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LREC 2020 Paper Dissemination (5/10)

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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 December 8, 2020

Links to each session

 

 
Lexicon, Lexical Database

MultiMWE: Building a Multi-lingual Multi-Word Expression (MWE) Parallel Corpora

Lifeng Han, Gareth Jones and Alan Smeaton

Multi-word expressions (MWEs) are a hot topic in research in natural language processing (NLP), including topics such as MWE detection, MWE decomposition, and research investigating the exploitation of MWEs in other NLP fields such as Machine Translation. However, the availability of bilingual or multi-lingual MWE corpora is very limited. The only bilingual MWE corpora that we are aware of is from the PARSEME (PARSing and Multi-word Expressions) EU Project. This is a small collection of only 871 pairs of English-German MWEs. In this paper, we present multi-lingual and bilingual MWE corpora that we have extracted from root parallel corpora. Our collections are 3,159,226 and 143,042 bilingual MWE pairs for German-English and Chinese-English respectively after filtering. We examine the quality of these extracted bilingual MWEs in MT experiments. Our initial experiments applying MWEs in MT show improved translation performances on MWE terms in qualitative analysis and better general evaluation scores in quantitative analysis, on both German-English and Chinese-English language pairs. We follow a standard experimental pipeline to create our MultiMWE corpora which are available online. Researchers can use this free corpus for their own models or use them in a knowledge base as model features.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.363.pdf

 

A Myanmar (Burmese)-English Named Entity Transliteration Dictionary

Aye Myat Mon, Chenchen Ding, Hour Kaing, Khin Mar Soe, Masao Utiyama and Eiichiro Sumita

Transliteration is generally a phonetically based transcription across different writing systems. It is a crucial task for various downstream natural language processing applications. For the Myanmar (Burmese) language, robust automatic transliteration for borrowed English words is a challenging task because of the complex Myanmar writing system and the lack of data. In this study, we constructed a Myanmar-English named entity dictionary containing more than eighty thousand transliteration instances. The data have been released under a CC BY-NC-SA license. We evaluated the automatic transliteration performance using statistical and neural network-based approaches based on the prepared data. The neural network model outperformed the statistical model significantly in terms of the BLEU score on the character level. Different units used in the Myanmar script for processing were also compared and discussed.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.364.pdf

 

CA-EHN: Commonsense Analogy from E-HowNet

Peng-Hsuan Li, Tsan-Yu Yang and Wei-Yun Ma

Embedding commonsense knowledge is crucial for end-to-end models to generalize inference beyond training corpora. However, existing word analogy datasets have tended to be handcrafted, involving permutations of hundreds of words with only dozens of pre-defined relations, mostly morphological relations and named entities. In this work, we model commonsense knowledge down to word-level analogical reasoning by leveraging E-HowNet, an ontology that annotates 88K Chinese words with their structured sense definitions and English translations. We present CA-EHN, the first commonsense word analogy dataset containing 90,505 analogies covering 5,656 words and 763 relations. Experiments show that CA-EHN stands out as a great indicator of how well word representations embed commonsense knowledge. The dataset is publicly available at \url{https://github.com/ckiplab/CA-EHN}.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.365.pdf

 

Building Semantic Grams of Human Knowledge

Valentina Leone, Giovanni Siragusa, Luigi Di Caro and Roberto Navigli

Word senses are typically defined with textual definitions for human consumption and, in computational lexicons, put in context via lexical-semantic relations such as synonymy, antonymy, hypernymy, etc. In this paper we embrace a radically different paradigm that provides a slot-filler structure, called “semagram”, to define the meaning of words in terms of their prototypical semantic information. We propose a semagram-based knowledge model composed of 26 semantic relationships which integrates features from a range of different sources, such as computational lexicons and property norms. We describe an annotation exercise regarding 50 concepts over 10 different categories and put forward different automated approaches for extending the semagram base to thousands of concepts. We finally evaluated the impact of the proposed resource on a semantic similarity task, showing significant improvements over state-of-the-art word embeddings.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.366.pdf

 

Automatically Building a Multilingual Lexicon of False Friends With No Supervision

Ana Sabina Uban and Liviu P. Dinu

Cognate words, defined as words in different languages which derive from a common etymon, can be useful for language learners, who can leverage the orthographical similarity of cognates to more easily understand a text in a foreign language. Deceptive cognates, or false friends, do not share the same meaning anymore; these can be instead deceiving and detrimental for language acquisition or text understanding in a foreign language. We use an automatic method of detecting false friends from a set of cognates, in a fully unsupervised fashion, based on cross-lingual word embeddings. We implement our method for English and five Romance languages, including a low-resource language (Romanian), and evaluate it against two different gold standards. The method can be extended easily to any language pair, requiring only large monolingual corpora for the involved languages and a small bilingual dictionary for the pair. We additionally propose a measure of "falseness" of a false friends pair. We publish freely the database of false friends in the six languages, along with the falseness scores for each cognate pair. The resource is the largest of the kind that we are aware of, both in terms of languages covered and number of word pairs.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.367.pdf

 

A Parallel WordNet for English, Swedish and Bulgarian

Krasimir Angelov

We present the parallel creation of a WordNet resource for Swedish and Bulgarian which is tightly aligned with the Princeton WordNet. The alignment is not only on the synset level, but also on word level, by matching words with their closest translations in each language. We argue that the tighter alignment is essential in machine translation and natural language generation. About one-fifth of the lexical entries are also linked to the corresponding Wikipedia articles. In addition to the traditional semantic relations in WordNet, we also integrate morphological and morpho-syntactic information. The resource comes with a corpus where examples from Princeton WordNet are translated to Swedish and Bulgarian. The examples are aligned on word and phrase level. The new resource is open-source and in its development we used only existing open-source resources.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.368.pdf

 

ENGLAWI: From Human- to Machine-Readable Wiktionary

Franck Sajous, Basilio Calderone and Nabil Hathout

This paper introduces ENGLAWI, a large, versatile, XML-encoded machine-readable dictionary extracted from Wiktionary. ENGLAWI contains 752,769 articles encoding the full body of information included in Wiktionary: simple words, compounds and multiword expressions, lemmas and inflectional paradigms, etymologies, phonemic transcriptions in IPA, definition glosses and usage examples, translations, semantic and morphological relations, spelling variants, etc. It is fully documented, released under a free license and supplied with G-PeTo, a series of scripts allowing easy information extraction from ENGLAWI. Additional resources extracted from ENGLAWI, such as an inflectional lexicon, a lexicon of diatopic variants and the inclusion dates of headwords in Wiktionary’s nomenclature are also provided. The paper describes the content of the resource and illustrates how it can be - and has been - used in previous studies. We finally introduce an ongoing work that computes lexicographic word embeddings from ENGLAWI’s definitions.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.369.pdf

 

Opening the Romance Verbal Inflection Dataset 2.0: A CLDF lexicon

Sacha Beniamine, Martin Maiden and Erich Round

We introduce the Romance Verbal Inflection Dataset 2.0, a multilingual lexicon of Romance inflection covering 74 varieties. The lexicon provides verbal paradigm forms in broad IPA phonemic notation. Both lexemes and paradigm cells are organized to reflect cognacy. Such multi-lingual inflected lexicons annotated for two dimensions of cognacy are necessary to study the evolution of inflectional paradigms, and test linguistic hypotheses systematically. However, these resources seldom exist, and when they do, they are not usually encoded in computationally usable ways. The Oxford Online Database of Romance Verb Morphology provides this kind of information, however, it is not maintained anymore and is only available as a web service without interfaces for machine-readability. We collect its data and clean and correct it for consistency using both heuristics and expert annotator judgements. Most resources used to study language evolution computationally rely strictly on multilingual contemporary information, and lack information about prior stages of the languages. To provide such information, we augment the database  with Latin paradigms from the LatInFlexi lexicon. Finally, to make it widely avalable, the resource is released under a GPLv3 license in CLDF format.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.370.pdf

 

word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs

Yo Joong Choe, Kyubyong Park and Dongwoo Kim

We present word2word, a publicly available dataset and an open-source Python package for cross-lingual word translations extracted from sentence-level parallel corpora. Our dataset provides top-k word translations in 3,564 (directed) language pairs across 62 languages in OpenSubtitles2018 (Lison et al., 2018). To obtain this dataset, we use a count-based bilingual lexicon extraction model based on the observation that not only source and target words but also source words themselves can be highly correlated. We illustrate that the resulting bilingual lexicons have high coverage and attain competitive translation quality for several language pairs. We wrap our dataset and model in an easy-to-use Python library, which supports downloading and retrieving top-k word translations in any of the supported language pairs as well as computing top-k word translations for custom parallel corpora.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.371.pdf

 

Introducing Lexical Masks: a New Representation of Lexical Entries for Better Evaluation and Exchange of Lexicons

Bruno Cartoni, Daniel Calvelo Aros, Denny Vrandecic and Saran Lertpradit

The evaluation and exchange of large lexicon databases remains a challenge in many NLP applications.  Despite the existence of commonly accepted standards for the format and the features used in a lexicon, there is still a lack of precise and interoperable specification requirements about how lexical entries of a particular language should look like, both in terms of the numbers of forms and in terms of features associated with these forms. This paper presents the notion of “lexical masks”, a powerful tool used to evaluate and exchange lexicon databases in many languages.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.372.pdf

 

A Large-Scale Leveled Readability Lexicon for Standard Arabic

Muhamed Al Khalil, Nizar Habash and Zhengyang Jiang

We present a large-scale 26,000-lemma leveled readability lexicon for Modern Standard Arabic. The lexicon was manually annotated in triplicate by language professionals from three regions in the Arab world. The annotations show a high degree of agreement; and major differences were limited to regional variations. Comparing lemma readability levels with their frequencies provided good insights in the benefits and pitfalls of frequency-based readability approaches. The lexicon will be publicly available.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.373.pdf

 

Preserving Semantic Information from Old Dictionaries: Linking Senses of the 'Altfranzösisches Wörterbuch' to WordNet

Achim Stein

Historical dictionaries of the pre-digital period are important resources for the study of older languages. Taking the example of the 'Altfranzösisches Wörterbuch', an Old French dictionary published from 1925 onwards, this contribution shows how the printed dictionaries can be turned into a more easily accessible and more sustainable lexical database, even though a full-text retro-conversion is too costly. Over 57,000 German sense definitions were identified in uncorrected OCR output. For verbs and nouns, 34,000 senses of more than 20,000 lemmas were matched with GermaNet, a semantic network for German, and, in a second step, linked to synsets of the English WordNet. These results are relevant for the automatic processing of Old French, for the annotation and exploitation of Old French text corpora, and for the philological study of Old French in general.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.374.pdf

 

Cifu: a Frequency Lexicon of Hong Kong Cantonese

Regine Lai and Grégoire Winterstein

This paper introduces Cifu, a lexical database for Hong Kong Cantonese (HKC) that offers phonological and orthographic information, frequency measures, and lexical neighborhood information for lexical items in HKC. Cifu is of use for NLP applications and the design and analysis of psycholinguistics experiments on HKC. We elaborate on the characteristics and challenges specific to HKC that were relevant in the design of Cifu. This includes lexical, orthographic and phonological aspects of HKC, word segmentation issues, the place of HKC in written media, and the availability of data. We discuss the measure of Neighborhood Density (ND), highlighting how the analytic nature of Cantonese and its writing system affect that measure. We justify using six different variations of ND, based on the possibility of inserting or deleting phonemes when searching for neighbors and on the choice of data for retrieving frequencies. Statistics about the four genres (written, adult spoken, children spoken and child-directed) within the dataset are discussed. We find that the lexical diversity of the child-directed speech genre is particularly low, compared to a size-matched written corpus. The correlations of word frequencies of different genres are all high, but in generally decrease as word length increases.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.375.pdf

 

Odi et Amo. Creating, Evaluating and Extending Sentiment Lexicons for Latin.

Rachele Sprugnoli, Marco Passarotti, Daniela Corbetta and Andrea Peverelli

Sentiment lexicons are essential for developing automatic sentiment analysis systems, but the resources currently available mostly cover modern languages. Lexicons for ancient languages are few and not evaluated with high-quality gold standards. However, the study of attitudes and emotions in ancient texts is a growing field of research which poses specific issues (e.g., lack of native speakers, limited amount of data, unusual textual genres for the sentiment analysis task, such as philosophical or documentary texts) and can have an impact on the work of scholars coming from several disciplines besides computational linguistics, e.g. historians and philologists. The work presented in this paper aims at providing the research community with a set of sentiment lexicons built by taking advantage of manually-curated resources belonging to the long tradition of Latin corpora and lexicons creation. Our interdisciplinary approach led us to release: i) two automatically generated sentiment lexicons; ii) a gold standard developed by two Latin language and culture experts; iii) a silver standard in which semantic and derivational relations are exploited so to extend the list of lexical items of the gold standard. In addition, the evaluation procedure is described together with a first application of the lexicons to a Latin tragedy.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.376.pdf

 

WordWars: A Dataset to Examine the Natural Selection of Words

Saif M. Mohammad

There is a growing body of work on how word meaning changes over time: mutation. In contrast, there is very little work on how different words compete to represent the same meaning, and how the degree of success of words in that competition changes over time: natural selection. We present a new dataset,  WordWars, with historical frequency data from the early 1800s to the early 2000s for monosemous English words in over 5000 synsets. We explore three broad questions with the dataset: (1) what is the degree to which predominant words in these synsets have changed, (2) how do prominent word features such as frequency, length, and concreteness impact natural selection, and (3) what are the differences between the predominant words of the 2000s and the predominant words of early 1800s. We show that close to one third of the synsets undergo a change in the predominant word in this time period. Manual annotation of these pairs shows that about 15% of these are orthographic variations, 25% involve affix changes, and 60% have completely different roots. We find that frequency, length, and concreteness all impact natural selection, albeit in different ways.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.377.pdf

 

Challenge Dataset of Cognates and False Friend Pairs from Indian Languages

Diptesh Kanojia, Malhar Kulkarni, Pushpak Bhattacharyya and Gholamreza Haffari

Cognates are present in multiple variants of the same text across different languages (e.g., "hund" in German and "hound" in the English language mean "dog"). They pose a challenge to various Natural Language Processing (NLP) applications such as Machine Translation, Cross-lingual Sense Disambiguation, Computational Phylogenetics, and Information Retrieval. A possible solution to address this challenge is to identify cognates across language pairs. In this paper, we describe the creation of two cognate datasets for twelve Indian languages namely Sanskrit, Hindi, Assamese, Oriya, Kannada, Gujarati, Tamil, Telugu, Punjabi, Bengali, Marathi, and Malayalam. We digitize the cognate data from an Indian language cognate dictionary and utilize linked Indian language Wordnets to generate cognate sets. Additionally, we use the Wordnet data to create a False Friends' dataset for eleven language pairs. We also evaluate the efficacy of our dataset using previously available baseline cognate detection approaches. We also perform a manual evaluation with the help of lexicographers and release the curated gold-standard dataset with this paper.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.378.pdf

 

Development of a Japanese Personality Dictionary based on Psychological Methods

Ritsuko Iwai, Daisuke Kawahara, Takatsune Kumada and Sadao Kurohashi

We propose a new approach to constructing a personality dictionary with psychological evidence. In this study, we collect personality words, using word embeddings, and construct a personality dictionary with weights for Big Five traits. The weights are calculated based on the responses of the large sample (N=1,938, female = 1,004, M=49.8years old:20-78, SD=16.3). All the respondents answered a 20-item personality questionnaire and 537 personality items derived from word embeddings. We present the procedures to examine the qualities of responses with psychological methods and to calculate the weights. These result in a personality dictionary with two sub-dictionaries. We also discuss an application of the acquired resources.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.379.pdf

 

A Lexicon-Based Approach for Detecting Hedges in Informal Text

Jumayel Islam, Lu Xiao and Robert E. Mercer

Hedging is a commonly used strategy in conversational management to show the speaker's lack of commitment to what they communicate, which may signal problems between the speakers. Our project is interested in examining the presence of hedging words and phrases in identifying the tension between an interviewer and interviewee during a survivor interview. While there have been studies on hedging detection in the natural language processing literature, all existing work has focused on structured texts and formal communications. Our project thus investigated a corpus of eight unstructured conversational interviews about the Rwanda Genocide and identified hedging patterns in the interviewees' responses. Our work produced three manually constructed lists of hedge words, booster words, and hedging phrases. Leveraging these lexicons, we developed a rule-based algorithm that detects sentence-level hedges in informal conversations such as survivor interviews. Our work also produced a dataset of 3000 sentences having the categories Hedge and Non-hedge annotated by three researchers. With experiments on this annotated dataset, we verify the efficacy of our proposed algorithm. Our work contributes to the further development of tools that identify hedges from informal conversations and discussions.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.380.pdf

 

Word Complexity Estimation for Japanese Lexical Simplification

Daiki Nishihara and Tomoyuki Kajiwara

We introduce three language resources for Japanese lexical simplification: 1) a large-scale word complexity lexicon, 2) the first synonym lexicon for converting complex words to simpler ones, and 3) the first toolkit for developing and benchmarking Japanese lexical simplification system. Our word complexity lexicon is expanded to a broader vocabulary using a classifier trained on a small, high-quality word complexity lexicon created by Japanese language teachers. Based on this word complexity estimator, we extracted simplified word pairs from a large-scale synonym lexicon and constructed a simplified synonym lexicon useful for lexical simplification. In addition, we developed a Python library that implements automatic evaluation and key methods in each subtask to ease the construction of a lexical simplification pipeline. Experimental results show that the proposed method based on our lexicon achieves the highest performance of Japanese lexical simplification. The current lexical simplification is mainly studied in English, which is rich in language resources such as lexicons and toolkits. The language resources constructed in this study will help advance the lexical simplification system in Japanese.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.381.pdf

 

Inducing Universal Semantic Tag Vectors

Da Huo and Gerard de Melo

Given the well-established usefulness of part-of-speech tag annotations in many syntactically oriented downstream NLP tasks, the recently proposed notion of semantic tagging (Bjerva et al. 2016) aims at tagging words with tags informed by semantic distinctions, which are likely to be useful across a range of semantic tasks. To this end, their annotation scheme distinguishes, for instance, privative attributes from subsective ones. While annotated corpora exist, their size is limited and thus many words are out-of-vocabulary words. In this paper, we study to what extent we can automatically predict the tags associated with unseen words. We draw on large-scale word representation data to derive a large new Semantic Tag lexicon. Our experiments show that we can infer semantic tags for words with high accuracy both monolingually and cross-lingually.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.382.pdf

 

LexiDB: Patterns & Methods for Corpus Linguistic Database Management

Matthew Coole, Paul Rayson and John Mariani

LexiDB is a tool for storing, managing and querying corpus data. In contrast to other database management systems (DBMSs), it is designed specifically for text corpora. It improves on other corpus management systems (CMSs) because data can be added and deleted from corpora on the fly with the ability to add live data to existing corpora. LexiDB sits between these two categories of DBMSs and CMSs, more specialised to language data than a general purpose DBMS but more flexible than a traditional static corpus management system. Previous work has demonstrated the scalability of LexiDB in response to the growing need to be able to scale out for ever growing corpus datasets. Here, we present the patterns and methods developed in LexiDB for storage, retrieval and querying of multi-level annotated corpus data. These techniques are evaluated and compared to an existing CMS (Corpus Workbench CWB - CQP) and indexer (Lucene). We find that LexiDB consistently outperforms existing tools for corpus queries. This is particularly apparent with large corpora and when handling queries with large result sets.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.383.pdf

 

Towards a Semi-Automatic Detection of Reflexive and Reciprocal Constructions and Their Representation in a Valency Lexicon

Václava Kettnerová, Marketa Lopatkova, Anna Vernerová and Petra Barancikova

Valency lexicons usually describe valency behavior of verbs in non-reflexive and non-reciprocal constructions. However, reflexive and reciprocal constructions are common morphosyntactic forms of verbs. Both of these constructions are characterized by regular changes in morphosyntactic properties of verbs, thus they can be described by grammatical rules. On the other hand, the possibility to create reflexive and/or reciprocal constructions cannot be trivially derived from the morphosyntactic structure of verbs as it is conditioned by their semantic properties as well. A large-coverage valency lexicon allowing for rule based generation of all well formed verb constructions should thus integrate the information on reflexivity and reciprocity. In this paper, we propose a semi-automatic procedure, based on grammatical constraints on reflexivity and reciprocity, detecting those verbs that form reflexive and reciprocal constructions in corpus data. However, exploitation of corpus data for this purpose is complicated due to the diverse functions of reflexive markers crossing the domain of reflexivity and reciprocity. The list of verbs identified by the previous procedure is thus further used in an automatic experiment, applying word embeddings for detecting semantically similar verbs. These candidate verbs have been manually verified and annotation of their reflexive and reciprocal constructions has been integrated into the valency lexicon of Czech verbs VALLEX.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.384.pdf

 

Languages Resources for Poorly Endowed Languages : The Case Study of Classical Armenian

Chahan Vidal-Gorène and Aliénor Decours-Perez

Classical Armenian is a poorly endowed language, that despite a great tradition of lexicographical erudition is coping with a lack of resources. Although numerous initiatives exist to preserve the Classical Armenian language, the lack of precise and complete grammatical and lexicographical resources remains. This article offers a situation analysis of the existing resources for Classical Armenian and presents the new digital resources provided on the Calfa platform. The Calfa project gathers existing resources and updates, enriches and enhances their content to offer the richest database for Classical Armenian today. Faced with the challenges specific to a poorly endowed language, the Calfa project is also developing new technologies and solutions to enable preservation, advanced research, and larger systems and developments for the Armenian language

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.385.pdf

 

Constructing Web-Accessible Semantic Role Labels and Frames for Japanese as Additions to the NPCMJ Parsed Corpus

Koichi Takeuchi, Alastair Butler, Iku Nagasaki, Takuya Okamura and Prashant Pardeshi

As part of constructing the NINJAL Parsed Corpus of Modern Japanese (NPCMJ), a web-accessible language resource, we are adding frame information for predicates, together with two types of semantic role labels that mark the contributions of arguments. One role type consists of numbered semantic roles, like in PropBank, to capture relations between arguments in different syntactic patterns. The other role type consists of semantic roles with conventional names.Both role types are compatible with hierarchical frames that belong to related predicates. Adding semantic role and frame information to the NPCMJ will support a web environment where language learners and linguists can search examples of Japanese for syntactic and semantic features. The annotation will also provide a language resource for NLP researchers making semantic parsing models (e.g., for AMR parsing) following machine learning approaches. In this paper, we describe how the two types of semantic role labels are defined under the frame based approach, i.e., both types can be consistently applied when linked to corresponding frames. Then we show special cases of syntactic patterns and the current status of the annotation work.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.386.pdf

 

Large-scale Cross-lingual Language Resources for Referencing and Framing

Piek Vossen, Filip Ilievski, Marten Postma, Antske Fokkens, Gosse Minnema and Levi Remijnse

In this article, we lay out the basic ideas and principles of the project Framing Situations in the Dutch Language. We provide our first results of data acquisition, together with the first data release. We introduce the notion of cross-lingual referential corpora. These corpora consist of texts that make reference to exactly the same incidents. The referential grounding allows us to analyze the framing of these incidents in different languages and across different texts. During the project, we will use the automatically generated data to study linguistic framing as a phenomenon, build framing resources such as lexicons and corpora. We expect to capture larger variation in framing compared to traditional approaches for building such resources. Our first data release, which contains structured data about a large number of incidents and reference texts, can be found at http://dutchframenet.nl/data-releases/.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.387.pdf

 

Modelling Etymology in LMF/TEI: The Grande Dicionário Houaiss da Língua Portuguesa Dictionary as a Use Case

Fahad Khan, Laurent Romary, Ana Salgado, Jack Bowers, Mohamed Khemakhem and Toma Tasovac

In this article we will introduce two of the new parts of the new multi-part version of the Lexical Markup Framework (LMF) ISO standard, namely part 3 of the standard (ISO 24613-3), which deals with etymological and diachronic data, and Part 4 (ISO 24613-4), which consists of a TEI serialisation of all of the prior parts of the model. We will demonstrate the use of both standards by describing the LMF encoding of a small number of examples taken from a sample conversion of the reference Portuguese dictionary  \textit{Grande Dicionário Houaiss da Língua Portuguesa}, part of a broader experiment comprising the analysis of different, heterogeneously encoded, Portuguese lexical resources. We present the examples in the Unified Modelling Language (UML) and also in a couple of cases in TEI.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.388.pdf

 

Linking the TUFS Basic Vocabulary to the Open Multilingual Wordnet

Francis Bond, Hiroki Nomoto, Luís Morgado da Costa and Arthur Bond

We describe the linking of the TUFS Basic Vocabulary Modules, created for online language learning, with the Open Multilingual Wordnet. The TUFS modules have roughly 500 lexical entries in 30 languages, each with the lemma, a link across the languages, an example sentence, usage notes and sound files. The Open Multilingual Wordnet has 34 languages (11 shared with TUFS) organized into synsets linked by semantic relations, with examples and definitions for some languages. The links can be used to (i) evaluate existing wordnets, (ii) add data to these wordnets and (iii) create new open wordnets for Khmer, Korean, Lao, Mongolian, Russian, Tagalog, Urdua nd Vietnamese

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.389.pdf

 

Some Issues with Building a Multilingual Wordnet

Francis Bond, Luis Morgado da Costa, Michael Wayne Goodman, John Philip McCrae and Ahti Lohk

In this paper we discuss the experience of bringing together over 40 different wordnets. We introduce some extensions to the GWA wordnet LMF format proposed in Vossen et al. (2016) and look at how this new information can be displayed. Notable extensions include: confidence, corpus frequency, orthographic variants, lexicalized and non-lexicalized synsets and lemmas, new parts of speech, and more. Many of these extensions already exist in multiple wordnets – the challenge was to find a compatible representation. To this end, we introduce a new version of the Open Multilingual Wordnet (Bond and Foster, 2013), that integrates a new set of tools that tests the extensions introduced by this new format, while also ensuring the integrity of the Collaborative Interlingual Index (CILI: Bond et al., 2016), avoiding the same new concept to be introduced through multiple projects.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.390.pdf

 

Collocations in Russian Lexicography and Russian Collocations Database

Maria Khokhlova

The paper presents the issue of collocability and collocations in Russian and gives a survey of a wide range of dictionaries both printed and online ones that describe collocations. Our project deals with building a database that will include dictionary and statistical collocations. The former can be described in various lexicographic resources whereas the latter can be extracted automatically from corpora. Dictionaries differ among themselves, the information is given in various ways, making it hard for language learners and researchers to acquire data. A number of dictionaries were analyzed and processed to retrieve verified collocations, however the overlap between the lists of collocations extracted from them is still rather small. This fact indicates there is a need to create a unified resource which takes into account collocability and more examples. The proposed resource will also be useful for linguists and for studying Russian as a foreign language. The obtained results can be important for machine learning and for other NLP tasks, for instance, automatic clustering of word combinations and disambiguation.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.391.pdf

 

Methodological Aspects of Developing and Managing an Etymological Lexical Resource: Introducing EtymDB-2.0

Clémentine Fourrier and Benoît Sagot

Diachronic lexical information is not only important in the field of historical linguistics, but is also increasingly used in NLP, most recently for machine translation of low resource languages. Therefore, there is a need for fine-grained, large-coverage and accurate etymological lexical resources. In this paper, we propose a set of guidelines to generate such resources, for each step of the life-cycle of an etymological lexicon: creation, update, evaluation, dissemination, and exploitation.  To illustrate the guidelines, we introduce EtymDB 2.0, an etymological database automatically generated from the Wiktionary, which contains 1.8 million lexemes, linked by more than 700,000 fine-grained etymological relations, across 2,536 living and dead languages. We also introduce use cases for which EtymDB 2.0 could represent a key resource, such as phylogenetic tree generation, low resource machine translation or medieval languages study.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.392.pdf

 

OFrLex: A Computational Morphological and Syntactic Lexicon for Old French

Gaël Guibon and Benoît Sagot

In this paper we describe our work on the development and enrichment of OFrLex, a freely available, large-coverage morphological and syntactic Old French lexicon. We rely on several heterogeneous language resources to extract structured and exploitable information. The extraction follows a semi-automatic procedure with substantial manual steps to respond to difficulties encountered while aligning lexical entries from distinct language resources. OFrLex aims at improving natural language processing tasks on Old French such as part-of-speech tagging and dependency parsing. We provide quantitative information on OFrLex and discuss its reliability. We also describe and evaluate a semi-automatic, word-embedding-based lexical enrichment process aimed at increasing the accuracy of the resource. Results of this extension technique will be manually validated in the near future, a step that will take advantage of OFrLex's viewing, searching and editing interface, which is already accessible online.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.393.pdf

 

Automatic Reconstruction of Missing Romanian Cognates and Unattested Latin Words

Alina Maria Ciobanu, Liviu P. Dinu and Laurentiu Zoicas

Producing related words is a key concern in historical linguistics. Given an input word, the task is to automatically produce either its proto-word, a cognate pair or a modern word derived from it. In this paper, we apply a method for producing related words based on sequence labeling, aiming to fill in the gaps in incomplete cognate sets in Romance languages with Latin etymology (producing Romanian cognates that are missing) and to reconstruct uncertified Latin words. We further investigate an ensemble-based aggregation for combining and re-ranking the word productions of multiple languages.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.394.pdf

 

A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment

Sina Ahmadi, John Philip McCrae, Sanni Nimb, Fahad Khan, Monica Monachini, Bolette Pedersen, Thierry Declerck, Tanja Wissik, Andrea Bellandi, Irene Pisani, Thomas Troelsgård, Sussi Olsen, Simon Krek, Veronika Lipp, Tamás Váradi, László Simon, András Gyorf

Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment is carried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships such as broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide range of languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data will pave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriously requiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.395.pdf

 

A Broad-Coverage Deep Semantic Lexicon for Verbs

James Allen, Hannah An, Ritwik Bose, Will de Beaumont and Choh Man Teng

Progress on deep language understanding is inhibited by the lack of a broad coverage lexicon that connects linguistic behavior to ontological concepts and axioms. We have developed COLLIE-V, a deep lexical resource for verbs, with the coverage of WordNet and syntactic and semantic details that meet or exceed existing resources. Bootstrapping from a hand-built lexicon and ontology, new ontological concepts and lexical entries, together with semantic role preferences and entailment axioms, are automatically derived by combining multiple constraints from parsing dictionary definitions and examples. We evaluated the accuracy of the technique along a number of different dimensions and were able to obtain high accuracy in deriving new concepts and lexical entries. COLLIE-V is publicly available.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.396.pdf

 

Computational Etymology and Word Emergence

Winston Wu and David Yarowsky

We developed an extensible, comprehensive Wiktionary parser that improves over several existing parsers. We predict the etymology of a word across the full range of etymology types and languages in Wiktionary, showing improvements over a strong baseline. We also model word emergence and show the application of etymology in modeling this phenomenon. We release our parser to further research in this understudied field.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.397.pdf

 

A Dataset of Translational Equivalents Built on the Basis of plWordNet-Princeton WordNet Synset Mapping

Ewa Rudnicka and Tomasz Naskręt

The paper presents a dataset of 11,000 Polish-English translational equivalents in the form of pairs of plWordNet and Princeton WordNet lexical units linked by three types of equivalence links: strong equivalence, regular equivalence, and weak equivalence. The resource consists of the two subsets. The first subset was built in result of manual annotation of an extended sample of Polish-English sense pairs partly randomly extracted from synsets linked by interlingual relations such as I-synononymy, I-partial synonymy and I-hyponymy and partly manually selected from the surrounding synsets in the hypernymy hierarchy. The second subset was created as a result of the manual checkup of an automatically generated lists of pairs of sense equivalents on the basis of a couple of simple, rule-based heuristics. For both subsets, the same methodology of equivalence annotation was adopted based on the verification of a set of formal, semantic-pragmatic and translational features. The constructed dataset is a novum in the wordnet domain and can facilitate the precision of bilingual NLP tasks such as automatic translation, bilingual word sense disambiguation and sentiment annotation.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.398.pdf

 

TRANSLIT: A Large-scale Name Transliteration Resource

Fernando Benites, Gilbert François Duivesteijn, Pius von Däniken and Mark Cieliebak

Transliteration is the process of expressing a proper name from a source language in the characters of a target language (e.g. from Cyrillic to Latin characters). We present TRANSLIT, a large-scale corpus with approx. 1.6 million entries in more than 180 languages with about 3 million variations of person and geolocation names. The corpus is based on various public data sources, which have been transformed into a unified format to simplify their usage, plus a newly compiled dataset from Wikipedia. In addition, we apply several machine learning methods to establish baselines for automatically detecting transliterated names in various languages. Our best systems achieve an accuracy of 92% on identification of transliterated pairs.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.399.pdf

 

Computing with Subjectivity Lexicons

Caio L. M. Jeronimo, Claudio E. C. Campelo, Leandro Balby Marinho, Allan Sales, Adriano Veloso and Roberta Viola

In this paper, we introduce a new set of lexicons for expressing subjectivity in text documents written in Brazilian Portuguese. Besides the non-English idiom, in contrast to other subjectivity lexicons available, these lexicons represent different subjectivity dimensions (other than sentiment) and are more compact in number of terms. This last feature was designed intentionally to leverage the power of word embedding techniques, i.e., with the words mapped to an embedding space and the appropriate distance measures, we can easily capture semantically related words to the ones in the lexicons. Thus, we do not need to build comprehensive vocabularies and can focus on the most representative words for each lexicon dimension. We showcase the use of these lexicons in three highly non-trivial tasks: (1) Automated Essay Scoring in the Presence of Biased Ratings, (2) Subjectivity Bias in Brazilian Presidential Elections and (3) Fake News Classification Based on Text Subjectivity. All these tasks involve text documents written in Portuguese.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.400.pdf

 

The ACoLi Dictionary Graph

Christian Chiarcos, Christian Fäth and Maxim Ionov

In this paper, we report the release of the ACoLi Dictionary Graph, a large-scale collection of multilingual open source dictionaries available in two machine-readable formats, a graph representation in RDF, using the OntoLex-Lemon vocabulary, and a simple tabular data format to facilitate their use in NLP tasks, such as translation inference across dictionaries. We describe the mapping and harmonization of the underlying data structures into a unified representation, its serialization in RDF and TSV, and the release of a massive and coherent amount of lexical data under open licenses.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.401.pdf

 

LR National, International Projects, Infrastructural, Policy issues

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Resources in Underrepresented Languages: Building a Representative Romanian Corpus

Ludmila Midrigan - Ciochina, Victoria Boyd, Lucila Sanchez-Ortega, Diana Malancea_Malac, Doina Midrigan and David P. Corina

The effort in the field of Linguistics to develop theories that aim to explain language-dependent effects on language processing is greatly facilitated by the availability of reliable resources representing different languages. This project presents a detailed description of the process of creating a large and representative corpus in Romanian – a relatively under-resourced language with unique structural and typological characteristics, that can be used as a reliable language resource for linguistic studies. The decisions that have guided the construction of the corpus, including the type of corpus, its size and component resource files are discussed. Issues related to data collection, data organization and storage, as well as characteristics of the data included in the corpus are described. Currently, the corpus has approximately 5,500,000 tokens originating from written text and 100,000 tokens of spoken language.  it includes language samples that represent a wide variety of registers (i.e. written language - 16 registers and 5 registers of spoken language), as well as different authors and speakers

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.402.pdf

 

World Class Language Technology - Developing a Language Technology Strategy for Danish

Sabine Kirchmeier, Bolette Pedersen, Sanni Nimb, Philip Diderichsen and Peter Juel Henrichsen

Although Denmark is one of the most digitized countries in Europe, no coordinated efforts have been made in recent years to support the Danish language with regard to language technology and artificial intelligence. In March 2019, however, the Danish government adopted a new, ambitious strategy for LT and artificial intelligence. In this paper, we describe the process behind the development of the language-related parts of the strategy: A Danish Language Technology Committee was constituted and a comprehensive series of workshops were organized in which users, suppliers, developers, and researchers gave their valuable input based on their experiences.  We describe how, based on this experience, the focus areas and recommendations for the LT strategy were established, and which steps are currently taken in order to put the strategy into practice.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.403.pdf

 

A Corpus for Automatic Readability Assessment and Text Simplification of German

Alessia Battisti, Dominik Pfütze, Andreas Säuberli, Marek Kostrzewa and Sarah Ebling

In this paper, we present a corpus for use in automatic readability assessment and automatic text simplification for German, the first of its kind for this language. The corpus is compiled from web sources and consists of parallel as well as monolingual-only (simplified German) data amounting to approximately 6,200 documents (nearly 211,000 sentences). As a unique feature, the corpus contains information on text structure (e.g., paragraphs, lines), typography (e.g., font type, font style), and images (content, position, and dimensions). While the importance of considering such information in machine learning tasks involving simplified language, such as readability assessment, has repeatedly been stressed in the literature, we provide empirical evidence for its benefit. We also demonstrate the added value of leveraging monolingual-only data for automatic text simplification via machine translation through applying back-translation, a data augmentation technique.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.404.pdf

 

The CLARIN Knowledge Centre for Atypical Communication Expertise

Henk van den Heuvel, Nelleke Oostdijk, Caroline Rowland and Paul Trilsbeek

This paper introduces a new CLARIN Knowledge Center which is the K-Centre for Atypical Communication Expertise (ACE for short) which has been established at the Centre for Language and Speech Technology (CLST) at Radboud University. Atypical communication is an umbrella term used here to denote language use by second language learners, people with language disorders or those suffering from language disabilities, but also more broadly by bilinguals and users of sign languages. It involves multiple modalities (text, speech, sign, gesture) and encompasses different developmental stages. ACE closely collaborates with The Language Archive (TLA) at the Max Planck Institute for Psycholinguistics in order to safeguard GDPR-compliant data storage and access. We explain the mission of ACE and show its potential on a number of showcases and a use case.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.405.pdf

 

Corpora of Disordered Speech in the Light of the GDPR: Two Use Cases from the DELAD Initiative

Henk van den Heuvel, Aleksei Kelli, Katarzyna Klessa and Satu Salaasti

Corpora of disordered speech (CDS) are costly to collect and difficult to share due to personal data protection and intellectual property (IP) issues. In this contribution we discuss the legal grounds for processing CDS in the light of the GDPR, and illustrate these with two use cases from the DELAD context. One use case deals with clinical datasets and another with legacy data from Polish hearing-impaired children. For both cases, processing based on consent and on public interest are taken into consideration.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.406.pdf

 

The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe

Georg Rehm, Katrin Marheinecke, Stefanie Hegele, Stelios Piperidis, Kalina Bontcheva, Jan Hajic, Khalid Choukri, Andrejs Vasiļjevs, Gerhard Backfried, Christoph Prinz, Jose Manuel Gomez-Perez, Luc Meertens, Paul Lukowicz, Josef van Genabith, Andrea Lösch,

Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe’s specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI – including many opportunities, synergies but also misconceptions – has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.407.pdf

 

A Framework for Shared Agreement of Language Tags beyond ISO 639

Frances Gillis-Webber and Sabine Tittel

The identification and annotation of languages in an unambiguous and standardized way is essential for the description of linguistic data. It is the prerequisite for machine-based interpretation, aggregation, and re-use of the data with respect to different languages. This makes it a key aspect especially for Linked Data and the multilingual Semantic Web. The standard for language tags is defined by IETF’s BCP 47 and ISO 639 provides the language codes that are the tags’ main constituents. However, for the identification of lesser-known languages, endangered languages, regional varieties or historical stages of a language, the ISO 639 codes are insufficient.  Also, the optional language sub-tags compliant with BCP 47 do not offer a possibility fine-grained enough to represent linguistic variation. We propose a versatile pattern that extends the BCP 47 sub-tag 'privateuse' and is, thus, able to overcome the limits of BCP 47 and ISO 639. Sufficient coverage of the pattern is demonstrated with the use case of linguistic Linked Data of the endangered Gascon language. We show how to use a URI shortcode for the extended sub-tag, making the length compliant with BCP 47.  We achieve this with a web application and API developed to encode and decode the language tag.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.408.pdf

 

Gigafida 2.0: The Reference Corpus of Written Standard Slovene

Simon Krek, Špela Arhar Holdt, Tomaž Erjavec, Jaka Čibej, Andraz Repar, Polona Gantar, Nikola Ljubešić, Iztok Kosem and Kaja Dobrovoljc

We describe a new version of the Gigafida reference corpus of Slovene. In addition to updating the corpus with new material and annotating it with better tools, the focus of the upgrade was also on its transformation from a general reference corpus, which contains all language variants including non-standard language, to the corpus of standard (written) Slovene. This decision could be implemented as new corpora dedicated specifically to non-standard language emerged recently. In the new version, the whole Gigafida corpus was deduplicated for the first time, which facilitates automatic extraction of data for the purposes of compilation of new lexicographic resources such as the collocations dictionary and the thesaurus of Slovene.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.409.pdf

 

Corpus Query Lingua Franca part II: Ontology

Stefan Evert, Oleg Harlamov, Philipp Heinrich and Piotr Banski

The present paper outlines the projected second part of the Corpus Query Lingua Franca (CQLF) family of standards: CQLF Ontology, which is currently in the process of standardization at the International Standards Organization (ISO), in its Technical Committee 37, Subcommittee 4 (TC37SC4) and its national mirrors. The first part of the family, ISO 24623-1 (henceforth CQLF Metamodel), was successfully adopted as an international standard at the beginning of 2018. The present paper reflects the state of the CQLF Ontology at the moment of submission for the Committee Draft ballot. We provide a brief overview of the CQLF Metamodel, present the assumptions and aims of the CQLF Ontology, its basic structure, and its potential extended applications. The full ontology is expected to emerge from a community process, starting from an initial version created by the authors of the present paper.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.410.pdf

 

A CLARIN Transcription Portal for Interview Data

Christoph Draxler, Henk van den Heuvel, Arjan van Hessen, Silvia Calamai and Louise Corti

In this paper we present a first version of a transcription portal for audio files based on automatic speech recognition (ASR) in various languages. The portal is implemented in the CLARIN resources research network and intended for use by non-technical scholars. We explain the background and interdisciplinary nature of interview data, the perks and quirks of using ASR for transcribing the audio in a research context, the dos and don’ts for optimal use of the portal, and future developments foreseen. The portal is promoted in a range of workshops, but there are a number of challenges that have to be met. These challenges concern privacy issues, ASR quality, and cost, amongst others.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.411.pdf

 

Ellogon Casual Annotation Infrastructure

Georgios Petasis and Leonidas Tsekouras

This paper presents a new annotation paradigm, casual annotation, along with a proposed architecture and a reference implementation, the Ellogon Casual Annotation Tool, which implements this paradigm and architecture. The novel aspects of the proposed paradigm originate from the vision to tightly integrate annotation with the casual, everyday activities of users. Annotating in a less "controlled" environment, and removing the bottleneck of selecting content and importing it to annotation infrastructures, casual annotation provides the ability to vastly increase the content that can be annotated and ease the annotation process through automatic pre-training. The proposed paradigm, architecture and reference implementation has been evaluated for more than two years on an annotation task related to sentiment analysis. Evaluation results suggest that, at least for this annotation task, there is a huge improvement in productivity after casual annotation adoption, in comparison to the more traditional annotation paradigms followed in the early stages of the annotation task.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.412.pdf

 

European Language Grid: An Overview

Georg Rehm, Maria Berger, Ela Elsholz, Stefanie Hegele, Florian Kintzel, Katrin Marheinecke, Stelios Piperidis, Miltos Deligiannis, Dimitris Galanis, Katerina Gkirtzou, Penny Labropoulou, Kalina Bontcheva, David Jones, Ian Roberts, Jan Hajic, Jana Hamrlov

With 24 official EU and many additional languages, multilingualism in Europe and an inclusive Digital Single Market can only be enabled through Language Technologies (LTs). European LT business is dominated by hundreds of SMEs and a few large players. Many are world-class, with technologies that outperform the global players. However, European LT business is also fragmented -- by nation states, languages, verticals and sectors, significantly holding back its impact. The European Language Grid (ELG) project addresses this fragmentation by establishing the ELG as the primary platform for LT in Europe. The ELG is a scalable cloud platform, providing, in an easy-to-integrate way, access to hundreds of commercial and non-commercial LTs for all European languages, including running tools and services as well as data sets and resources. Once fully operational, it will enable the commercial and non-commercial European LT community to deposit and upload their technologies and data sets into the ELG, to deploy them through the grid, and to connect with other resources. The ELG will boost the Multilingual Digital Single Market towards a thriving European LT community, creating new jobs and opportunities. Furthermore, the ELG project organises two open calls for up to 20 pilot projects. It also sets up 32 national competence centres and the European LT Council for outreach and coordination purposes.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.413.pdf

 

The Competitiveness Analysis of the European Language Technology Market

Andrejs Vasiļjevs, Inguna Skadina, Indra Samite, Kaspars Kauliņš, Ēriks Ajausks, Jūlija Meļņika and Aivars Bērziņš

This paper presents the key results of a study on the global competitiveness of the European Language Technology market for three areas – Machine Translation, speech technology, and cross-lingual search. EU competitiveness is analyzed in comparison to North America and Asia. The study focuses on seven dimensions (research, innovations, investments, market dominance, industry, infrastructure, and Open Data) that have been selected to characterize the language technology market. The study concludes that while Europe still has strong positions in Research and Innovation, it lags behind North America and Asia in scaling innovations and conquering market share.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.414.pdf

 

Constructing a Bilingual Hadith Corpus Using a Segmentation Tool

Shatha Altammami, Eric Atwell and Ammar Alsalka

This article describes the process of gathering and constructing a bilingual parallel corpus of Islamic Hadith, which is the set of narratives reporting different aspects of the prophet Muhammad's life. The corpus data is gathered from the six canonical Hadith collections using a custom segmentation tool that automatically segments and annotates the two Hadith components with 92% accuracy. This Hadith segmenter minimises the costs of language resource creation and produces consistent results independently from previous knowledge and experiences that usually influence human annotators. The corpus includes more than 10M tokens and will be freely available via the LREC repository.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.415.pdf

 

Facilitating Corpus Usage: Making Icelandic Corpora More Accessible for Researchers and Language Users

Steinþór Steingrímsson, Starkaður Barkarson and Gunnar Thor Örnólfsson

We introduce an array of open and accessible tools to facilitate the use of the Icelandic Gigaword Corpus, in the field of Natural Language Processing as well as for students, linguists, sociologists and others benefitting from using large corpora. A KWIC engine, powered by the Swedish Korp tool is adapted to the specifics of the corpus. An n-gram viewer, highly customizable to suit different needs, allows users to study word usage throughout the period of our text collection. A frequency dictionary provides much sought after information about word frequency statistics, computed for each subcorpus as well as aggregate, disambiguating homographs based on their respective lemmas and morphosyntactic tags. Furthermore, we provide n-grams based on the corpus, and a variety of pre-trained word embeddings models, based on word2vec, GloVe, fastText and ELMo. For three of the model types, multiple word embedding models are available trained with different algorithms and using either lemmatised or unlemmatised texts.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.416.pdf

 

Interoperability in an Infrastructure Enabling Multidisciplinary Research: The case of CLARIN

Franciska de Jong, Bente Maegaard, Darja Fišer, Dieter van Uytvanck and Andreas Witt

CLARIN is a European Research Infrastructure providing access to language resources and technologies for researchers in the humanities and social sciences. It supports the use and study of language data in general and aims to increase the potential for comparative research of cultural and societal phenomena across the boundaries of languages and disciplines, all in line with the European agenda for Open Science. Data infrastructures such as CLARIN have recently embarked on the emerging frameworks for the federation of infrastructural services, such as the European Open Science Cloud and the integration of services resulting from multidisciplinary collaboration in federated services for the wider SSH domain. In this paper we describe the interoperability requirements that arise through the existing ambitions and the emerging frameworks. The interoperability theme will be addressed at several levels, including organisation and ecosystem, design of workflow services, data curation, performance measurement and collaboration.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.417.pdf

 

Language Technology Programme for Icelandic 2019-2023

Anna Nikulásdóttir, Jón Guðnason, Anton Karl Ingason, Hrafn Loftsson, Eiríkur Rögnvaldsson, Einar Freyr Sigurðsson and Steinþór Steingrímsson

In this paper, we describe a new national language technology programme for Icelandic. The programme, which spans a period of five years, aims at making Icelandic usable in communication and interactions in the digital world, by developing accessible, open-source language resources and software. The research and development work within the programme is carried out by a consortium of universities, institutions, and private companies, with a strong emphasis on cooperation between academia and industries. Five core projects will be the main content of the programme: language resources, speech recognition, speech synthesis, machine translation, and spell and grammar checking. We also describe other national language technology programmes and give an overview over the history of language technology in Iceland.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.418.pdf

 

Privacy by Design and Language Resources

Pawel Kamocki and Andreas Witt

Privacy by Design (also referred to as Data Protection by Design) is an approach in which solutions and mechanisms addressing privacy and data protection are embedded through the entire project lifecycle, from the early design stage, rather than just added as an additional lawyer to the final product. Formulated in the 1990 by the Privacy Commissionner of Ontario, the principle of Privacy by Design has been discussed by institutions and policymakers on both sides of the Atlantic, and mentioned already in the 1995 EU Data Protection Directive (95/46/EC). More recently, Privacy by Design was introduced as one of the requirements of the General Data Protection Regulation (GDPR), obliging data controllers to define and adopt, already at the conception phase, appropriate measures and safeguards to implement data protection principles and protect the rights of the data subject. Failing to meet this obligation may result in a hefty fine, as it was the case in the Uniontrad decision by the French Data Protection Authority (CNIL). The ambition of the proposed paper is to analyse the practical meaning of Privacy by Design in the context of Language Resources, and propose measures and safeguards that can be implemented by the community to ensure respect of this principle.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.419.pdf

 

Making Metadata Fit for Next Generation Language Technology Platforms: The Metadata Schema of the European Language Grid

Penny Labropoulou, Katerina Gkirtzou, Maria Gavriilidou, Miltos Deligiannis, Dimitris Galanis, Stelios Piperidis, Georg Rehm, Maria Berger, Valérie Mapelli, Michael Rigault, Victoria Arranz, Khalid Choukri, Gerhard Backfried, Jose Manuel Gomez-Perez and Andres Garcia-Silva

The current scientific and technological landscape is characterised by the increasing availability of data resources and processing tools and services. In this setting, metadata have emerged as a key factor facilitating management, sharing and usage of such digital assets. In this paper we present ELG-SHARE, a rich metadata schema catering for the description of Language Resources and Technologies (processing and generation services and tools, models, corpora, term lists, etc.), as well as related entities (e.g., organizations, projects, supporting documents, etc.). The schema powers the European Language Grid platform that aims to be the primary hub and marketplace for industry-relevant Language Technology in Europe. ELG-SHARE has been based on various metadata schemas, vocabularies, and ontologies, as well as related recommendations and guidelines.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.420.pdf

 

Related Works in the Linguistic Data Consortium Catalog

Daniel Jaquette, Christopher Cieri and Denise DiPersio

Defining relations between language resources provides an archive with the ability to better serve its users. This paper covers the development and implementation of a Related Works addition to the Linguistic Data Consortium’s (LDC) catalog. The authors go step-by-step through the development of the Related Works schema, implementation of the software and database changes, and data entry of the relations. The Related Work schema involved developing of a set of controlled terms for relations based on previous work and other schema. Software and database changes consisted of both front and back end interface additions, along with modification and additions to the LDC Catalog database tables. Data entry consisted of two parts: seed data from previous work and 2019 language resources, and ongoing legacy population. Previous work in this area is discussed as well as overview information about the LDC Catalog. A list of the full LDC Related Works terms is included with brief explanations.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.421.pdf

 

Language Data Sharing in European Public Services – Overcoming Obstacles and Creating Sustainable Data Sharing Infrastructures

Lilli Smal, Andrea Lösch, Josef van Genabith, Maria Giagkou, Thierry Declerck and Stephan Busemann

Data is key in training modern language technologies. In this paper, we summarise the findings of the first pan-European study on obstacles to sharing language data across 29 EU Member States and CEF-affiliated countries carried out under the ELRC White Paper action on Sustainable Language Data Sharing to Support Language Equality in Multilingual Europe. Why Language Data Matters. We present the methodology of the study, the obstacles identified and report on recommendations on how to overcome those. The obstacles are classified into (1) lack of appreciation of the value of language data, (2) structural challenges, (3) disposition towards CAT tools and lack of digital skills, (4) inadequate language data management practices, (5) limited access to outsourced translations, and (6) legal concerns. Recommendations are grouped into addressing the European/national policy level, and the organisational/institutional level.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.422.pdf

 

A Progress Report on Activities at the Linguistic Data Consortium Benefitting the LREC Community

Christopher Cieri, James Fiumara, Stephanie Strassel, Jonathan Wright, Denise DiPersio and Mark Liberman

This latest in a series of Linguistic Data Consortium (LDC) progress reports to the LREC community does not describe any single language resource, evaluation campaign or technology but sketches the activities, since the last report, of a data center devoted to supporting the work of LREC attendees among other research communities. Specifically, we describe 96 new corpora released in 2018-2020 to date, a new technology evaluation campaign, ongoing activities to support multiple common task human language technology programs, and innovations to advance the methodology of language data collection and annotation.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.423.pdf

 

Digital Language Infrastructures – Documenting Language Actors

Verena Lyding, Alexander König and Monica Pretti

The major European language infrastructure initiatives like CLARIN (Hinrichs and Krauwer, 2014), DARIAH (Edmond et al., 2017) or Europeana (Europeana Foundation, 2015) have been built by focusing in the first place on institutions of larger scale, like specialized research departments and larger official units like national libraries, etc. However, besides these principal players also a large number of smaller language actors could contribute to and benefit from language infrastructures. Especially since these smaller institutions, like local libraries, archives and publishers, often collect, manage and host language resources of particular value for their geographical and cultural region, it seems highly relevant to find ways of engaging and connecting them to existing European infrastructure initiatives. In this article, we first highlight the need for reaching out to smaller local language actors and discuss challenges related to this ambition. Then we present the first step in how this objective was approached within a local language infrastructure project, namely by means of a structured documentation of the local language actors landscape in South Tyrol. We describe how the documentation efforts were structured and organized, and what tool we have set up to distribute the collected data online, by adapting existing CLARIN solutions.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.424.pdf

 

Samrómur: Crowd-sourcing Data Collection for Icelandic Speech Recognition

David Erik Mollberg, Ólafur Helgi Jónsson, Sunneva Þorsteinsdóttir, Steinþór Steingrímsson, Eydís Huld Magnúsdóttir and Jon Gudnason

This contribution describes an ongoing project of speech data collection, using the web application Samrómur which is built upon Common Voice, Mozilla Foundation's web platform for open-source voice collection. The goal of the project is to build a large-scale speech corpus for Automatic Speech Recognition (ASR) for Icelandic. Upon completion, Samrómur will be the largest open speech corpus for Icelandic collected from the public domain. We discuss the methods used for the crowd-sourcing effort and show the importance of marketing and good media coverage when launching a  crowd-sourcing campaign. Preliminary results exceed our expectations, and in one month we collected data that we had estimated would take three months to obtain. Furthermore, our initial dataset of around 45 thousand utterances has good demographic coverage, is gender-balanced and with proper age distribution. We also report on the task of validating the recordings, which we have not promoted, but have had numerous hours invested by volunteers.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.425.pdf

Machine Learning

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Semi-supervised Development of ASR Systems for Multilingual Code-switched Speech in Under-resourced Languages

Astik Biswas, Emre Yilmaz, Febe De Wet, Ewald Van der westhuizen and Thomas Niesler

This paper reports on the semi-supervised development of acoustic and language models for under-resourced, code-switched speech in five South African languages.

Two approaches are considered. The first constructs four separate bilingual automatic speech recognisers (ASRs) corresponding to four different language pairs between which speakers switch frequently. The second uses a single, unified, five-lingual ASR system that represents all the languages (English, isiZulu, isiXhosa, Setswana and Sesotho). We evaluate the effectiveness of these two approaches when used to add additional data to our extremely sparse training sets. Results indicate that batch-wise semi-supervised training yields better results than a non-batch-wise approach. Furthermore, while the separate bilingual systems achieved better recognition performance than the unified system, they benefited more from pseudolabels generated by the five-lingual system than from those generated by the bilingual systems.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.426.pdf

 

CLFD: A Novel Vectorization Technique and Its Application in Fake News Detection

Michail Mersinias, Stergos Afantenos and Georgios Chalkiadakis

In recent years, fake news detection has been an emerging research area. In this paper, we put forward a novel statistical approach for the generation of feature vectors to describe a document. Our so-called class label frequency distance (clfd), is shown experimentally to provide an effective way for boosting the performance of methods. Specifically, our experiments, carried out in the fake news detection domain, verify that efficient traditional methods that use our vectorization approach, consistently outperform deep learning methods that use word embeddings for small and medium sized datasets, while the results are comparable for large datasets. In addition, we demonstrate that a novel hybrid method that utilizes both a clfd-boosted logistic regression classifier and a deep learning one, clearly outperforms deep learning methods even in large datasets.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.427.pdf

 

SimplifyUR: Unsupervised Lexical Text Simplification for Urdu

Namoos Hayat Qasmi, Haris Bin Zia, Awais Athar and Agha Ali Raza

This paper presents the first attempt at Automatic Text Simplification (ATS) for Urdu, the language of 170 million people worldwide. Being a low-resource language in terms of standard linguistic resources, recent text simplification approaches that rely on manually crafted simplified corpora or lexicons such as WordNet are not applicable to Urdu. Urdu is a morphologically rich language that requires unique considerations such as proper handling of inflectional case and honorifics. We present an unsupervised method for lexical simplification of complex Urdu text. Our method only requires plain Urdu text and makes use of word embeddings together with a set of morphological features to generate simplifications. Our system achieves a BLEU score of 80.15 and SARI score of 42.02 upon automatic evaluation on manually crafted simplified corpora. We also report results for human evaluations for correctness, grammaticality, meaning-preservation and simplicity of the output. Our code and corpus are publicly available to make our results reproducible.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.428.pdf

 

Jamo Pair Encoding: Subcharacter Representation-based Extreme Korean Vocabulary Compression for Efficient Subword Tokenization

Sangwhan Moon and Naoaki Okazaki

In the context of multilingual language model pre-training, vocabulary size for languages with a broad set of potential characters is an unsolved problem. We propose two algorithms applicable in any unsupervised multilingual pre-training task, increasing the elasticity of budget required for building the vocabulary in Byte-Pair Encoding inspired tokenizers, significantly reducing the cost of supporting Korean in a multilingual model.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.429.pdf

 

Offensive Language and Hate Speech Detection for Danish

Gudbjartur Ingi Sigurbergsson and Leon Derczynski

The presence of offensive language on social media platforms and the implications this poses is becoming a major concern in modern society. Given the enormous amount of content created every day, automatic methods are required to detect and deal with this type of content. Until now, most of the research has focused on solving the problem for the English language, while the problem is multilingual. We construct a Danish dataset DKhate containing user-generated comments from various social media platforms, and to our knowledge, the first of its kind, annotated for various types and target of offensive language. We develop four automatic classification systems, each designed to work for both the English and the Danish language. In the detection of offensive language in English, the best performing system achieves a macro averaged F1-score of 0.74, and the best performing system for Danish achieves a macro averaged F1-score of 0.70. In the detection of whether or not an offensive post is targeted, the best performing system for English achieves a macro averaged F1-score of 0.62, while the best performing system for Danish achieves a macro averaged F1-score of 0.73. Finally, in the detection of the target type in a targeted offensive post, the best performing system for English achieves a macro averaged F1-score of 0.56, and the best performing system for Danish achieves a macro averaged F1-score of 0.63. Our work for both the English and the Danish language captures the type and targets of offensive language, and present automatic methods for detecting different kinds of offensive language such as hate speech and cyberbullying.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.430.pdf

 

Semi-supervised Deep Embedded Clustering with Anomaly Detection for Semantic Frame Induction

Zheng Xin Yong and Tiago Timponi Torrent

Although FrameNet is recognized as one of the most fine-grained lexical databases, its coverage of lexical units is still limited. To tackle this issue, we propose a two-step frame induction process: for a set of lexical units not yet present in Berkeley FrameNet data release 1.7, first remove those that cannot fit into any existing semantic frame in FrameNet; then, assign the remaining lexical units to their correct frames. We also present the Semi-supervised Deep Embedded Clustering with Anomaly Detection (SDEC-AD) model—an algorithm that maps high-dimensional contextualized vector representations of lexical units to a low-dimensional latent space for better frame prediction and uses reconstruction error to identify lexical units that cannot evoke frames in FrameNet. SDEC-AD outperforms the state-of-the-art methods in both steps of the frame induction process. Empirical results also show that definitions provide contextual information for representing and characterizing the frame membership of lexical units.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.431.pdf

 

Search Query Language Identification Using Weak Labeling

Ritiz Tambi, Ajinkya Kale and Tracy Holloway King

Language identification is a well-known task for natural language documents. In this paper we explore search query language identification which is usually the first task before any other query understanding. Without loss of generalization, we run our experiments on the Adobe Stock search engine. Even though the domain is relatively generic because Adobe Stock queries cover a broad range of objects and concepts, out-of-the-box language identifiers do not perform well due to the extremely short text found in queries. Unlike other well-studied supervised approaches for this task, we examine a practical approach for the cold start problem for automatically getting large-scale query-language pairs for training. We describe the process of creating weak-labeled training data and then human-annotated evaluation data for the search query language identification task. The effectiveness of this technique is demonstrated by training a gradient boosting model for language classification given a query. We out-perform the open domain text model baselines by a large margin.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.432.pdf

 

Automated Phonological Transcription of Akkadian Cuneiform Text

Aleksi Sahala, Miikka Silfverberg, Antti Arppe and Krister Lindén

Akkadian was an East-Semitic language spoken in ancient Mesopotamia. The language is attested on hundreds of thousands of cuneiform clay tablets. Several Akkadian text corpora contain only the transliterated text. In this paper, we investigate automated phonological transcription of the transliterated corpora. The phonological transcription provides a linguistically appealing form to represent Akkadian, because the transcription is normalized according to the grammatical description of a given dialect and explicitly shows the Akkadian renderings for Sumerian logograms. Because cuneiform text does not mark the inflection for logograms, the inflected form needs to be inferred from the sentence context. To the best of our knowledge, this is the first documented attempt to automatically transcribe Akkadian. Using a context-aware neural network model, we are able to automatically transcribe syllabic tokens at near human performance with 96% recall @ 3, while the logogram transcription remains more challenging at 82% recall @ 3.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.433.pdf

 

COSTRA 1.0: A Dataset of Complex Sentence Transformations

Petra Barancikova and Ondřej Bojar

We present COSTRA 1.0, a dataset of complex sentence transformations. The dataset is intended for the study of sentence-level embeddings beyond simple word alternations or standard paraphrasing. This first version of the dataset is limited to sentences in Czech but the construction method is universal and we plan to use it also for other languages. The dataset consist of 4,262 unique sentences with average length of 10 words, illustrating 15 types of modifications such as simplification, generalization, or formal and informal language variation. The hope is that with this dataset, we should be able to test semantic properties of sentence embeddings and perhaps even to find some topologically interesting ``skeleton'' in the sentence embedding space. A preliminary analysis using LASER, multi-purpose multi-lingual sentence embeddings suggests that the LASER space does not exhibit the desired properties.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.434.pdf

 

Automatic In-the-wild Dataset Annotation with Deep Generalized Multiple Instance Learning

Joana Correia, Isabel Trancoso and Bhiksha Raj

The automation of the diagnosis and monitoring of speech affecting diseases in real life situations, such as Depression or Parkinson's disease, depends on the existence of rich and large datasets that resemble real life conditions, such as those collected from in-the-wild multimedia repositories like YouTube. However, the cost of manually labeling these large datasets can be prohibitive. In this work, we propose to overcome this problem by automating the annotation process, without any requirements for human intervention. We formulate the annotation problem as a Multiple Instance Learning (MIL) problem, and propose a novel solution that is based on end-to-end differentiable neural networks. Our solution has the additional advantage of generalizing the MIL framework to more scenarios where the data is stil organized in bags but does not meet the MIL bag label conditions. We demonstrate the performance of the proposed method in labeling the in-the-Wild Speech Medical (WSM) Corpus, using simple textual cues extracted from videos and their metadata. Furthermore we show what is the contribution of each type of textual cues for the final model performance, as well as study the influence of the size of the bags of instances in determining the difficulty of the learning problem

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.435.pdf

 

How Much Data Do You Need? About the Creation of a Ground Truth for Black Letter and the Effectiveness of Neural OCR

Phillip Benjamin Ströbel, Simon Clematide and Martin Volk

Recent advances in Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) have led to more accurate textrecognition of historical documents.  The Digital Humanities heavily profit from these developments, but they still struggle whenchoosing from the plethora of OCR systems available on the one hand and when defining workflows for their projects on the other hand.In this work, we present our approach to build a ground truth for a historical German-language newspaper published in black letter. Wealso report how we used it to systematically evaluate the performance of different OCR engines. Additionally, we used this ground truthto make an informed estimate as to how much data is necessary to achieve high-quality OCR results. The outcomes of our experimentsshow that HTR architectures can successfully recognise black letter text and that a ground truth size of 50 newspaper pages suffices toachieve good OCR accuracy. Moreover, our models perform equally well on data they have not seen during training, which means thatadditional manual correction for diverging data is superfluous.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.436.pdf

 

Dirichlet-Smoothed Word Embeddings for Low-Resource Settings

Jakob Jungmaier, Nora Kassner and Benjamin Roth

Nowadays, classical count-based word embeddings using positive pointwise mutual information (PPMI) weighted co-occurrence matrices have been widely superseded by machine-learning-based methods like word2vec and GloVe. But these methods are usually applied using very large amounts of text data. In many cases, however, there is not much text data available, for example for specific domains or low-resource languages. This paper revisits PPMI by adding Dirichlet smoothing to correct its bias towards rare words. We evaluate on standard word similarity data sets and compare to word2vec and the recent state of the art for low-resource settings: Positive and Unlabeled (PU) Learning for word embeddings. The proposed method outperforms PU-Learning for low-resource settings and obtains competitive results for Maltese and Luxembourgish.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.437.pdf

 

On The Performance of Time-Pooling Strategies for End-to-End Spoken Language Identification

Joao Monteiro, Md Jahangir Alam and Tiago Falk

Automatic speech processing applications often have to deal with the problem of aggregating local descriptors (i.e., representations of input speech data corresponding to specific portions across the time dimension) and turning them into a single fixed-dimension representation, known as global descriptor, on top of which downstream classification tasks can be performed. In this paper, we provide an empirical assessment of different time pooling strategies when used with state-of-the-art representation learning models. In particular, insights are provided as to when it is suitable to use simple statistics of local descriptors or when more sophisticated approaches are needed. Here, language identification is used as a case study and a database containing ten oriental languages under varying test conditions (short-duration test recordings, confusing languages, unseen languages) is used. Experiments are performed with classifiers trained on top of global descriptors to provide insights on open-set evaluation performance and show that appropriate selection of such pooling strategies yield embeddings able to outperform well-known benchmark systems as well as previously results based on attention only.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.438.pdf

 

Neural Disambiguation of Lemma and Part of Speech in Morphologically Rich Languages

José María Hoya Quecedo, Koppatz Maximilian and Roman Yangarber

We consider the problem of disambiguating the lemma and part of speech of ambiguous words in morphologically rich languages. We propose a method for disambiguating ambiguous words in context, using a large un-annotated corpus of text, and a morphological analyser—with no manual disambiguation or data annotation. We assume that the morphological analyser produces multiple analyses for ambiguous words. The idea is to train recurrent neural networks on the output that the morphological analyser produces for unambiguous words. We present performance on POS and lemma disambiguation that reaches or surpasses the state of the art—including supervised models—using no manually annotated data. We evaluate the method on several morphologically rich languages.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.439.pdf

 

Non-Linearity in Mapping Based Cross-Lingual Word Embeddings

Jiawei Zhao and Andrew Gilman

Recent works on cross-lingual word embeddings have been mainly focused on linear-mapping-based approaches, where pre-trained word embeddings are mapped into a shared vector space using a linear transformation. However, there is a limitation in such approaches--they follow a key assumption: words with similar meanings share similar geometric arrangements between their monolingual word embeddings, which suggest that there is a linear relationship between languages. However, such assumption may not hold for all language pairs across all semantic concepts. We investigate whether non-linear mappings can better describe the relationship between different languages by utilising kernel Canonical Correlation Analysis (KCCA). Experimental results on five language pairs show an improvement over current state-of-art results in both supervised and self-learning scenarios, confirming that non-linear mapping is a better way to describe the relationship between languages.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.440.pdf

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