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LREC 2020 Paper Dissemination (8/10)

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LREC 2020 was not held in Marseille this year and only the Proceedings were published.

The ELRA Board and the LREC 2020 Programme Committee now feel that those papers should be disseminated again, in a thematic-oriented way, shedding light on specific “topics/sessions”.

Packages with several sessions will be disseminated every Tuesday for 10 weeks, from Nov 10, 2020 until the end of January 2021.

Each session displays papers’ title and authors, with corresponding abstract (for ease of reading) and url, in like manner as the Book of Abstracts we used to print and distribute at LRECs.

We hope that you discover interesting, even exciting, work that may be useful for your own research.

Group of papers sent on January 12, 2021

Links to each session

 

Parsing, Grammar, Syntax, Treebank

Syntax and Semantics in a Treebank for Esperanto

Eckhard Bick

In this paper we describe and evaluate syntactic and semantic aspects of Arbobanko, a treebank for the artificial language Esperanto, as well as tools and methods used in the production of the treebank. In addition to classical morphosyntax and dependency structure, the treebank was enriched with a lexical-semantic layer covering named entities, a semantic type ontology for nouns and adjectives and a framenet-inspired semantic classification of verbs. For an under-resourced language, the quality of automatic syntactic and semantic pre-annotation is of obvious importance, and by evaluating the underlying parser and the coverage of its semantic ontologies, we try to answer the question whether the language's extremely regular morphology and transparent semantic affixes translate into a more regular syntax and higher parsing accuracy. On the linguistic side, the treebank allows us to address and quantify typological issues such as the question of word order, auxiliary constructions, lexical transparency and semantic type ambiguity in Esperanto.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.630.pdf

 

Implementation and Evaluation of an LFG-based Parser for Wolof

Cheikh M. Bamba Dione

This paper reports on a parsing system for Wolof based on the LFG formalism. The parser covers core constructions of Wolof, including noun classes, cleft, copula, causative and applicative sentences. It also deals with several types of coordination, including same constituent coordination, asymmetric and asyndetic coordination. The system uses a cascade of finite-state transducers for word tokenization and morphological analysis as well as various lexicons. In addition, robust parsing techniques, including fragmenting and skimming, are used to optimize grammar coverage. Parsing coverage is evaluated by running test-suites of naturally occurring Wolof sentences through the parser. The evaluation of parsing coverage reveals that 72.72% of the test sentences receive full parses; 27.27% receive partial parses. To measure accuracy, the parsed sentences are disambiguated manually using an incremental parsebanking approach based on discriminants. The evaluation of parsing quality reveals that the parser achieves 67.2% recall, 92.8% precision and an f-score of 77.9%.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.631.pdf

 

The Treebank of Vedic Sanskrit

Oliver Hellwig, Salvatore Scarlata, Elia Ackermann and Paul Widmer

This paper introduces the first treebank of Vedic Sanskrit, a morphologically rich ancient Indian language that is of central importance for linguistic and historical research. The selection of the more than 3,700 sentences contained in this treebank reflects the development of metrical and prose texts over a period of 600 years. We discuss how these sentences are annotated in the Universal Dependencies scheme and which syntactic constructions required special attention. In addition, we describe a syntactic labeler based on neural networks that supports the initial annotation of the treebank, and whose evaluation can be helpful for setting up a full syntactic parser of Vedic Sanskrit.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.632.pdf

 

Inherent Dependency Displacement Bias of Transition-Based Algorithms

Mark Anderson and Carlos Gómez-Rodríguez

A wide variety of transition-based algorithms are currently used for dependency parsers. Empirical studies have shown that performance varies across different treebanks in such a way that one algorithm outperforms another on one treebank and the reverse is true for a different treebank. There is often no discernible reason for what causes one algorithm to be more suitable for a certain treebank and less so for another. In this paper we shed some light on this by introducing the concept of an algorithm's inherent dependency displacement distribution. This  characterises the bias of the algorithm in terms of dependency displacement, which quantify both distance and direction of syntactic relations. We show that the similarity of an algorithm's inherent distribution to a treebank's displacement distribution is clearly correlated to the algorithm's parsing performance on that treebank, specificially with highly significant and substantial correlations for the predominant sentence lengths in Universal Dependency treebanks. We also obtain results which show a more discrete analysis of dependency displacement does not result in any meaningful correlations.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.633.pdf

 

A Gold Standard Dependency Treebank for Turkish

Tolga Kayadelen, Adnan Ozturel and Bernd Bohnet

We introduce TWT; a new treebank for Turkish which consists of web and Wikipedia sentences that are annotated for segmentation, morphology, part-of-speech and dependency relations. To date, it is the largest publicly available human-annotated morpho-syntactic Turkish treebank in terms of the annotated word count. It is also the first large Turkish dependency treebank that has a dedicated Wikipedia section. We present the tagsets and the methodology that are used in annotating the treebank and also the results of the baseline experiments on Turkish dependency parsing with this treebank.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.634.pdf

 

Chunk Different Kind of Spoken Discourse: Challenges for Machine Learning

Iris Eshkol-Taravella, Mariame Maarouf, Flora Badin, Marie Skrovec and Isabelle Tellier

This paper describes the development of a chunker for spoken data by supervised machine learning using the CRFs, based on a small reference corpus composed of two kinds of discourse: prepared monologue vs. spontaneous talk in interaction. The methodology considers the specific character of the spoken data. The machine learning uses the results of several available taggers, without correcting the results manually. Experiments show that the discourse type (monologue vs. free talk), the speech nature (spontaneous vs. prepared) and the corpus size can influence the results of the machine learning process and must be considered while interpreting the results.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.635.pdf

 

GRAIN-S: Manually Annotated Syntax for German Interviews

Agnieszka Falenska, Zoltán Czesznak, Kerstin Jung, Moritz Völkel, Wolfgang Seeker and Jonas Kuhn

We present GRAIN-S, a set of manually created syntactic annotations for radio interviews in German. The dataset extends an existing corpus GRAIN and comes with constituency and dependency trees for six interviews. The rare combination of gold- and silver-standard annotation layers coming from GRAIN with high-quality syntax trees can serve as a useful resource for speech- and text-based research. Moreover, since interviews can be put between carefully prepared speech and spontaneous conversational speech, they cover phenomena not seen in traditional newspaper-based treebanks. Therefore, GRAIN-S can contribute to research into techniques for model adaptation and for building more corpus-independent tools. GRAIN-S follows TIGER, one of the established syntactic treebanks of German. We describe the annotation process and discuss decisions necessary to adapt the original TIGER guidelines to the interviews domain. Next, we give details on the conversion from TIGER-style trees to dependency trees. We provide data statistics and demonstrate differences between the new dataset and existing out-of-domain test sets annotated with TIGER syntactic structures. Finally, we provide baseline parsing results for further comparison.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.636.pdf

 

Yorùbá Dependency Treebank (YTB)

Olájídé Ishola and Daniel Zeman

Low-resource languages present enormous NLP opportunities as well as varying degrees of difficulties. The newly released treebank of hand-annotated parts of the Yoruba Bible provides an avenue for dependency analysis of the Yoruba language; the application of a new grammar formalism to the language. In this paper, we discuss our choice of Universal Dependencies, important dependency annotation decisions considered in the creation of the first annotation guidelines for Yoruba and results of our parsing experiments. We also lay the foundation for future incorporation of other domains with the initial test on

Yoruba Wikipedia articles and highlighted future directions for the rapid expansion of the treebank.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.637.pdf

 

English Recipe Flow Graph Corpus

Yoko Yamakata, Shinsuke Mori and John Carroll

We present an annotated corpus of English cooking recipe procedures, and describe and evaluate computational methods for learning these annotations. The corpus consists of 300 recipes written by members of the public, which we have annotated with domain-specific linguistic and semantic structure. Each recipe is annotated with (1) `recipe named entities' (r-NEs) specific to the recipe domain, and (2) a flow graph representing in detail the sequencing of steps, and interactions between cooking tools, food ingredients and the products of intermediate steps. For these two kinds of annotations, inter-annotator agreement ranges from 82.3 to 90.5 F1, indicating that our annotation scheme is appropriate and consistent. We experiment with producing these annotations automatically. For r-NE tagging we train a deep neural network NER tool; to compute flow graphs we train a dependency-style parsing procedure which we apply to the entire sequence of r-NEs in a recipe.In evaluations, our systems achieve 71.1 to 87.5 F1, demonstrating that our annotation scheme is learnable.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.638.pdf

 

Development of a General-Purpose Categorial Grammar Treebank

Yusuke Kubota, Koji Mineshima, Noritsugu Hayashi and Shinya Okano

This paper introduces ABC Treebank, a general-purpose categorial grammar (CG) treebank for Japanese. It is 'general-purpose' in the sense that it is not tailored to a specific variant of CG, but rather aims to offer a theory-neutral linguistic resource (as much as possible) which can be converted to different versions of CG (specifically, CCG and Type-Logical Grammar) relatively easily. In terms of linguistic analysis, it improves over the existing Japanese CG treebank (Japanese CCGBank) on the treatment of certain linguistic phenomena (passives, causatives, and control/raising predicates) for which the lexical specification of the syntactic information reflecting local dependencies turns out to be crucial. In this paper, we describe the underlying 'theory' dubbed ABC Grammar that is taken as a basis for our treebank, outline the general construction of the corpus, and report on some preliminary results applying the treebank in a semantic parsing system for generating logical representations of sentences.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.639.pdf

 

Dependency Parsing for Urdu: Resources, Conversions and Learning

Toqeer Ehsan and Miriam Butt

This paper adds to the available resources for the under-resourced language Urdu by converting different types of existing treebanks for Urdu into a common format that is based on Universal Dependencies. We present comparative results for training two dependency parsers, the MaltParser and a transition-based BiLSTM parser on this new resource. The BiLSTM parser incorporates word embeddings which improve the parsing results  significantly. The BiLSTM parser outperforms the MaltParser with a UAS of 89.6 and an LAS of 84.2 with respect to our standardized treebank resource.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.640.pdf

 

Prague Dependency Treebank - Consolidated 1.0

Jan Hajic, Eduard Bejček, Jaroslava Hlavacova, Marie Mikulová, Milan Straka, Jan Štěpánek and Barbora Štěpánková

We present a richly annotated and genre-diversified language resource, the Prague Dependency Treebank-Consolidated 1.0 (PDT-C 1.0), the purpose of which is - as it always been the case for the family of the Prague Dependency Treebanks - to serve both as a training data for various types of NLP tasks as well as for linguistically-oriented research. PDT-C 1.0 contains four different datasets of Czech, uniformly annotated using the standard PDT scheme (albeit not everything is annotated manually, as we describe in detail here). The texts come from different sources: daily newspaper articles, Czech translation of the Wall Street Journal, transcribed dialogs and a small amount of user-generated, short, often non-standard language segments typed into a web translator. Altogether, the treebank contains around 180,000 sentences with their morphological, surface and deep syntactic annotation. The diversity of the texts and annotations should serve well the NLP applications as well as it is an invaluable resource for linguistic research, including comparative studies regarding texts of different genres. The corpus is publicly and freely available.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.641.pdf

 

Training a Swedish Constituency Parser on Six Incompatible Treebanks

Richard Johansson and Yvonne Adesam

We investigate a transition-based parser that uses Eukalyptus, a function-tagged constituent treebank for Swedish which includes discontinuous constituents. In addition, we show that the accuracy of this parser can be improved by using a multitask learning architecture that makes it possible to train the parser on additional treebanks that use other annotation models.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.642.pdf

 

Parsing as Tagging

Robert Vacareanu, George Caique Gouveia Barbosa, Marco A. Valenzuela-Escárcega and Mihai Surdeanu

We propose a simple yet accurate method for dependency parsing that treats parsing as tagging (PaT). That is, our approach addresses the parsing of dependency trees with a sequence model implemented with a bidirectional LSTM over BERT embeddings, where the “tag” to be predicted at each token position is the relative position of the corresponding head. For example, for the sentence John eats cake, the tag to be predicted for the token cake is -1 because its head (eats) occurs one token to the left. Despite its simplicity, our approach performs well. For example, our approach outperforms the state-of-the-art method of (Fernández-González and Gómez-Rodrı́guez, 2019) on Universal Dependencies (UD) by 1.76% unlabeled attachment score (UAS) for English, 1.98% UAS for French, and 1.16% UAS for German. On average, on 12 UD languages, our method with minimal tuning performs comparably with this state-of-the-art approach: better by 0.11% UAS, and worse by 0.58% LAS.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.643.pdf

 

The EDGeS Diachronic Bible Corpus

Gerlof Bouma, Evie Coussé, Trude Dijkstra and Nicoline van der Sijs

We present the EDGeS Diachronic Bible Corpus: a diachronically and synchronically parallel corpus of Bible translations in Dutch, English, German and Swedish, with texts from the 14th century until today. It is compiled in the context of an intended longitudinal and contrastive study of complex verb constructions in Germanic. The paper discusses the corpus design principles, its selection of 36 Bibles, and the information and metadata encoded for the corpus texts. The EDGeS corpus will be available in two forms: the whole corpus will be accessible for researchers behind a login in the well-known OPUS search infrastructure, and the open subpart of the corpus will be available for download.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.644.pdf

 

Treebanking User-Generated Content: A Proposal for a Unified Representation in Universal Dependencies

Manuela Sanguinetti, Cristina Bosco, Lauren Cassidy, Özlem Çetinoğlu, Alessandra Teresa Cignarella, Teresa Lynn, Ines Rehbein, Josef Ruppenhofer, Djamé Seddah and Amir Zeldes

The paper presents a discussion on the main linguistic phenomena of user-generated texts found in web and social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this paper is twofold: (1) to provide a short, though comprehensive, overview of such treebanks - based on available literature - along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The main goal of this paper is to provide a common framework for those teams interested in developing similar resources in UD, thus enabling cross-linguistic consistency, which is a principle that has always been in the spirit of UD.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.645.pdf

 

A Diachronic Treebank of Russian Spanning More Than a Thousand Years

Aleksandrs Berdicevskis and Hanne Eckhoff

We describe the Tromsø Old Russian and Old Church Slavonic Treebank (TOROT) that spans from the earliest Old Church Slavonic to modern Russian texts, covering more than a thousand years of continuous language history. We focus on the latest additions to the treebank, first of all, the modern subcorpus that was created by a high-quality conversion of the existing treebank of contemporary standard Russian (SynTagRus).

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.646.pdf

 

ÆTHEL: Automatically Extracted Typelogical Derivations for Dutch

Konstantinos Kogkalidis, Michael Moortgat and Richard Moot

We present ÆTHEL, a semantic compositionality dataset for written Dutch. ÆTHEL consists of two parts. First, it contains a lexicon of supertags for about 900 000 words in context. The supertags correspond to types of the simply typed linear lambda-calculus, enhanced with dependency decorations that capture grammatical roles supplementary to function-argument structures. On the basis of these types, ÆTHEL further provides 72 192 validated derivations, presented in four formats: natural-deduction and sequent-style proofs, linear logic proofnets and the associated programs (lambda terms) for meaning composition. ÆTHEL's types and derivations are obtained by means of an extraction algorithm applied to the syntactic analyses of LASSY Small, the gold standard corpus of written Dutch. We discuss the extraction algorithm and show how `virtual elements' in the original LASSY annotation of unbounded dependencies and coordination phenomena give rise to higher-order types. We suggest some example usecases highlighting the benefits of a type-driven approach at the syntax semantics interface. The following resources are open-sourced with ÆTHEL: the lexical mappings between words and types, a subset of the dataset consisting of 7 924 semantic parses, and the Python code that implements the extraction algorithm.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.647.pdf

 

GUMBY – A Free, Balanced, and Rich English Web Corpus

Luke Gessler, Siyao Peng, Yang Liu, Yilun Zhu, Shabnam Behzad and Amir Zeldes

We present a freely available, genre-balanced English web corpus totaling 4M tokens and featuring a large number of high-quality automatic annotation layers, including dependency trees, non-named entity annotations, coreference resolution, and discourse trees in Rhetorical Structure Theory. By tapping open online data sources the corpus is meant to offer a more sizable alternative to smaller manually created annotated data sets, while avoiding pitfalls such as imbalanced or unknown composition, licensing problems, and low-quality natural language processing. We harness knowledge from multiple annotation layers in order to achieve a "better than NLP" benchmark and evaluate the accuracy of the resulting resource.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.648.pdf

 

Typical Sentences as a Resource for Valence

Uwe Quasthoff, Lars Hellan, Erik Körner, Thomas Eckart, Dirk Goldhahn and Dorothee Beermann

Verb valence information can be derived from corpora by using subcorpora of typical sentences that are constructed in a language independent manner based on frequent POS structures. The inspection of typical sentences with a fixed verb in a certain position can show the valence information directly. Using verb fingerprints, consisting of the most typical sentence patterns the verb appears in, we are able to identify standard valence patterns and compare them against a language's valence profile. With a very limited number of training data per language, valence information for other verbs can be derived as well. Based on the Norwegian valence patterns we are able to find comparative patterns in German where typical sentences are able to express the same situation in an equivalent way and can so construct verb valence pairs for a bilingual PolyVal dictionary. This contribution discusses this application with a focus on the Norwegian valence dictionary NorVal.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.649.pdf

 

Recognizing Sentence-level Logical Document Structures with the Help of Context-free Grammars

Jonathan Hildebrand, Wahed Hemati and Alexander Mehler

Current sentence boundary detectors split documents into sequentially ordered sentences by detecting their beginnings and ends. Sentences, however, are more deeply structured even on this side of constituent and dependency structure: they can consist of a main sentence and several subordinate clauses as well as further segments (e.g. inserts in parentheses); they can even recursively embed whole sentences and then contain multiple sentence beginnings and ends. In this paper, we introduce a tool that segments sentences into tree structures to detect this type of recursive structure. To this end, we retrain different constituency parsers with the help of modified training data to transform them into sentence segmenters. With these segmenters, documents are mapped to sequences of sentence-related “logical document structures”. The resulting segmenters aim to improve downstream tasks by providing additional structural information. In this context, we experiment with German dependency parsing. We show that for certain sentence categories, which can be determined automatically, improvements in German dependency parsing can be achieved using our segmenter for preprocessing. The assumption suggests that improvements in other languages and tasks can be achieved.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.650.pdf

 

When Collaborative Treebank Curation Meets Graph Grammars

Gaël Guibon, Marine Courtin, Kim Gerdes and Bruno Guillaume

In this paper we present Arborator-Grew, a collaborative annotation tool for treebank development. Arborator-Grew combines the features of two preexisting tools: Arborator and Grew. Arborator is a widely used collaborative graphical online dependency treebank annotation tool. Grew is a tool for graph querying and rewriting specialized in structures needed in NLP, i.e. syntactic and semantic dependency trees and graphs. Grew also has an online version, Grew-match, where all Universal Dependencies treebanks in their classical, deep and surface-syntactic flavors can be queried. Arborator-Grew is a complete redevelopment and modernization of Arborator, replacing its own internal database storage by a new Grew API, which adds a powerful query tool to Arborator's existing treebank creation and correction features. This includes complex access control for parallel expert and crowd-sourced annotation, tree comparison visualization, and various exercise modes for teaching and training of annotators. Arborator-Grew opens up new paths of collectively creating, updating, maintaining, and curating syntactic treebanks and semantic graph banks.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.651.pdf

 

ODIL_Syntax: a Free Spontaneous Spoken French Treebank Annotated with Constituent Trees

Ilaine Wang, Aurore Pelletier, Jean-Yves Antoine and Anaïs Halftermeyer

This paper describes ODIL Syntax, a French treebank built on spontaneous speech transcripts. The syntactic structure of every speech turn is represented by constituent trees, through a procedure which combines an automatic annotation provided by a parser (here, the Stanford Parser) and a manual revision. ODIL Syntax respects the annotation scheme designed for the French TreeBank (FTB), with the addition of some annotation guidelines that aims at representing specific features of the spoken language such as speech disfluencies. The corpus will be freely distributed by January 2020 under a Creative Commons licence. It will ground a further semantic enrichment dedicated to the representation of temporal entities and temporal relations, as a second phase of the ODIL@Temporal project. The paper details the annotation scheme we followed with a emphasis on the representation of speech disfluencies. We then present the annotation procedure that was carried out on the Contemplata annotation platform. In the last section, we provide some distributional characteristics of the annotated corpus (POS distribution, multiword expressions).

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.652.pdf

 

Towards the Conversion of National Corpus of Polish to Universal Dependencies

Alina Wróblewska

The research presented in this paper aims at enriching the manually morphosyntactically annotated part of National Corpus of Polish (NKJP1M) with a syntactic layer, i.e. dependency trees of sentences, and at converting both dependency trees and morphosyntactic annotations of particular tokens to Universal Dependencies. The dependency layer is built using a semi-automatic annotation procedure. The sentences from NKJP1M are first parsed with a dependency parser trained on Polish Dependency Bank, i.e. the largest bank of Polish dependency trees. The predicted dependency trees and the morphosyntactic annotations of tokens are then automatically converted into UD dependency graphs. NKJP1M sentences are an essential part of Polish Dependency Bank, we thus replace some automatically predicted dependency trees with their manually annotated equivalents. The final dependency treebank consists of 86K trees (including 15K gold-standard trees). A natural language pre-processing model trained on the enlarged set of (possibly noisy) dependency trees outperforms a model trained on a smaller set of the gold-standard trees in predicting part-of-speech tags, morphological features, lemmata, and labelled dependency trees

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.653.pdf

 

Phonetic Databases, Phonology

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SegBo: A Database of Borrowed Sounds in the World’s Language

Eitan Grossman, Elad Eisen, Dmitry Nikolaev and Steven Moran

Phonological segment borrowing is a process through which languages acquire new contrastive speech sounds as the result of borrowing new words from other languages. Despite the fact that phonological segment borrowing is documented in many of the world’s languages, to date there has been no large-scale quantitative study of the phenomenon. In this paper, we present SegBo, a novel cross-linguistic database of borrowed phonological segments. We describe our data aggregation pipeline and the resulting language sample.  We also present two short case studies based on the database. The first deals with the impact of large colonial languages on the sound systems of the world’s languages; the second deals with universals of borrowing in the domain of rhotic consonants.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.654.pdf

 

Developing Resources for Automated Speech Processing of Quebec French

Mélanie Lancien, Marie-Hélène Côté and Brigitte Bigi

The analysis of the structure of speech nearly always rests on the alignment of the speech recording with a phonetic transcription. Nowadays several tools can perform this speech segmentation automatically. However, none of them allows the automatic segmentation of Quebec French (QF hereafter), the acoustics and phonotactics of QF differing widely from that of France French (FF hereafter). To adequately segment QF, features like diphthongization of long vowels and affrication of coronal stops have to be taken into account. Thus acoustic models for automatic segmentation must be trained on speech samples exhibiting those phenomena. Dictionaries and lexicons must also be adapted and integrate differences in lexical units and in the phonology of QF. This paper presents the development of linguistic resources to be included into SPPAS software tool in order to get Text normalization, Phonetization, Alignment and Syllabification. We adapted the existing French lexicon and developed a QF-specific pronunciation dictionary. We then created an acoustic model from the existing ones and adapted it with 5 minutes of manually time-aligned data. These new resources are all freely distributed with SPPAS version 2.7; they perform the full process of speech segmentation in Quebec French.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.655.pdf

 

AlloVera: A Multilingual Allophone Database

David R. Mortensen, Xinjian Li, Patrick Littell, Alexis Michaud, Shruti Rijhwani, Antonios Anastasopoulos, Alan W Black, Florian Metze and Graham Neubig

We introduce a new resource, AlloVera, which provides mappings from 218 allophones to phonemes for 14 languages. Phonemes are contrastive phonological units, and allophones are their various concrete realizations, which are predictable from phonological context. While phonemic representations are language specific, phonetic representations (stated in terms of (allo)phones) are much closer to a universal (language-independent) transcription. AlloVera allows the training of speech recognition models that output phonetic transcriptions in the International Phonetic Alphabet (IPA), regardless of the input language. We show that a “universal” allophone model, Allosaurus, built with AlloVera, outperforms “universal” phonemic models and language-specific models on a speech-transcription task. We explore the implications of this technology (and related technologies) for the documentation of endangered and minority languages. We further explore other applications for which AlloVera will be suitable as it grows, including phonological typology.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.656.pdf

 

Arabic Speech Rhythm Corpus: Read and Spontaneous Speaking Styles

Omnia Ibrahim, Homa Asadi, Eman Kassem and Volker Dellwo

Databases for studying speech rhythm and tempo exist for numerous languages. The present corpus was built to allow comparisons between Arabic speech rhythm and other languages. 10 Egyptian speakers (gender-balanced) produced speech in two different speaking styles (read and spontaneous). The design of the reading task replicates the methodology used in the creation of BonnTempo corpus (BTC). During the spontaneous task, speakers talked freely for more than one minute about their daily life and/or their studies, then they described the directions to come to the university from a famous near location using a map as a visual stimulus. For corpus annotation, the database has been manually and automatically time-labeled, which makes it feasible to perform a quantitative analysis of the rhythm of Arabic in both Modern Standard Arabic (MSA) and Egyptian dialect variety. The database serves as a phonetic resource, which allows researchers to examine various aspects of Arabic supra-segmental features and it can be used for forensic phonetic research, for comparison of different speakers, analyzing variability in different speaking styles, and automatic speech and speaker recognition.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.657.pdf

 

Comparing Methods for Measuring Dialect Similarity in Norwegian

Janne Johannessen, Andre Kåsen, Kristin Hagen, Anders Nøklestad and Joel Priestley

The present article presents four experiments with two different methods for measuring dialect similarity in Norwegian: the Levenshtein method and the neural long short term memory (LSTM)  autoencoder network, a machine learning algorithm. The visual output in the form of dialect maps is then compared with canonical maps found in the dialect literature. All of this enables us to say that one does not need fine-grained transcriptions of speech to replicate classical classification patterns.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.658.pdf

 

AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition

Afroz Ahamad, Ankit Anand and Pranesh Bhargava

Modern Automatic Speech Recognition (ASR) technology has evolved to identify the speech spoken by native speakers of a language very well. However, identification of the speech spoken by non-native speakers continues to be a major challenge for it. In this work, we first spell out the key requirements for creating a well-curated database of speech samples in non-native accents for training and testing robust ASR systems. We then introduce AccentDB, one such database that contains samples of 4 Indian-English accents collected by us, and a compilation of samples from 4 native-English, and a metropolitan Indian-English accent. We also present an analysis on separability of the collected accent data. Further, we present several accent classification models and evaluate them thoroughly against human-labelled accent classes. We test the generalization of our classifier models in a variety of setups of seen and unseen data. Finally, we introduce accent neutralization of non-native accents to native accents using autoencoder models with task-specific architectures. Thus, our work aims to aid ASR systems at every stage of development with a database for training, classification models for feature augmentation, and neutralization systems for acoustic transformations of non-native accents of English.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.659.pdf

 

Question Answering

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A Framework for Evaluation of Machine Reading Comprehension Gold Standards

Viktor Schlegel, Marco Valentino, Andre Freitas, Goran Nenadic and Riza Batista-Navarro

Machine Reading Comprehension (MRC) is the task of answering a question over a paragraph of text. While neural MRC systems gain popularity and achieve noticeable performance, issues are being raised with the methodology used to establish their performance, particularly concerning the data design of gold standards that are used to evaluate them. There is but a limited understanding of the challenges present in this data, which makes it hard to draw comparisons and formulate reliable hypotheses. As a first step towards alleviating the problem, this paper proposes a unifying framework to systematically investigate the present linguistic features, required reasoning and background knowledge and factual correctness on one hand, and the presence of lexical cues as a lower bound for the requirement of understanding on the other hand. We propose a qualitative annotation schema for the first and a set of approximative metrics for the latter. In a first application of the framework, we analyse modern MRC gold standards and present our findings: the absence of features that contribute towards lexical ambiguity, the varying factual correctness of the expected answers and the presence of lexical cues, all of which potentially lower the reading comprehension complexity and quality of the evaluation data.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.660.pdf

 

Multi-class Hierarchical Question Classification for Multiple Choice Science Exams

Dongfang Xu, Peter Jansen, Jaycie Martin, Zhengnan Xie, Vikas Yadav, Harish Tayyar Madabushi, Oyvind Tafjord and Peter Clark

Prior work has demonstrated that question classification (QC), recognizing the problem domain of a question, can help answer it more accurately. However, developing strong QC algorithms has been hindered by the limited size and complexity of annotated data available. To address this, we present the largest challenge dataset for QC, containing 7,787 science exam questions paired with detailed classification labels from a fine-grained hierarchical taxonomy of 406 problem domains. We then show that a BERT-based model trained on this dataset achieves a large (+0.12 MAP) gain compared with previous methods, while also achieving state-of-the-art performance on benchmark open-domain and biomedical QC datasets. Finally, we show that using this model's predictions of question topic significantly improves the accuracy of a question answering system by +1.7% P@1, with substantial future gains possible as QC performance improves.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.661.pdf

 

Assessing Users’ Reputation from Syntactic and Semantic Information in Community Question Answering

Yonas Woldemariam

Textual content is the most significant as well as substantially the big part of CQA (Community Question Answering) forums. Users gain reputation for contributing such content. Although linguistic quality is the very essence of textual information, that does not seem to be considered in estimating users’ reputation. As existing users’ reputation systems seem to solely rely on vote counting, adding that bit of linguistic information surely improves their quality. In this study, we investigate the relationship between users’ reputation and linguistic features extracted from their associated answers content. And we build statistical models on a Stack Overflow dataset that learn reputation from complex syntactic and semantic structures of such content. The resulting models reveal how users’ writing styles in answering questions play important roles in building reputation points. In our experiments, extracting answers from systematically selected users followed by linguistic features annotation and models building. The models are evaluated on in-domain (e.g., Server Fault, Super User) and out-domain (e.g., English, Maths) datasets. We found out that the selected linguistic features have quite significant influences over reputation scores. In the best case scenario, the selected linguistic feature set could explain 80% variation in reputation scores with the prediction error of 3%. The performance results obtained from the baseline models have been significantly improved by adding syntactic and punctuation marks features.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.662.pdf

 

Unsupervised Domain Adaptation of Language Models for Reading Comprehension

Kosuke Nishida, Kyosuke Nishida, Itsumi Saito, Hisako Asano and Junji Tomita

This study tackles unsupervised domain adaptation of reading comprehension (UDARC). Reading comprehension (RC) is a task to learn the capability for question answering with textual sources. State-of-the-art models on RC still do not have general linguistic intelligence; i.e., their accuracy worsens for out-domain datasets that are not used in the training. We hypothesize that this discrepancy is caused by a lack of the language modeling (LM) capability for the out-domain. The UDARC task allows models to use supervised RC training data in the source domain and only unlabeled passages in the target domain. To solve the UDARC problem, we provide two domain adaptation models. The first one learns the out-domain LM and in-domain RC task sequentially. The second one is the proposed model that uses a multi-task learning approach of LM and RC. The models can retain both the RC capability acquired from the supervised data in the source domain and the LM capability from the unlabeled data in the target domain. We evaluated the models on UDARC with five datasets in different domains. The models outperformed the model without domain adaptation. In particular, the proposed model yielded an improvement of 4.3/4.2 points in EM/F1 in an unseen biomedical domain.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.663.pdf

 

Propagate-Selector: Detecting Supporting Sentences for Question Answering via Graph Neural Networks

Seunghyun Yoon, Franck Dernoncourt, Doo Soon Kim, Trung Bui and Kyomin Jung

In this study, we propose a novel graph neural network called propagate-selector (PS), which propagates information over sentences to understand information that cannot be inferred when considering sentences in isolation. First, we design a graph structure in which each node represents an individual sentence, and some pairs of nodes are selectively connected based on the text structure. Then, we develop an iterative attentive aggregation and a skip-combine method in which a node interacts with its neighborhood nodes to accumulate the necessary information. To evaluate the performance of the proposed approaches, we conduct experiments with the standard HotpotQA dataset. The empirical results demonstrate the superiority of our proposed approach, which obtains the best performances, compared to the widely used answer-selection models that do not consider the intersentential relationship.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.664.pdf

 

An Empirical Comparison of Question Classification Methods for Question Answering Systems

Eduardo Cortes, Vinicius Woloszyn, Arne Binder, Tilo Himmelsbach, Dante Barone and Sebastian Möller

Question classification is an important component of Question Answering Systems responsible for identifying the type of an answer a particular question requires. For instance, ``Who is the prime minister of the United Kingdom?" demands a name of a PERSON, while ``When was the queen of the United Kingdom born?" entails a DATE. This work makes an extensible review of the most recent methods for Question Classification, taking into consideration their applicability in low-resourced languages. First, we propose a manual classification of the current state-of-the-art methods in four distinct categories: low, medium, high, and very high level of dependency on external resources. Second, we applied this categorization in an empirical comparison in terms of the amount of data necessary for training and performance in different languages. In addition to complementing earlier works in this field, our study shows a boost on methods relying on recent language models, overcoming methods not suitable for low-resourced languages.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.665.pdf

 

Cross-sentence Pre-trained Model for Interactive QA matching

Jinmeng Wu and Yanbin Hao

Semantic matching measures the dependencies between query and answer representations, it is an important criterion for evaluating whether the matching is successful. In fact, such matching does not examine each sentence individually, context information outside a sentence should be considered equally important to the syntactic context inside a sentence.  We proposed a new QA matching model, built upon a cross-sentence context-aware architecture. An interactive attention mechanism with a pre-trained language model is proposed to automatically select salient positional answer representations that contribute more significantly to the answer relevance of a given question. In addition to the context information captured at each word position, we incorporate a new quantity of context information jump to facilitate the attention weight formulation. This reflects the amount of new information brought by the next word and is computed by modeling the joint probability between two adjacent word states. The proposed method is compared to multiple state-of-the-art ones evaluated using the TREC library, WikiQA, and the Yahoo! community question datasets. Experimental results show that the proposed method outperforms satisfactorily the competing ones.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.666.pdf

 

SQuAD2-CR: Semi-supervised Annotation for Cause and Rationales for Unanswerability in SQuAD 2.0

Gyeongbok Lee, Seung-won Hwang and Hyunsouk Cho

Existing machine reading comprehension models are reported to be brittle for adversarially perturbed questions when optimizing only for accuracy, which led to the creation of new reading comprehension benchmarks, such as SQuAD 2.0 which contains such type of questions. However, despite the super-human accuracy of existing models on such datasets, it is still unclear how the model predicts the answerability of the question, potentially due to the absence of a shared annotation for the explanation. To address such absence, we release SQuAD2-CR dataset, which contains annotations on unanswerable questions from the SQuAD 2.0 dataset, to enable an explanatory analysis of the model prediction. Specifically, we annotate (1) explanation on why the most plausible answer span cannot be the answer and (2) which part of the question causes unanswerability. We share intuitions and experimental results that how this dataset can be used to analyze and improve the interpretability of existing reading comprehension model behavior.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.667.pdf

 

Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs

Takashi Kodama, Ryuichiro Higashinaka, Koh Mitsuda, Ryo Masumura, Yushi Aono, Ryuta Nakamura, Noritake Adachi and Hidetoshi Kawabata

This paper concerns the problem of realizing consistent personalities in neural conversational modeling by using user generated question-answer pairs as training data. Using the framework of role play-based question answering, we collected single-turn question-answer pairs for particular characters from online users. Meta information was also collected such as emotion and intimacy related to question-answer pairs. We verified the quality of the collected data and, by subjective evaluation, we also verified their usefulness in training neural conversational models for generating utterances reflecting the meta information, especially emotion.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.668.pdf

 

AIA-BDE: A Corpus of FAQs in Portuguese and their Variations

Hugo Gonçalo Oliveira, João Ferreira, José Santos, Pedro Fialho, Ricardo Rodrigues, Luisa Coheur and Ana Alves

We present AIA-BDE, a corpus of 380 domain-oriented FAQs in Portuguese and their variations, i.e., paraphrases or entailed questions, created manually, by humans, or automatically, with Google Translate. Its aims to be used as a benchmark for FAQ retrieval and automatic question-answering, but may be useful in other contexts, such as the development of task-oriented dialogue systems, or models for natural language inference in an interrogative context. We also report on two experiments. Matching variations with their original questions was not trivial with a set of unsupervised baselines, especially for manually created variations. Besides high performances obtained with ELMo and BERT embeddings, an Information Retrieval system was surprisingly competitive when considering only the first hit. In the second experiment, text classifiers were trained with the original questions, and tested when assigning each variation to one of three possible sources, or assigning them as out-of-domain. Here, the difference between manual and automatic variations was not so significant.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.669.pdf

 

TutorialVQA: Question Answering Dataset for Tutorial Videos

Anthony Colas, Seokhwan Kim, Franck Dernoncourt, Siddhesh Gupte, Zhe Wang and Doo Soon Kim

Despite the number of currently available datasets on video-question answering, there still remains a need for a dataset involving multi-step and non-factoid answers. Moreover, relying on video transcripts remains an under-explored topic. To adequately address this, we propose a new question answering task on instructional videos, because of their verbose and narrative nature. While previous studies on video question answering have focused on generating a short text as an answer, given a question and video clip, our task aims to identify a span of a video segment as an answer which contains instructional details with various granularities. This work focuses on screencast tutorial videos pertaining to an image editing program. We introduce a dataset, TutorialVQA, consisting of about 6,000 manually collected triples of (video, question, answer span). We also provide experimental results with several baseline algorithms using the video transcripts. The results indicate that the task is challenging and call for the investigation of new algorithms.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.670.pdf

 

WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference

Zhengnan Xie, Sebastian Thiem, Jaycie Martin, Elizabeth Wainwright, Steven Marmorstein and Peter Jansen

Explainable question answering for complex questions often requires combining large numbers of facts to answer a question while providing a human-readable explanation for the answer, a process known as multi-hop inference.  Standardized science questions require combining an average of 6 facts, and as many as 16 facts, in order to answer and explain, but most existing datasets for multi-hop reasoning focus on combining only two facts, significantly limiting the ability of multi-hop inference algorithms to learn to generate large inferences.  In this work we present the second iteration of the WorldTree project, a corpus of 5,114 standardized science exam questions paired with large detailed multi-fact explanations that combine core scientific knowledge and world knowledge.  Each explanation is represented as a lexically-connected "explanation graph" that combines an average of 6 facts drawn from a semi-structured knowledge base of 9,216 facts across 66 tables.  We use this explanation corpus to author a set of 344 high-level science domain inference patterns similar to semantic frames supporting multi-hop inference.  Together, these resources provide training data and instrumentation for developing many-fact multi-hop inference models for question answering.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.671.pdf

 

Chat or Learn: a Data-Driven Robust Question-Answering System

Gabriel Luthier and Andrei Popescu-Belis

We present a voice-based conversational agent which combines the robustness of chatbots and the utility of question answering (QA) systems.  Indeed, while data-driven chatbots are typically user-friendly but not goal-oriented, QA systems tend to perform poorly at chitchat.  The proposed chatbot relies on a controller which performs dialogue act classification and feeds user input either to a sequence-to-sequence chatbot or to a QA system.  The resulting chatbot is a spoken QA application for the Google Home smart speaker.  The system is endowed with general-domain knowledge from Wikipedia articles and uses coreference resolution to detect relatedness between questions.  We present our choices of data sets for training and testing the components, and present the experimental results that helped us optimize the parameters of the chatbot.  In particular, we discuss the appropriateness of using the SQuAD dataset for evaluating end-to-end QA, in the light of our system's behavior.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.672.pdf

 

Project PIAF: Building a Native French Question-Answering Dataset

Rachel Keraron, Guillaume Lancrenon, Mathilde Bras, Frédéric Allary, Gilles Moyse, Thomas Scialom, Edmundo-Pavel Soriano-Morales and Jacopo Staiano

Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.673.pdf

 

Cross-lingual and Cross-domain Evaluation of Machine Reading Comprehension with Squad and CALOR-Quest Corpora

Delphine Charlet, Geraldine Damnati, Frederic Bechet, gabriel marzinotto and Johannes Heinecke

Machine Reading received recently a lot of attention thanks to both the availability of very large corpora such as SQuAD or MS MARCO containing triplets (document, question, answer), and the introduction of Transformer Language Models such as BERT which obtain excellent results, even matching human performance according to the SQuAD leaderboard. One of the key features of Transformer Models is their ability to be jointly trained across multiple languages, using a shared subword vocabulary, leading to the construction of cross-lingual lexical representations. This feature has been used recently to perform zero-shot cross-lingual experiments where a multilingual BERT model fine-tuned on a machine reading comprehension task exclusively for English was directly applied to Chinese and French documents with interesting performance. In this paper we study the cross-language and cross-domain capabilities of BERT on a Machine Reading Comprehension task on two corpora: SQuAD and a new French Machine Reading dataset, called CALOR-QUEST. The semantic annotation available on CALOR-QUEST allows us to give a detailed analysis on the kinds of questions that are properly handled through the cross-language process. We will try to answer this question: which factor between language mismatch and domain mismatch has the strongest influence on the performances of a Machine Reading Comprehension task?

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.674.pdf

 

ScholarlyRead: A New Dataset for Scientific Article Reading Comprehension

Tanik Saikh, Asif Ekbal and Pushpak Bhattacharyya

We present ScholarlyRead, span-of-word-based scholarly articles’ Reading Comprehension (RC) dataset with approximately 10K manually checked passage-question-answer instances. ScholarlyRead was constructed in semi-automatic way. We consider the articles from two popular journals of a reputed publishing house. Firstly, we generate questions from these articles in an automatic way. Generated questions are then manually checked by the human annotators. We propose a baseline model based on Bi-Directional Attention Flow (BiDAF) network that yields the F1 score of 37.31%. The framework would be useful for building Question-Answering (QA) systems on scientific articles.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.675.pdf

 

Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task

Md Tahmid Rahman Laskar, Jimmy Xiangji Huang and Enamul Hoque

Word embeddings that consider context have attracted great attention for various natural language processing tasks in recent years. In this paper, we utilize contextualized word embeddings with the transformer encoder for sentence similarity modeling in the answer selection task. We present two different approaches (feature-based and fine-tuning-based) for answer selection. In the feature-based approach, we utilize two types of contextualized embeddings, namely the Embeddings from Language Models (ELMo) and the Bidirectional Encoder Representations from Transformers (BERT) and integrate each of them with the transformer encoder. We find that integrating these contextual embeddings with the transformer encoder is effective to improve the performance of sentence similarity modeling. In the second approach, we fine-tune two pre-trained transformer encoder models for the answer selection task. Based on our experiments on six datasets, we find that the fine-tuning approach outperforms the feature-based approach on all of them. Among our fine-tuning-based models, the Robustly Optimized BERT Pretraining Approach (RoBERTa) model results in new state-of-the-art performance across five datasets.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.676.pdf

 

Automatic Spanish Translation of SQuAD Dataset for Multi-lingual Question Answering

Casimiro Pio Carrino, Marta R. Costa-jussà and José A. R. Fonollosa

Recently, multilingual question answering became a crucial research topic, and it is receiving increased interest in the NLP community. However, the unavailability of large-scale datasets makes it challenging to train multilingual QA systems with performance comparable to the English ones. In this work, we develop the Translate Align Retrieve (TAR) method to automatically translate the Stanford Question Answering Dataset (SQuAD) v1.1 to Spanish. We then used this dataset to train Spanish QA systems by fine-tuning a Multilingual-BERT model. Finally, we evaluated our QA models with the recently proposed MLQA and XQuAD benchmarks for cross-lingual Extractive QA. Experimental results show that our models outperform the previous Multilingual-BERT baselines achieving the new state-of-the-art values of 68.1 F1 on the Spanish MLQA corpus and 77.6 F1 on the Spanish XQuAD corpus. The resulting, synthetically generated SQuAD-es v1.1 corpora, with almost 100% of data contained in the original English version, to the best of our knowledge, is the first large-scale QA training resource for Spanish.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.677.pdf

 

A Corpus for Visual Question Answering Annotated with Frame Semantic Information

Mehrdad Alizadeh and Barbara Di Eugenio

Visual Question Answering (VQA) has been widely explored as a computer vision problem, however enhancing VQA systems with linguistic information is necessary for tackling the complexity of the task. The language understanding part can play a major role especially for questions asking about events or actions expressed via verbs. We hypothesize that if the question focuses on events described by verbs, then the model should be aware of or trained with verb semantics, as expressed via semantic role labels, argument types, and/or frame elements. Unfortunately, no VQA dataset exists that includes verb semantic information. We created a new VQA dataset annotated with verb semantic information called imSituVQA. imSituVQA is built by taking advantage of the imSitu dataset annotations. The imSitu dataset consists of images manually labeled with semantic frame elements, mostly taken from FrameNet.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.678.pdf

 

Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering

Sarvesh Soni and Kirk Roberts

We evaluate the performance of various Transformer language models, when pre-trained and fine-tuned on different combinations of open-domain, biomedical, and clinical corpora on two clinical question answering (QA) datasets (CliCR and emrQA). We perform our evaluations on the task of machine reading comprehension, which involves training the model to answer a question given an unstructured context paragraph. We conduct a total of 48 experiments on different combinations of the large open-domain and domain-specific corpora. We found that an initial fine-tuning on an open-domain dataset, SQuAD, consistently improves the clinical QA performance across all the model variants.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.679.pdf

 

REPROLANG2020 Track

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The Learnability of the Annotated Input in NMT Replicating (Vanmassenhove and Way, 2018) with OpenNMT

Nicolas Ballier, Nabil Amari, Laure Merat and Jean-Baptiste Yunès

In this paper, we reproduce some of the experiments related to neural network training for Machine Translation as reported in (Vanmassenhove and Way, 2018). They annotated a sample from the EN-FR and EN-DE Europarl aligned corpora with syntactic and semantic annotations to train neural networks with the Nematus Neural Machine Translation (NMT) toolkit. Following the original publication, we obtained lower BLEU scores than the authors of the original paper, but on a more limited set of annotations. In the second half of the paper, we try to analyze the difference in the results obtained and suggest some methods to improve the results. We discuss the Byte Pair Encoding (BPE) used in the pre-processing phase and suggest feature ablation in relation to the granularity of syntactic and semantic annotations. The learnability of the annotated input is discussed in relation to existing resources for the target languages. We also discuss the feature representation likely to have been adopted for combining features.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.691.pdf

 

Reproducing Monolingual, Multilingual and Cross-Lingual CEFR Predictions

Yves Bestgen

his study aims to reproduce the research of Vajjala and Rama (2018) which showed that it is possible to predict the quality of a text written by learners of a given language by means of a model built on the basis of texts written by learners of another language. These authors also pointed out that POStag and dependency n-grams were significantly more effective than text length and global linguistic indices frequently used for this kind of task. The analyses performed show that some important points of their code did not correspond to the explanations given in the paper. These analyses confirm the possibility to use syntactic n-gram features in cross-lingual experiments to categorize texts according to their CEFR level (Common European Framework of Reference for Languages). However, text length and some classical indexes of readability are much more effective in the monolingual and the multilingual experiments than what Vajjala and Rama concluded and are even the best performing features when the cross-lingual task is seen as a regression problem. This study emphasized the importance for reproducibility of setting explicitly the reading order of the instances when using a K-fold CV procedure and, more generally, the need to properly randomize these instances before. It also evaluates a two-step procedure to determine the degree of statistical significance of the differences observed in a K-fold cross-validation schema and argues against the use of a Bonferroni-type correction in this context.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.687.pdf

 

ULMFiT replication

Mohamed Abdellatif and Ahmed Elgammal

In this paper, we reproduce some of the experiments of text classification by fine tuning pre-trained language model on the six English data-sets described in Howard and Ruder (2018) (verification). Then we investigate applicability of the model as is (pre-trained onEnglish) by conducting additional experiments on three other non-English data-sets that are not in the original paper (extension). For the verification experiments, we didn’t generate the exact same numbers as the original paper, however, the replication results are in the same range as compared to the baselines reported for comparison purposes. We attribute this to the limitation in computational resources which forced us to run on smaller batch sizes and for fewer number of epochs. Otherwise, we followed in the footsteps of the author to the best of our abilities (e.g. the libraries1, tutorials2, hyper-parameters and transfer learning methodology). We report implementation details as well as lessons learned in the appendices.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.685.pdf

 

A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well

Nicolas Garneau, Mathieu Godbout, David Beauchemin, Audrey Durand and Luc Lamontagne

In this paper, we reproduce the experiments of Artetxe et al. (2018b) regarding the robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. We show that the reproduction of their method is indeed feasible with some minor assumptions. We further investigate the robustness of their model by introducing four new languages that are less similar to English than the ones proposed by the original paper. In order to assess the stability of their model, we also conduct a grid search over sensible hyperparameters. We then propose key recommendations that apply to any research project in order to deliver fully reproducible research.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.681.pdf

 

Reproduction and Replication: A Case Study with Automatic Essay Scoring

Eva Huber and Çağrı Çöltekin

As in many experimental sciences, reproducibility of experiments has gained ever more attention in the NLP community. This paper presents our reproduction efforts of an earlier study of automatic essay scoring (AES) for determining the proficiency of second language learners in a multilingual setting. We present three sets of experiments with different objectives. First, as prescribed by the LREC 2020 REPROLANG shared task, we rerun the original AES system using the code published by the original authors on the same dataset. Second, we repeat the same experiments on the same data with a different implementation. And third, we test the original system on a different dataset and a different language. Most of our findings are in line with the findings of the original paper. Nevertheless, there are some discrepancies between our results and the results presented in the original paper. We report and discuss these differences in detail. We further go into some points related to confirmation of research findings through reproduction, including the choice of the dataset, reporting and accounting for variability, use of appropriate evaluation metrics, and making code and data available. We also discuss the varying uses and differences between the terms reproduction and replication, and we argue that reproduction, the confirmation of conclusions through independent experiments in varied settings is more valuable than exact replication of the published values.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.688.pdf

 

REPROLANG 2020: Automatic Proficiency Scoring of Czech, English, German, Italian, and Spanish Learner Essays

Andrew Caines and Paula Buttery

We report on our attempts to reproduce the work described in Vajjala & Rama 2018, `Experiments with universal CEFR classification', as part of REPROLANG 2020: this involves featured-based and neural approaches to essay scoring in Czech, German and Italian. Our results are broadly in line with those from the original paper, with some differences due to the stochastic nature of machine learning and programming language used. We correct an error in the reported metrics, introduce new baselines, apply the experiments to English and Spanish corpora, and generate adversarial data to test classifier robustness. We conclude that feature-based approaches perform better than neural network classifiers for text datasets of this size, though neural network modifications do bring performance closer to the best feature-based models.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.689.pdf

 

Reproducing a Morphosyntactic Tagger with a Meta-BiLSTM Model over Context Sensitive Token Encodings

Yung Han Khoe

Reproducibility is generally regarded as being a requirement for any form of experimental science. Even so, reproduction of research results is only recently beginning to be practiced and acknowledged. In the context of the REPROLANG 2020 shared task, we contribute to this trend by reproducing the work reported on by Bohnet et al. (2018) on morphosyntactic tagging. Their meta-BiLSTM model achieved state-of-the-art results across a wide range of languages. This was done by integrating sentence-level and single-word context through synchronized training by a meta-model. Our reproduction only partially confirms the main results of the paper in terms of outperforming earlier models. The results of our reproductions improve on earlier models on the morphological tagging task, but not on the part-of-speech tagging task. Furthermore, even where we improve on earlier models, we fail to match the F1-scores reported for the meta-BiLSTM model. Because we chose not to contact the original authors for our reproduction study, the uncertainty about the degree of parallelism that was achieved between the original study and our reproduction limits the value of our findings as an assessment of the reliability of the original results. At the same time, however, it underscores the relevance of our reproduction effort in regard to the reproducibility and interpretability of those findings. The discrepancies between our findings and the original results demonstrate that there is room for improvement in many aspects of reporting regarding the reproducibility of the experiments. In addition, we suggest that different reporting choices could improve the interpretability of the results.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.683.pdf

 

Language Proficiency Scoring

Cristina Arhiliuc, Jelena Mitrović and Michael Granitzer

The Common European Framework of Reference (CEFR) provides generic guidelines for the evaluation of language proficiency. Nevertheless, for automated proficiency classification systems, different approaches for different languages are proposed. Our paper evaluates and extends the results of an approach to Automatic Essay Scoring proposed as a part of the REPROLANG 2020 challenge. We provide a comparison between our results and the ones from the published paper and we include a new corpus for the English language for further experiments. Our results are lower than the expected ones when using the same approach and the system does not scale well with the added English corpus.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.690.pdf

 

A Closer Look on Unsupervised Cross-lingual Word Embeddings Mapping

Kamil Pluciński, Mateusz Lango and Michał Zimniewicz

In this work, we study the unsupervised cross-lingual word embeddings mapping method presented by Artetxe et al. (2018). First, wesuccessfully reproduced the experiments performed in the original work, finding only minor differences. Furthermore, we verified themethod’s robustness on different embedding representations and new language pairs, particularly these involving Slavic languages likePolish or Czech. We also performed an experimental analysis of the impact of the method’s parameters on the final result. Finally, welooked for an alternative way of initialization, which directly relies on the isometric assumption. Our work confirms the results presentedearlier, at the same time pointing at interesting problems occurring while using the method with different types of embeddings or onless-common language pairs.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.682.pdf

 

Reproducing Neural Ensemble Classifier for Semantic Relation Extraction inScientific Papers

Kyeongmin Rim, Jingxuan Tu, Kelley Lynch and James Pustejovsky

Within the natural language processing (NLP) community, shared tasks play an important role.  They define a common goal and allowthe the comparison of different methods on the same data. SemEval-2018 Task 7 involves the identification and classification of relationsin abstracts from computational linguistics (CL) publications. In this paper we describe an attempt to reproduce the methods and resultsfrom the top performing system at for SemEval-2018 Task 7. We describe challenges we encountered in the process, report on the resultsof our system, and discuss the ways that our attempt at reproduction can inform best practices.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.684.pdf

 

CombiNMT: An Exploration into Neural Text Simplification Models

Michael Cooper and Matthew Shardlow

This work presents a replication study of Exploring Neural Text Simplification Models (Nisioi et al., 2017). We were able to successfully replicate and extend the methods presented in the original paper. Alongside the replication results, we present our improvements dubbed CombiNMT. By using an updated implementation of OpenNMT, and incorporating the Newsela corpus alongside the original Wikipedia dataset (Hwang et al., 2016), as well as refining both datasets to select high quality training examples. Our work present two new systems, CombiNMT995, which is a result of matched sentences with a cosine similarity of 0.995 or less, and CombiNMT98, which, similarly, runs on a cosine similarity of 0.98 or less. By extending the human evaluation presented within the original paper, increasing both the number of annotators and the number of sentences annotated, with the intention of increasing the quality of the results, CombiNMT998 shows significant improvement over any of the Neural Text Simplification (NTS) systems from the original paper in terms of both the number of changes and the percentage of correct changes made.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.686.pdf

 

A Shared Task of a New, Collaborative Type to Foster Reproducibility: A First Exercise in the Area of Language Science and Technology with REPROLANG2020

António Branco, Nicoletta Calzolari, Piek Vossen, Gertjan Van Noord, Dieter van Uytvanck, João Silva, Luís Gomes, André Moreira and Willem Elbers

In this paper, we introduce a new type of shared task — which is collaborative rather than competitive — designed to support and fosterthe reproduction of research results. We also describe the first event running such a novel challenge, present the results obtained, discussthe lessons learned and ponder on future undertakings.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.680.pdf

 

Semantic Web and Linked Data

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KGvec2go – Knowledge Graph Embeddings as a Service

Jan Portisch, Michael Hladik and Heiko Paulheim

In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications. Currently, we serve pre-trained embeddings for four knowledge graphs. We introduce the service and its usage, and we show further that the trained models have semantic value by evaluating them on multiple semantic benchmarks. The evaluation also reveals that the combination of multiple models can lead to a better outcome than the best individual model.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.692.pdf

 

Ontology Matching Using Convolutional Neural Networks

Alexandre Bento, Amal Zouaq and Michel Gagnon

In order to achieve interoperability of information in the context of the Semantic Web, it is necessary to find effective ways to align different ontologies. As the number of ontologies grows for a given domain, and as overlap between ontologies grows proportionally, it is becoming more and more crucial to develop accurate and reliable techniques to perform this task automatically. While traditional approaches to address this challenge are based on string metrics and structure analysis, in this paper we present a methodology to align ontologies automatically using machine learning techniques. Specifically, we use convolutional neural networks to perform string matching between class labels using character embeddings. We also rely on the set of superclasses to perform the best alignment. Our results show that we obtain state-of-the-art performance on ontologies from the Ontology Alignment Evaluation Initiative (OAEI). Our model also maintains good performance when tested on a different domain, which could lead to potential cross-domain applications.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.693.pdf

 

Defying Wikidata: Validation of Terminological Relations in the Web of Data

Patricia Martín-Chozas, Sina Ahmadi and Elena Montiel-Ponsoda

In this paper we present an approach to validate terminological data retrieved from open encyclopaedic knowledge bases.  This need arises from the enrichment of automatically extracted terms with information from existing resources in theLinguistic Linked Open Data cloud. Specifically, the resource employed for this enrichment is WIKIDATA, since it is one of the biggest knowledge bases freely available within the Semantic Web.   During the experiment,  we noticed that certain RDF properties in the Knowledge Base did not contain the data they are intended to represent, but a different type of information. In this paper we propose an approach to validate the retrieved data based on four axioms that rely on two linguistic theories: the x-bar theory and the multidimensional theory of terminology.The validation process is supported by a second knowledge base specialised in linguistic data;  in this case,  CONCEPTNET.   In our experiment, we validate terms from the legal domain in four languages:  Dutch, English, German and Spanish.  The final aim is to generate a set of sound and reliable terminological resources in RDF to contribute to the population of the Linguistic Linked Open Data cloud.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.694.pdf

 

 

Recent Developments for the Linguistic Linked Open Data Infrastructure

Thierry Declerck, John Philip McCrae, Matthias Hartung, Jorge Gracia, Christian Chiarcos, Elena Montiel-Ponsoda, Philipp Cimiano, Artem Revenko, Roser Saurí, Deirdre Lee, Stefania Racioppa, Jamal Abdul Nasir, Matthias Orlikowsk, Marta Lanau-Coronas, Chris

In this paper we describe the contributions made by the European H2020 project “Prêt-à-LLOD” (‘Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors’) to the further development of the Linguistic Linked Open Data (LLOD) infrastructure. Prêt-à-LLOD aims to develop a new methodology for building data value chains applicable to a wide range of sectors and applications and based around language resources and language technologies that can be integrated by means of semantic technologies. We describe the methods implemented for increasing the number of language data sets in the LLOD. We also present the approach for ensuring interoperability and for porting LLOD data sets and services to other infrastructures, as well as the contribution of the projects to existing standards.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.695.pdf

 

Annotation Interoperability for the Post-ISOCat Era

Christian Chiarcos, Christian Fäth and Frank Abromeit

With this paper, we provide an overview over ISOCat successor solutions and annotation standardization efforts since 2010, and we describe the low-cost harmonization of post-ISOCat vocabularies by means of modular, linked ontologies: The CLARIN Concept Registry, LexInfo, Universal Parts of Speech, Universal Dependencies and UniMorph are linked with the Ontologies of Linguistic Annotation and through it with ISOCat, the GOLD ontology, the Typological Database Systems ontology and a large number of annotation schemes.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.696.pdf

 

Semantics

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A Large Harvested Corpus of Location Metonymy

Kevin Alex Mathews and Michael Strube

Metonymy is a figure of speech in which an entity is referred to by another related entity. The existing datasets of metonymy are either too small in size or lack sufficient coverage. We propose a new, labelled, high-quality corpus of location metonymy called WiMCor, which is large in size and has high coverage. The corpus is harvested semi-automatically from English Wikipedia. We use different labels of varying granularity to annotate the corpus. The corpus can directly be used for training and evaluating automatic metonymy resolution systems. We construct benchmarks for metonymy resolution, and evaluate baseline methods using the new corpus.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.697.pdf

 

The DAPRECO Knowledge Base: Representing the GDPR in LegalRuleML

Livio Robaldo, Cesare Bartolini and Gabriele Lenzini

The DAPRECO knowledge base (D-KB) is a repository of rules written in LegalRuleML, an XML formalism designed to represent the logical content of legal documents. The rules represent the provisions of the General Data Protection Regulation (GDPR). The D-KB builds upon the Privacy Ontology (PrOnto) (Palmirani et al., 2018), which provides a model for the legal concepts involved in the GDPR, by adding a further layer of constraints in the form of if-then rules, referring either to standard first order logic implications or to deontic statements. If-then rules are formalized in reified I/O logic (Robaldo and Sun, 2017) and then codified in (LegalRuleML, 2019). To date, the D-KB is the biggest knowledge base in LegalRuleML freely available online at (Robaldo et al., 2019).

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.698.pdf

 

The Universal Decompositional Semantics Dataset and Decomp Toolkit

Aaron Steven White, Elias Stengel-Eskin, Siddharth Vashishtha, Venkata Subrahmanyan Govindarajan, Dee Ann Reisinger, Tim Vieira, Keisuke Sakaguchi, Sheng Zhang, Francis Ferraro, Rachel Rudinger, Kyle Rawlins and Benjamin Van Durme

We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantics-aligned annotation sets within a single semantic graph specification---with graph structures defined by the predicative patterns produced by the PredPatt tool and real-valued node and edge attributes constructed using sophisticated normalization procedures. The Decomp toolkit provides a suite of Python 3 tools for querying UDS graphs using SPARQL. Both UDS1.0 and Decomp0.1 are publicly available at http://decomp.io.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.699.pdf

 

Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit?

Emmanuele Chersoni, Ludovica Pannitto, Enrico Santus, Alessandro Lenci and Chu-Ren Huang

While neural embeddings represent a popular choice for word representation in a wide variety of NLP tasks, their usage for thematic fit modeling has been limited, as they have been reported to lag behind syntax-based count models. In this paper, we propose a complete evaluation of count models and word embeddings on thematic fit estimation, by taking into account a larger number of parameters and verb roles and introducing also dependency-based embeddings in the comparison. Our results show a complex scenario, where a determinant factor for the performance seems to be the availability to the model of reliable syntactic information for building the distributional representations of the roles.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.700.pdf

 

Ciron: a New Benchmark Dataset for Chinese Irony Detection

Rong Xiang, Xuefeng Gao, Yunfei Long, Anran Li, Emmanuele Chersoni, Qin Lu and Chu-Ren Huang

Automatic Chinese irony detection is a challenging task, and it has a strong impact on linguistic research. However, Chinese irony detection often lacks labeled benchmark datasets. In this paper, we introduce Ciron, the first Chinese benchmark dataset available for irony detection for machine learning models. Ciron includes more than 8.7K posts, collected from Weibo, a micro blogging platform. Most importantly, Ciron is collected with no pre-conditions to ensure a much wider coverage. Evaluation on seven different machine learning classifiers proves the usefulness of Ciron as an important resource for Chinese irony detection.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.701.pdf

 

wikiHowToImprove: A Resource and Analyses on Edits in Instructional Texts

Talita Anthonio, Irshad Bhat and Michael Roth

Instructional texts, such as articles in wikiHow, describe the actions necessary to accomplish a certain goal. In wikiHow and other resources, such instructions are subject to revision edits on a regular basis. Do these edits improve instructions only in terms of style and correctness, or do they provide clarifications necessary to follow the instructions and to accomplish the goal? We describe a resource and first studies towards answering this question. Specifically, we create wikiHowToImprove, a collection of revision histories for about 2.7 million sentences from about 246\,000 wikiHow articles. We describe human annotation studies on categorizing a subset of sentence-level edits and provide baseline models for the task of automatically distinguishing  ``older'' from ``newer'' revisions of a sentence.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.702.pdf

 

Must Children be Vaccinated or not? Annotating Modal Verbs in the Vaccination Debate

Liza King and Roser Morante

In this paper we analyze the use of modal verbs in a corpus of texts related to the vaccination debate. Broadly speaking, the vaccination debate centers around whether vaccination is safe, and whether it is morally acceptable to enforce mandatory vaccination.  In order to successfully intervene and curb the spread of preventable diseases due to low vaccination rates, health practitioners need to be adequately informed on public perception of the safety and necessity of vaccines. Public perception can relate to the strength of conviction that an individual may have towards a proposition (e.g. `one must vaccinate' versus `one should vaccinate'), as well as qualify the type of proposition, be it related to morality (`government should not interfere in my personal choice') or related to possibility (`too many vaccines at once could hurt my child'). Text mining and analysis of modal auxiliaries are economically viable means of gaining insights into these perspectives, particularly on a large scale due to the widespread use of social media and blogs as vehicles of communication.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.703.pdf

 

NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution

Aditya Khandelwal and Suraj Sawant

Negation is an important characteristic of language, and a major component of information extraction from text. This subtask is of considerable importance to the biomedical domain. Over the years, multiple approaches have been explored to address this problem: Rule-based systems, Machine Learning classifiers, Conditional Random Field models, CNNs and more recently BiLSTMs. In this paper, we look at applying Transfer Learning to this problem. First, we extensively review previous literature addressing Negation Detection and Scope Resolution across the 3 datasets that have gained popularity over the years: the BioScope Corpus, the Sherlock dataset, and the SFU Review Corpus. We then explore the decision choices involved with using BERT, a popular transfer learning model, for this task, and report state-of-the-art results for scope resolution across all 3 datasets. Our model, referred to as NegBERT, achieves a token level F1 score on scope resolution of 92.36 on the Sherlock dataset, 95.68 on the BioScope Abstracts subcorpus, 91.24 on the BioScope Full Papers subcorpus, 90.95 on the SFU Review Corpus, outperforming the previous state-of-the-art systems by a significant margin. We also analyze the model’s generalizability to datasets on which it is not trained.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.704.pdf

 

Spatial Multi-Arrangement for Clustering and Multi-way Similarity Dataset Construction

Olga Majewska, Diana McCarthy, Jasper van den Bosch, Nikolaus Kriegeskorte, Ivan Vulić and Anna Korhonen

We present a novel methodology for fast bottom-up creation of large-scale semantic similarity resources to support development and evaluation of NLP systems. Our work targets verb similarity, but the methodology is equally applicable to other parts of speech. Our approach circumvents the bottleneck of slow and expensive manual development of lexical resources by leveraging semantic intuitions of native speakers and adapting a spatial multi-arrangement approach from cognitive neuroscience, used before only with visual stimuli, to lexical stimuli. Our approach critically obtains judgments of word similarity in the context of a set of related words, rather than of word pairs in isolation. We also handle lexical ambiguity as a natural consequence of a two-phase process where verbs are placed in broad semantic classes prior to the fine-grained spatial similarity judgments. Our proposed design produces a large-scale verb resource comprising 17 relatedness-based classes and a verb similarity dataset containing similarity scores for 29,721 unique verb pairs and 825 target verbs, which we release with this paper.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.705.pdf

 

A Short Survey on Sense-Annotated Corpora

Tommaso Pasini and Jose Camacho-Collados

Large sense-annotated datasets are increasingly necessary for training deep supervised systems in Word Sense Disambiguation. However, gathering high-quality sense-annotated data for as many instances as possible is a laborious and expensive task. This has led to the proliferation of automatic and semi-automatic methods for overcoming the so-called knowledge-acquisition bottleneck. In this short survey we present an overview of sense-annotated corpora, annotated either manually- or (semi)automatically, that are currently available for different languages and featuring distinct lexical resources as inventory of senses, i.e. WordNet, Wikipedia, BabelNet. Furthermore, we provide the reader with general statistics of each dataset and an analysis of their specific features.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.706.pdf

 

Using Distributional Thesaurus Embedding for Co-hyponymy Detection

Abhik Jana, Nikhil Reddy Varimalla and Pawan Goyal

Discriminating lexical relations among distributionally similar words has always been a challenge for natural language processing (NLP) community. In this paper, we investigate whether the network embedding of distributional thesaurus can be effectively utilized to detect co-hyponymy relations. By extensive experiments over three benchmark datasets, we show that the vector representation obtained by applying node2vec on distributional thesaurus outperforms the state-of-the-art models for binary classification of co-hyponymy vs. hypernymy, as well as co-hyponymy vs. meronymy, by huge margins.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.707.pdf

 

NUBes: A Corpus of Negation and Uncertainty in Spanish Clinical Texts

Salvador Lima Lopez, Naiara Perez, Montse Cuadros and German Rigau

This paper introduces the first version of the NUBes corpus (Negation and Uncertainty annotations in Biomedical texts in Spanish). The corpus is part of an on-going research and currently consists of 29,682 sentences obtained from anonymised health records annotated with negation and uncertainty.  The article includes an exhaustive comparison with similar corpora in Spanish, and presents the main annotation and design decisions. Additionally, we perform preliminary experiments using deep learning algorithms to validate the annotated dataset. As far as we know, NUBes is the largest available corpora for negation in Spanish and the first that also incorporates the annotation of speculation cues, scopes, and events.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.708.pdf

 

Decomposing and Comparing Meaning Relations: Paraphrasing, Textual Entailment, Contradiction, and Specificity

Venelin Kovatchev, Darina Gold, M. Antonia Marti, Maria Salamo and Torsten Zesch

In this paper, we present a methodology for decomposing and comparing multiple meaning relations (paraphrasing, textual entailment, contradiction, and specificity). The methodology includes SHARel - a new typology that consists of 26 linguistic and 8 reason-based categories. We use the typology to annotate a corpus of 520 sentence pairs in English and we demonstrate that unlike previous typologies, SHARel can be applied to all relations of interest with a high inter-annotator agreement. We analyze and compare the frequency and distribution of the linguistic and  reason-based phenomena involved in paraphrasing, textual entailment, contradiction,  and specificity. This comparison allows for a much more in-depth analysis of the workings of the individual relations and the way they interact and compare with each other. We release all resources (typology, annotation guidelines, and annotated corpus) to the community.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.709.pdf

 

Object Naming in Language and Vision: A Survey and a New Dataset

Carina Silberer, Sina Zarrieß and Gemma Boleda

People choose particular names for objects, such as dog or puppy for a given dog. Object naming has been studied in Psycholinguistics, but has received relatively little attention in Computational Linguistics. We review resources from Language and Vision that could be used to study object naming on a large scale, discuss their shortcomings, and create a new dataset that affords more opportunities for analysis and modeling. Our dataset, ManyNames, provides 36 name annotations for each of 25K objects in images selected from VisualGenome. We highlight the challenges involved and provide a preliminary analysis of the ManyNames data, showing that there is a high level of agreement in naming, on average. At the same time, the average number of name types associated with an object is much higher in our dataset than in existing corpora for Language and Vision, such that ManyNames provides a rich resource for studying phenomena like hierarchical variation (chihuahua vs. dog), which has been discussed at length in the theoretical literature, and other less well studied phenomena like cross-classification (cake vs. dessert).

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.710.pdf

 

MSD-1030: A Well-built Multi-Sense Evaluation Dataset for Sense Representation Models

Ting-Yu Yen, Yang-Yin Lee, Yow-Ting Shiue, Hen-Hsen Huang and Hsin-Hsi Chen

Sense embedding models handle polysemy by giving each distinct meaning of a word form a separate representation. They are considered improvements over word models, and their effectiveness is usually judged with benchmarks such as semantic similarity datasets. However, most of these datasets are not designed for evaluating sense embeddings. In this research, we show that there are at least six concerns about evaluating sense embeddings with existing benchmark datasets, including the large proportions of single-sense words and the unexpected inferior performance of several multi-sense models to their single-sense counterparts. These observations call into serious question whether evaluations based on these datasets can reflect the sense model’s ability to capture different meanings. To address the issues, we propose the Multi-Sense Dataset (MSD-1030), which contains a high ratio of multi-sense word pairs. A series of analyses and experiments show that MSD-1030 serves as a more reliable benchmark for sense embeddings. The dataset is available at http://nlg.csie.ntu.edu.tw/nlpresource/MSD-1030/.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.711.pdf

 

Figure Me Out: A Gold Standard Dataset for Metaphor Interpretation

Omnia Zayed, John Philip McCrae and Paul Buitelaar

Metaphor comprehension and understanding is a complex cognitive task that requires interpreting metaphors by grasping the interaction between the meaning of their target and source concepts. This is very challenging for humans, let alone computers. Thus, automatic metaphor interpretation is understudied in part due to the lack of publicly available datasets. The creation and manual annotation of such datasets is a demanding task which requires huge cognitive effort and time. Moreover, there will always be a question of accuracy and consistency of the annotated data due to the subjective nature of the problem. This work addresses these issues by presenting an annotation scheme to interpret verb-noun metaphoric expressions in text. The proposed approach is designed with the goal of reducing the workload on annotators and maintain consistency. Our methodology employs an automatic retrieval approach which utilises external lexical resources, word embeddings and semantic similarity to generate possible interpretations of identified metaphors in order to enable quick and accurate annotation. We validate our proposed approach by annotating around 1,500 metaphors in tweets which were annotated by six native English speakers. As a result of this work, we publish as linked data the first gold standard dataset for metaphor interpretation which will facilitate research in this area.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.712.pdf

 

Extrinsic Evaluation of French Dependency Parsers on a Specialized Corpus: Comparison of Distributional Thesauri

Ludovic Tanguy, Pauline Brunet and Olivier Ferret

We present a study in which we compare 11 different French dependency parsers on a specialized corpus (consisting of research articles on NLP from the proceedings of the TALN conference). Due to the lack of a suitable gold standard, we use each of the parsers' output to generate distributional thesauri using a frequency-based method. We compare these 11 thesauri to assess the impact of choosing a parser over another. We show that, without any reference data, we can still identify relevant subsets among the different parsers. We also show that the similarity we identify between parsers is confirmed on a restricted distributional benchmark.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.713.pdf

 

Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing

Xiaojing Yu, Tianlong Chen, Zhengjie Yu, Huiyu Li, Yang Yang, Xiaoqian Jiang and Anxiao Jiang

Clinical trials often require that patients meet eligibility criteria (e.g., have specific conditions) to ensure the safety and the effectiveness of studies.  However, retrieving eligible patients for a trial from the electronic health record (EHR) database remains a challenging task for clinicians since it requires not only medical knowledge about eligibility criteria, but also an adequate understanding of structured query language (SQL).  In this paper, we introduce a new dataset that includes the first-of-its-kind eligibility-criteria corpus and the corresponding queries for criteria-to-sql (Criteria2SQL), a task translating the eligibility criteria to executable SQL queries. Compared to existing datasets, the queries in the dataset here are derived from the eligibility criteria of clinical trials and include {\it Order-sensitive, Counting-based, and Boolean-type} cases which are not seen before. In addition to the dataset, we propose a novel neural semantic parser as a strong baseline model. Extensive experiments show that the proposed parser outperforms existing state-of-the-art general-purpose text-to-sql models while highlighting the challenges presented by the new dataset. The uniqueness and the diversity of the dataset leave a lot of research opportunities for future improvement.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.714.pdf

 

Recognizing Semantic Relations by Combining Transformers and Fully Connected Models

Dmitri Roussinov, Serge Sharoff and Nadezhda Puchnina

Automatically recognizing an existing semantic relation (e.g. “is a”, “part of”, “property of”, “opposite of” etc.) between two words (phrases, concepts, etc.) is an important task affecting many NLP applications and has been subject of extensive experimentation and modeling. Current approaches to automatically telling if a relation exists between two given concepts X and Y can be grouped into two types: 1) those modeling word-paths connecting X and Y in text and 2) those modeling distributional properties of X and Y separately, not necessary in the proximity to each other. Here, we investigate how both types can be improved and combined. We suggest a distributional approach that is based on an attention-based transformer. We have also developed a novel word path model that combines useful properties of a convolutional network with a fully connected language model. While our transformer-based approach works better, both our models significantly outperform the state-of-the-art within their classes of approaches. We also demonstrate that combining the two approaches results in additional gains since they use somewhat different data sources.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.715.pdf

 

Word Attribute Prediction Enhanced by Lexical Entailment Tasks

Mika Hasegawa, Tetsunori Kobayashi and Yoshihiko Hayashi

Human semantic knowledge about concepts acquired through perceptual inputs and daily experiences can be expressed as a bundle of attributes. Unlike the conventional distributed word representations that are purely induced from a text corpus, a semantic attribute is associated with a designated dimension in attribute-based vector representations. Thus, semantic attribute vectors can effectively capture the commonalities and differences among concepts.  However, as semantic attributes have been generally created by psychological experimental settings involving human annotators, an automatic method to create or extend such resources is highly demanded in terms of language resource development and maintenance. This study proposes a two-stage neural network architecture, Word2Attr, in which initially acquired attribute representations are then fine-tuned by employing supervised lexical entailment tasks.  The quantitative empirical results demonstrated that the fine-tuning was indeed effective in improving the performances of semantic/visual similarity/relatedness evaluation tasks. Although the qualitative analysis confirmed that the proposed method could often discover valid but not-yet human-annotated attributes, they also exposed future issues to be worked: we should refine the inventory of semantic attributes that currently relies on an existing dataset.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.716.pdf

 

From Spatial Relations to Spatial Configurations

Soham Dan, Parisa Kordjamshidi, Julia Bonn, Archna Bhatia, Zheng Cai, Martha Palmer and Dan Roth

Spatial Reasoning from language is essential for natural language understanding. Supporting it requires a representation scheme that can capture spatial phenomena encountered in language as well as in images and videos.Existing spatial representations are not sufficient for describing spatial configurations used in complex tasks. This paper extends the capabilities of existing spatial representation languages and increases coverage of the semantic aspects that are needed to ground spatial meaning of natural language text in the world. Our spatial relation language is able to represent a large, comprehensive set of spatial concepts crucial for reasoning and is designed to support composition of static and dynamic spatial configurations. We integrate this language with the Abstract Meaning Representation (AMR) annotation schema and present a corpus annotated by this extended AMR. To exhibit the applicability of our representation scheme, we annotate text taken from diverse datasets and show how we extend the capabilities of existing spatial representation languages with fine-grained decomposition of semantics and blend it seamlessly with AMRs of sentences and discourse representations as a whole.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.717.pdf

 

Representing Verbs with Visual Argument Vectors

Irene Sucameli and Alessandro Lenci

Is it possible to use images to model verb semantic similarities? Starting from this core question, we developed two textual distributional semantic models and a visual one. We found particularly interesting and challenging to investigate this Part of Speech since verbs are not often analysed in researches focused on multimodal distributional semantics.  After the creation of the visual and textual distributional space, the three models were evaluated in relation to SimLex-999, a gold standard resource.  Through this evaluation, we demonstrate that, using visual distributional models, it is possible to extract meaningful information and to effectively capture the semantic similarity between verbs.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.718.pdf

 

Are White Ravens Ever White? - Non-Literal Adjective-Noun Phrases in Polish

Agnieszka Mykowiecka and Malgorzata Marciniak

In the paper we describe two  resources of Polish data focused on literal and metaphorical meanings of adjective-noun phrases. The first one is FigAN and consists of isolated phrases which are divided into three types: phrases with only literal meaning,  with only metaphorical meaning, and phrases which can be interpreted as literal or metaphorical ones depending on a context of use. The second data is the FigSen corpus which consists of 1833 short fragments of texts containing at least one phrase from the FigAN data which may have both meanings. The corpus is annotated in two ways. One approach concerns annotation of all adjective-noun phrases.  In the second approach,  literal or metaphorical senses are assigned to all adjectives and nouns in the data. The paper addresses statistics of  data and compares two types of annotation. The corpora were used in experiments of automatic recognition of Polish non-literal adjective noun phrases.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.719.pdf

 

CoSimLex: A Resource for Evaluating Graded Word Similarity in Context

Carlos Santos Armendariz, Matthew Purver, Matej Ulčar, Senja Pollak, Nikola Ljubešić and Mark Granroth-Wilding

State of the art natural language processing tools are built on context-dependent word embeddings, but no direct method for evaluating these representations currently exists. Standard tasks and datasets for intrinsic evaluation of embeddings are based on judgements of similarity, but ignore context; standard tasks for word sense disambiguation take account of context but do not provide continuous measures of meaning similarity. This paper describes an effort to build a new dataset, CoSimLex, intended to fill this gap. Building on the standard pairwise similarity task of SimLex-999, it provides context-dependent similarity measures; covers not only discrete differences in word sense but more subtle, graded changes in meaning; and covers not only a well-resourced language (English) but a number of less-resourced languages. We define the task and evaluation metrics, outline the dataset collection methodology, and describe the status of the dataset so far.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.720.pdf

 

A French Version of the FraCaS Test Suite

Maxime Amblard, Clément Beysson, Philippe de Groote, Bruno Guillaume and Sylvain Pogodalla

This paper presents a French version of the FraCaS test suite. This test suite, originally written in English, contains problems illustrating semantic inference in natural language. We describe linguistic choices we had to make when translating the FraCaS test suite in French, and discuss some of the issues that were raised by the translation. We also report an experiment we ran in order to test both the translation and the logical semantics underlying the problems of the test suite. This provides a way of checking formal semanticists' hypotheses against actual semantic capacity of speakers (in the present case, French speakers), and allow us to compare the results we obtained with the ones of similar experiments that have been conducted for other languages.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.721.pdf

 

Automatic Compilation of Resources for Academic Writing and Evaluating with Informal Word Identification and Paraphrasing System

Seid Muhie Yimam, Gopalakrishnan Venkatesh, John Lee and Chris Biemann

We present the first approach to automatically building resources for academic writing. The aim is to build a writing aid system that automatically edits a text so that it better adheres to the academic style of writing. On top of existing academic resources, such as the Corpus of Contemporary American English (COCA) academic Word List, the New Academic Word List, and the Academic Collocation List, we also explore how to dynamically build such resources that would be used to automatically identify informal or non-academic words or phrases. The resources are compiled using different generic approaches that can be extended for different domains and languages. We describe the evaluation of resources with a system implementation. The system consists of an informal word identification (IWI), academic candidate paraphrase generation, and paraphrase ranking components. To generate candidates and rank them in context, we have used the PPDB and WordNet paraphrase resources. We use the Concepts in Context (CoInCO) "All-Words" lexical substitution dataset both for the informal word identification and paraphrase generation experiments. Our informal word identification component achieves an F-1 score of 82%, significantly outperforming a stratified classifier baseline. The main contribution of this work is a domain-independent methodology to build targeted resources for writing aids.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.722.pdf

 

Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains

Bianca Scarlini, Tommaso Pasini and Roberto Navigli

The knowledge acquisition bottleneck problem dramatically hampers the creation of sense-annotated data for Word Sense Disambiguation (WSD). Sense-annotated data are scarce for English and almost absent for other languages. This limits the range of action of deep-learning approaches, which today are at the base of any NLP task and are hungry for data. We mitigate this issue and encourage further research in multilingual WSD by releasing to the NLP community five large datasets annotated with word-senses in five different languages, namely, English, French, Italian, German and Spanish, and 5 distinct datasets in English, each for a different semantic domain. We show that supervised WSD models trained on our data attain higher performance than when trained on other automatically-created corpora. We release all our data containing more than 15 million annotated instances in 5 different languages at http://trainomatic.org/onesec.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.723.pdf

 

FrSemCor: Annotating a French Corpus with Supersenses

Lucie Barque, Pauline Haas, Richard Huyghe, Delphine Tribout, Marie Candito, Benoit Crabbé and Vincent Segonne

French, as many languages, lacks semantically annotated corpus data. Our aim is to provide the linguistic and NLP research communities with a gold standard sense-annotated corpus of French, using WordNet Unique Beginners as semantic tags, thus allowing for interoperability. In this paper, we report on the first phase of the project, which focused on the annotation of common nouns. The resulting dataset consists of more than 12,000 French noun occurrences which were annotated in double blind and adjudicated according to a carefully redefined set of supersenses. The resource is released online under a Creative Commons Licence.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.724.pdf

 

A Formal Analysis of Multimodal Referring Strategies Under Common Ground

Nikhil Krishnaswamy and James Pustejovsky

In this paper, we present an analysis of computationally generated mixed-modality definite referring expressions using combinations of gesture and linguistic descriptions. In doing so, we expose some striking formal semantic properties of the interactions between gesture and language, conditioned on the introduction of content into the common ground between the (computational) speaker and (human) viewer, and demonstrate how these formal features can contribute to training better models to predict viewer judgment of referring expressions, and potentially to the generation of more natural and informative referring expressions.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.725.pdf

 

Improving Neural Metaphor Detection with Visual Datasets

Gitit Kehat and James Pustejovsky

We present new results on Metaphor Detection by using text from visual datasets. Using a straightforward technique for sampling text from Vision-Language datasets, we create a data structure we term a visibility word embedding. We then combine these embeddings in a relatively simple BiLSTM module augmented with contextualized word representations (ELMo), and show improvement over previous state-of-the-art approaches that use more complex neural network  architectures and richer linguistic features, for the task of verb classification.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.726.pdf

 

Building a Hebrew Semantic Role Labeling Lexical Resource from Parallel Movie Subtitles

Ben Eyal and Michael Elhadad

We present a semantic role labeling resource for Hebrew built semi-automatically through annotation projection from English. This corpus is derived from the multilingual OpenSubtitles dataset and includes short informal sentences, for which reliable linguistic annotations have been computed.  We provide a fully annotated version of the data including morphological analysis, dependency syntax and semantic role labeling in both FrameNet and ProbBank styles.  Sentences are aligned between English and Hebrew, both sides include full annotations and the explicit mapping from the English arguments to the Hebrew ones. We train a neural SRL model on this Hebrew resource exploiting the pre-trained multilingual BERT transformer model, and provide the first available baseline model for Hebrew SRL as a reference point. The code we provide is generic and can be adapted to other languages to bootstrap SRL resources.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.727.pdf

 

Word Sense Disambiguation for 158 Languages using Word Embeddings Only

Varvara Logacheva, Denis Teslenko, Artem Shelmanov, Steffen Remus, Dmitry Ustalov, Andrey Kutuzov, Ekaterina Artemova, Chris Biemann, Simone Paolo Ponzetto and Alexander Panchenko

Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed to solve this task. However, (i) the inherent Zipfian distribution of supervised training instances for a given word and/or (ii) the  quality of linguistic knowledge representations motivate the development of completely unsupervised and knowledge-free approaches to word sense disambiguation (WSD). They are particularly useful for under-resourced languages which do not have any resources for building either supervised and/or knowledge-based models. In this paper, we present a method that takes as input a standard pre-trained word embedding model and induces a fully-fledged word sense inventory, which can be used for disambiguation in context. We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings by Grave et al., (2018), enabling WSD in these languages. Models and system are available online.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.728.pdf

 

Extraction of Hyponymic Relations in French with Knowledge-Pattern-Based Word Sketches

Antonio San Martín, Catherine Trekker and Pilar León-Araúz

Hyponymy is the cornerstone of taxonomies and concept hierarchies. However, the extraction of hypernym-hyponym pairs from a corpus can be time-consuming, and reconstructing the hierarchical network of a domain is often an extremely complex process. This paper presents the development and evaluation of the French EcoLexicon Semantic Sketch Grammar (ESSG-fr), a French hyponymic sketch grammar for Sketch Engine based on knowledge patterns. It offers a user-friendly way of extracting hyponymic pairs in the form of word sketches in any user-owned corpus. The ESSG-fr contains three times more hyponymic patterns than its English counterpart and has been tested in a multidisciplinary corpus. It is thus expected to be domain-independent. Moreover, the following methodological innovations have been included in its development: (1) use of English hyponymic patterns in a parallel corpus to find new French patterns; (2) automatic inclusion of the results of the Sketch Engine thesaurus to find new variants of the patterns. As for its evaluation, the ESSG-fr returns 70% valid hyperonyms and hyponyms, measured on 180 extracted pairs of terms in three different domains.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.729.pdf

 

SeCoDa: Sense Complexity Dataset

David Strohmaier, Sian Gooding, Shiva Taslimipoor and Ekaterina Kochmar

The Sense Complexity Dataset (SeCoDa) provides a corpus that is annotated jointly for complexity and word senses. It thus provides a valuable resource for both word sense disambiguation and the task of complex word identification. The intention is that this dataset will be used to identify complexity at the level of word senses rather than word tokens. For word sense annotation SeCoDa uses a hierarchical scheme that is based on information available in the Cambridge Advanced Learner's Dictionary. This way we can offer more coarse-grained senses than directly available in WordNet.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.730.pdf

 

A New Resource for German Causal Language

Ines Rehbein and Josef Ruppenhofer

We present a new resource for German causal language, with annotations in context for verbs, nouns and prepositions. Our dataset includes 4,390 annotated instances for more than 150 different triggers. The annotation scheme distinguishes three different types of causal events (CONSEQUENCE, MOTIVATION, PURPOSE). We also provide annotations for semantic roles, i.e. of the cause and effect for the causal event as well as the actor and affected party, if present. In the paper, we present inter-annotator agreement scores for our dataset and discuss problems for annotating causal language. Finally, we present experiments where we frame causal annotation as a sequence labelling problem and report baseline results for the prediciton of causal arguments and for predicting different types of causation.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.731.pdf

 

One Classifier for All Ambiguous Words: Overcoming Data Sparsity by Utilizing Sense Correlations Across Words

Prafulla Kumar Choubey and Ruihong Huang

Most supervised word sense disambiguation (WSD) systems build word-specific classifiers by leveraging labeled data. However, when using word-specific classifiers, the sparseness of annotations leads to inferior sense disambiguation performance on less frequently seen words. To combat data sparsity, we propose to learn a single model that derives sense representations and meanwhile enforces congruence between a word instance and its right sense by using both sense-annotated data and lexical resources. The model is shared across words that allows utilizing sense correlations across words, and therefore helps to transfer common disambiguation rules from annotation-rich words to annotation-lean words.  Empirical evaluation on benchmark datasets shows that the proposed shared model outperforms the equivalent classifier-based models by 1.7%, 2.5% and 3.8% in F1-score when using GloVe, ELMo and BERT word embeddings respectively.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.732.pdf

 

A Corpus of Adpositional Supersenses for Mandarin Chinese

Siyao Peng, Yang Liu, Yilun Zhu, Austin Blodgett, Yushi Zhao and Nathan Schneider

Adpositions are frequent markers of semantic relations, but they are highly ambiguous and vary significantly from language to language. Moreover, there is a dearth of annotated corpora for investigating the cross-linguistic variation of adposition semantics, or for building multilingual disambiguation systems. This paper presents a corpus in which all adpositions have been semantically annotated in Mandarin Chinese; to the best of our knowledge, this is the first Chinese corpus to be broadly annotated with adposition semantics. Our approach adapts a framework that defined a general set of supersenses according to ostensibly language-independent semantic criteria, though its development focused primarily on English prepositions (Schneider et al., 2018). We find that the supersense categories are well-suited to Chinese adpositions despite syntactic differences from English. On a Mandarin translation of The Little Prince, we achieve high inter-annotator agreement and analyze semantic correspondences of adposition tokens in bitext.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.733.pdf

 

The Russian PropBank

Sarah Moeller, Irina Wagner, Martha Palmer, Kathryn Conger and Skatje Myers

This paper presents a proposition bank for Russian (RuPB), a resource for semantic role labeling (SRL). The motivating goal for this resource is to automatically project semantic role labels from English to Russian. This paper describes frame creation strategies, coverage, and the process of sense disambiguation. It discusses language-specific issues that complicated the process of building the PropBank and how these challenges were exploited as language-internal guidance for consistency and coherence.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.734.pdf

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