adopts British English as the institutional standard, one might expect Singaporean children to follow British pronunciation patterns, but we observe that Singaporean children also present similar patterns to Americans for TRAP-BATH spilt vowels: (1) British and Singaporean children both produce these vowels with a relatively lowered tongue height. (2) These vowels are more fronted for American and Singaporean children (p < 0.001). In addition, when comparing /æ/ and /ε/ productions, British speakers show the clearest distinction between the two vowels; Singaporean and American speakers exhibit a higher and more fronted tongue position for /æ/ (p < 0.001), causing /æ/ to be acoustically more similar to /ε/.
In this work, we propose the use of phone-level language models to estimate phonotactic complexity—measured in bits per phoneme—which makes cross-linguistic comparison straightforward. We compare the entropy across languages using this simple measure, gaining insight on how complex different language’s phonotactics are. Finally, we show a very strong negative correlation between phonotactic complexity and the average length of words—Spearman rho=-0.744—when analysing a collection of 106 languages with 1016 basic concepts each.
This is a humanitarian work –a counter-terrorism effort. The presentation describes the experiences of developing a multi-lingua, interactive chatbot trained on the corpus of two Nigerian Languages (Hausa and Fulfude), with simultaneous translation to a third (Kanuri), to stimulate conversations, deliver tailored contents to the users thereby aiding in the detection of the probability and degree of radicalization in young learners through data analysis of the games moves and vocabularies. As chatbots have the ability to simulate a human conversation based on rhetorical behavior, the system is able to learn the need of individual user through constant interaction and deliver tailored contents that promote good behavior in Hausa, Fulfulde and Kanuri languages.
This work presents a set of experiments conducted to predict the gender of Twitter users based on language-independent features extracted from the text of the users’ tweets. The experiments were performed on a version of TwiSty dataset including tweets written by the users of six different languages: Portuguese, French, Dutch, English, German, and Italian. Logistic regression (LR), and feed-forward neural networks (FFNN) with back-propagation were used to build models in two different settings: Inter-Lingual (IL) and Cross-Lingual (CL). In the IL setting, the training and testing were performed on the same language whereas in the CL, Italian and German datasets were set aside and only used as test sets and the rest were combined to compose training and development sets. In the IL, the highest accuracy score belongs to LR whereas, in the CL, FFNN with three hidden layers yields the highest score. The results show that neural network based models underperform traditional models when the size of the training set is small; however, they beat traditional models by a non-trivial margin, when they are fed with large enough data. Finally, the feature analysis confirms that men and women have different writing styles independent of their language.
Finding that explicitly modeling structures leads to better generalization, we consider the task of predicting Cantonese pronunciations of logographs (Chinese characters) using logographs’ recursive structures. This task is a suitable case study for two reasons. First, logographs’ pronunciations depend on structures (i.e. the hierarchies of sub-units in logographs) Second, the quality of logographic structures is consistent since the structures are constructed automatically using a set of rules. Thus, this task is less affected by confounds such as varying quality between annotators. Empirical results show that modeling structures explicitly using treeLSTM outperforms LSTM baseline, reducing prediction error by 6.0% relative.
Unlike major Western languages, most African languages are very low-resourced. Furthermore, the resources that do exist are often scattered and difficult to obtain and discover. As a result, the data and code for existing research has rarely been shared, meaning researchers struggle to reproduce reported results, and almost no publicly available benchmarks or leaderboards for African machine translation models exist. To start to address these problems, we trained neural machine translation models for a subset of Southern African languages on publicly-available datasets. We provide the code for training the models and evaluate the models on a newly released evaluation set, with the aim of starting a leaderboard for Southern African languages and spur future research in the field.
We present initial experiments to evaluate the performance of tasks such as Part of Speech Tagging on data corrupted by Optical Character Recognition (OCR). Our results, based on English and German data, using artificial experiments as well as initial real OCRed data indicate that already a small drop in OCR quality considerably increases the error rates, which would have a significant impact on subsequent processing steps.
Kurdish is a less-resourced language consisting of different dialects written in various scripts. Approximately 30 million people in different countries speak the language. The lack of corpora is one of the main obstacles in Kurdish language processing. In this paper, we present KTC-the Kurdish Textbooks Corpus, which is composed of 31 K-12 textbooks in Sorani dialect. The corpus is normalized and categorized into 12 educational subjects containing 693,800 tokens (110,297 types). Our resource is publicly available for non-commercial use under the CC BY-NC-SA 4.0 license.
A broad range of information needs can often be stated as a question. Question Answering (QA) systems attempt to provide users concise answer(s) to natural language questions. The existing Amharic QA systems handle fact-based questions that usually take named entities as an answer. To deal with more complex information needs we developed an Amharic non-factoid QA for biography, definition, and description questions. A hybrid approach has been used for the question classification. For document filtering and answer extraction we have used lexical patterns. On the other hand to answer biography questions we have used a summarizer and the generated summary is validated using a text classifier. Our QA system is evaluated and has shown a promising result.
Polysemous Language in Child Directed Speech Learning the meaning of words is one of the fundamental building blocks of verbal communication. Models of child language acquisition have generally made the simplifying assumption that each word appears in child-directed speech with a single meaning. To understand naturalistic word learning during childhood, it is essential to know whether children hear input that is in fact constrained to single meaning per word, or whether the environment naturally contains multiple senses.In this study, we use a topic modeling approach to automatically induce word senses from child-directed speech. Our results confirm the plausibility of our automated analysis approach and reveal an increasing rate of using multiple senses in child-directed speech, starting with corpora from children as early as the first year of life.
We critique recent work on ethics in natural language processing. Those discussions have focused on data collection, experimental design, and interventions in modeling. But we argue that we ought to first understand the frameworks of ethics that are being used to evaluate the fairness and justice of algorithmic systems. Here, we begin that discussion by outlining deontological and consequentialist ethics, and make predictions on the research agenda prioritized by each.
[Multiple-submission] In the midst of a generation widely exposed to and influenced by media entertainment, the NLP research community has shown relatively little attention on the sexist comments in popular TV series. To understand sexism in TV series, we propose a way of collecting distant supervision dataset using Character Persona information with the psychological theories on sexism. We assume that sexist characters from TV shows are more prone to making sexist comments when talking about women, and show that this hypothesis is valid through experiment. Finally, we conduct an interesting analysis on popular TV show characters and successfully identify different shades of sexism that is often overlooked.
People judge pairwise similarity by deciding which aspects of the words’ meanings are relevant for the comparison of the given pair. However, computational representations of meaning rely on dimensions of the vector representation for similarity comparisons, without considering the specific pairing at hand. Prior work has adapted computational similarity judgments by using the softmax function in order to address this limitation by capturing asymmetry in human judgments. We extend this analysis by showing that a simple modification of cosine similarity offers a better correlation with human judgments over a comprehensive dataset. The modification performs best when the similarity between two words is calculated with reference to other words that are most similar and dissimilar to the pair.
Detecting abusive language is a significant research topic, which has received a lot of attention recently. Our work focused on detecting personal attacks in online conversations. State-of-the-art research on this task has largely used deep learning with word embeddings. We explored the use of sentiment lexicons as well as semantic lexicons towards improving the accuracy of the baseline Convolutional Neural Network (CNN) using regular word embeddings. This is a work in progress, limited by time constraints and appropriate infrastructure. Our preliminary results showed promise for utilizing lexicons, especially semantic lexicons, for the task of detecting abusive language.