LREC 2020 Paper Dissemination (4/10)

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 1, 2020

Links to each session

Knowledge Discovery and Representation


Humans Keep It One Hundred: an Overview of AI Journey

Tatiana Shavrina, Anton Emelyanov, Alena Fenogenova, Vadim Fomin, Vladislav Mikhailov, Andrey Evlampiev, Valentin Malykh, Vladimir Larin, Alex Natekin, Aleksandr Vatulin, Peter Romov, Daniil Anastasiev, Nikolai Zinov and Andrey Chertok

Artificial General Intelligence (AGI) is showing growing performance in numerous applications – beating human performance in Chess and Go, using knowledge bases and text sources to answer questions (SQuAD) and even pass human examination (Aristo project). In this paper, we describe the results of AI Journey, a competition of AI-systems aimed to improve AI performance on knowledge bases, reasoning and text generation. Competing systems pass the final native language exam (in Russian), including versatile grammar tasks (test and open questions) and an essay, achieving a high score of 69%, with 68% being an average human result. During the competition, a baseline for the task and essay parts was proposed, and 80+ systems were submitted, showing different approaches to task understanding and reasoning. All the data and solutions can be found on github


Towards Data-driven Ontologies: a Filtering Approach using Keywords and Natural Language Constructs

Maaike de Boer and Jack P. C. Verhoosel

Creating ontologies is an expensive task. Our vision is that we can automatically generate ontologies based on a set of relevant documents to create a kick-start in ontology creating sessions. In this paper, we focus on enhancing two often used methods, OpenIE and co-occurrences. We evaluate the methods on two document sets, one about pizza and one about the agriculture domain. The methods are evaluated using two types of F1-score (objective, quantitative) and through a human assessment (subjective, qualitative). The results show that 1) Cooc performs both objectively and subjectively better than OpenIE; 2) the filtering methods based on keywords and on Word2vec perform similarly; 3) the filtering methods both perform better compared to OpenIE and similar to Cooc; 4) Cooc-NVP performs best, especially considering the subjective evaluation. Although, the investigated methods provide a good start for extracting an ontology out of a set of domain documents, various improvements are still possible, especially in the natural language based methods.


A French Corpus and Annotation Schema for Named Entity Recognition and Relation Extraction of Financial News

Ali Jabbari, Olivier Sauvage, Hamada Zeine and Hamza Chergui

In financial services industry, compliance involves a series of practices and controls in order to meet key regulatory standards which aim to reduce financial risk and crime, e.g.\ money laundering and financing of terrorism. Faced with the growing risks, it is imperative for financial institutions to seek automated information extraction techniques for monitoring financial activities of their customers. This work describes an ontology of compliance-related concepts and relationships along with a corpus annotated according to it. The presented corpus consists of financial news articles in French and allows for training and evaluating domain-specific named entity recognition and relation extraction algorithms. We present some of our experimental results on named entity recognition and relation extraction using our annotated corpus. We aim to furthermore use the the proposed ontology towards construction of a knowledge base of financial relations.


Inferences for Lexical Semantic Resource Building with Less Supervision

Nadia Bebeshina and Mathieu Lafourcade

Lexical semantic resources may be built using various approaches such as extraction from corpora, integration of the relevant pieces of knowledge from the pre-existing knowledge resources, and endogenous inference. Each of these techniques needs human supervision in order to deal with the  potential errors, mapping difficulties or inferred candidate validation. We detail how various inference processes can be employed for the less supervised lexical semantic resource building. Our experience is based on the combination of different inference techniques for multilingual resource building and evaluation.


Acquiring Social Knowledge about Personality and Driving-related Behavior

Ritsuko Iwai, Daisuke Kawahara, Takatsune Kumada and Sadao Kurohashi

In this paper, we introduce our psychological approach to collect human-specific social knowledge from a text corpus, using NLP techniques. It is often not explicitly described but shared among people, which we call social knowledge.  We focus on the social knowledge, especially personality and driving. We used the language resources that were developed based on psychological research methods; a Japanese personality dictionary (317 words) and a driving experience corpus (8,080 sentences) annotated with behavior and subjectivity. Using them, we automatically extracted collocations between personality descriptors and driving-related behavior from a driving behavior and subjectivity corpus (1,803,328 sentences after filtering) and obtained unique 5,334 collocations. To evaluate the collocations as social knowledge, we designed four step-by-step crowdsourcing tasks.  They resulted in 266 pieces of social knowledge. They include the knowledge that might be difficult to recall by themselves but easy to agree with.  We discuss the acquired social knowledge and the contribution to implementations into systems.


Implicit Knowledge in Argumentative Texts: An Annotated Corpus

Maria Becker, Katharina Korfhage and Anette Frank

When speaking or writing, people omit information that seems clear and evident, such that only part of the message is expressed in words. Especially in argumentative texts it is very common that (important) parts of the argument are implied and omitted. We hypothesize that for argument analysis it will be beneficial to reconstruct this implied information. As a starting point for filling knowledge gaps, we build a corpus consisting of high-quality human annotations of missing and implied information in argumentative texts. To learn more about  the characteristics of both the argumentative texts and the added information, we further annotate the data with semantic clause types and commonsense knowledge relations. The outcome of our work is a carefully designed and richly annotated dataset, for which we then provide an in-depth analysis by investigating characteristic distributions and correlations of the assigned labels. We reveal interesting patterns and intersections between the annotation categories and properties of our dataset, which enable insights into the characteristics of both argumentative texts and implicit knowledge in terms of structural features and semantic information. The results of our analysis can help to assist automated argument analysis and can guide the process of revealing implicit information in argumentative texts automatically.


Multiple Knowledge GraphDB (MKGDB)

Stefano Faralli, Paola Velardi and Farid Yusifli

We present MKGDB, a large-scale graph database created as a combination of multiple taxonomy backbones extracted from 5 existing knowledge graphs, namely: ConceptNet, DBpedia, WebIsAGraph, WordNet and the Wikipedia category hierarchy.  MKGDB, thanks the versatility of the Neo4j graph database manager technology, is intended to favour and help the development of  open-domain natural language processing  applications relying on knowledge bases, such as information extraction, hypernymy discovery, topic clustering, and others. Our resource consists of a large  hypernymy graph which counts more than 37 million nodes and more than 81 million hypernymy relations.


Orchestrating NLP Services for the Legal Domain

Julian Moreno-Schneider, Georg Rehm, Elena Montiel-Ponsoda, Víctor Rodriguez-Doncel, Artem Revenko, Sotirios Karampatakis, Maria Khvalchik, Christian Sageder, Jorge Gracia and Filippo Maganza

Legal technology is currently receiving a lot of attention from various angles. In this contribution we describe the main technical components of a system that is currently under development in the European innovation project Lynx, which includes partners from industry and research. The key contribution of this paper is a workflow manager that enables the flexible orchestration of workflows based on a portfolio of Natural Language Processing and Content Curation services as well as a Multilingual Legal Knowledge Graph that contains semantic information and meaningful references to legal documents. We also describe different use cases with which we experiment and develop prototypical solutions.


Evaluation Dataset and Methodology for Extracting Application-Specific Taxonomies from the Wikipedia Knowledge Graph

Georgeta Bordea, Stefano Faralli, Fleur Mougin, Paul Buitelaar and Gayo Diallo

In this work, we address the task of extracting application-specific taxonomies from the category hierarchy of Wikipedia. Previous work on pruning the Wikipedia knowledge graph relied on silver standard taxonomies which can only be automatically extracted for a small subset of domains rooted in relatively focused nodes, placed at an intermediate level in the knowledge graphs. In this work, we propose an iterative methodology to extract an application-specific gold standard dataset from a knowledge graph and an evaluation framework to comparatively assess the quality of noisy automatically extracted taxonomies. We employ an existing state of the art algorithm in an iterative manner and we propose several sampling strategies to reduce the amount of manual work needed for evaluation. A first gold standard dataset is released to the research community for this task along with a companion evaluation framework. This dataset addresses a real-world application from the medical domain, namely the extraction of food-drug and herb-drug interactions.


Subjective Evaluation of Comprehensibility in Movie Interactions

Estelle Randria, Lionel Fontan, Maxime Le Coz, Isabelle Ferrané and Julien Pinquier

Various research works have dealt with the comprehensibility of textual, audio, or audiovisual documents, and showed  that factors related to text (e.g. linguistic complexity), sound (e.g. speech intelligibility), image (e.g. presence of visual context), or even to cognition and emotion can  play  a  major  role  in  the ability of humans to understand the semantic and pragmatic contents of a given document. However, to date, no reference human data is available that could help investigating the role of the linguistic and extralinguistic information present at these different levels (i.e., linguistic, audio/phonetic, and visual) in multimodal documents (e.g., movies). The present work aimed  at  building  a  corpus  of  human annotations that would help to study further how much and in which way the human perception of comprehensibility (i.e., of the difficulty of comprehension, referred in this paper as overall difficulty) of audiovisual documents is affected (1) by lexical complexity, grammatical complexity, and speech intelligibility, and (2) by the modality/ies (text, audio, video) available to the human recipient.


Representing Multiword Term Variation in a Terminological Knowledge Base: a Corpus-Based Study

Pilar León-Araúz, Arianne Reimerink and Melania Cabezas-García

In scientific and technical communication, multiword terms are the most frequent type of lexical units. Rendering them in another language is not an easy task due to their cognitive complexity, the proliferation of different forms, and their unsystematic representation in terminographic resources. This often results in a broad spectrum of translations for multiword terms, which also foment term variation since they consist of two or more constituents. In this study we carried out a quantitative and qualitative analysis of Spanish translation variants of a set of environment-related concepts by evaluating equivalents in three parallel corpora, two comparable corpora and two terminological resources. Our results showed that MWTs exhibit a significant degree of term variation of different characteristics, which were used to establish a set of criteria according to which term variants should be selected, organized and described in terminological knowledge bases.


Understanding Spatial Relations through Multiple Modalities

Soham Dan, Hangfeng He and Dan Roth

Recognizing spatial relations and reasoning about them is essential in multiple applications including navigation, direction giving and human-computer interaction in general. Spatial relations between objects can either be explicit – expressed as spatial prepositions, or implicit – expressed by spatial verbs such as moving, walking, shifting, etc. Both these, but implicit relations in particular, require significant common sense understanding. In this paper, we introduce the task of inferring implicit and explicit spatial relations between two entities in an image. We design a model that uses both textual and visual information to predict the spatial relations, making use of both positional and size information of objects and image embeddings. We contrast our spatial model with powerful language models and show how our modeling complements the power of these, improving prediction accuracy and coverage and facilitates dealing with unseen subjects, objects and relations.


A Topic-Aligned Multilingual Corpus of Wikipedia Articles for Studying Information Asymmetry in Low Resource Languages

Dwaipayan Roy, Sumit Bhatia and Prateek Jain

Wikipedia is the largest web-based open encyclopedia covering more than three hundred languages. However, different language editions of Wikipedia differ significantly in terms of their information coverage. We present a systematic comparison of information coverage in English Wikipedia (most exhaustive) and Wikipedias in eight other widely spoken languages (Arabic, German, Hindi, Korean, Portuguese, Russian, Spanish and Turkish). We analyze the content present in the respective Wikipedias in terms of the coverage of topics as well as the depth of coverage of topics included in these Wikipedias. Our analysis quantifies and provides useful insights about the information gap that exists between different language editions of Wikipedia and offers a roadmap for the IR community to bridge this gap.


Pártélet: A Hungarian Corpus of Propaganda Texts from the Hungarian Socialist Era

Zoltán Kmetty, Veronika Vincze, Dorottya Demszky, Orsolya Ring, Balázs Nagy and Martina Katalin Szabó

In this paper, we present Pártélet, a digitized Hungarian corpus of Communist propaganda texts. Pártélet was the official journal of the governing party during the Hungarian socialism from 1956 to 1989, hence it represents the direct political agitation and propaganda of the dictatorial system in question. The paper has a dual purpose: first, to present a general review of the corpus compilation process and the basic statistical data of the corpus, and second, to demonstrate through two case studies what the dataset can be used for. We show that our corpus provides a unique opportunity for conducting research on Hungarian propaganda discourse, as well as analyzing changes of this discourse over a 35-year period of time with computer-assisted methods.


KORE 50^DYWC: An Evaluation Data Set for Entity Linking Based on DBpedia, YAGO, Wikidata, and Crunchbase

Kristian Noullet, Rico Mix and Michael Färber

A major domain of research in natural language processing is named entity recognition and disambiguation (NERD). One of the main ways of attempting to achieve this goal is through use of Semantic Web technologies and its structured data formats. Due to the nature of structured data, information can be extracted more easily, therewith allowing for the creation of knowledge graphs. In order to properly evaluate a NERD system, gold standard data sets are required. A plethora of different evaluation data sets exists, mostly relying on either Wikipedia or DBpedia. Therefore, we have extended a widely-used gold standard data set, KORE 50, to not only accommodate NERD tasks for DBpedia, but also for YAGO, Wikidata and Crunchbase. As such, our data set, KORE 50^DYWC, allows for a broader spectrum of evaluation. Among others, the knowledge graph agnosticity of NERD systems may be evaluated which, to the best of our knowledge, was not possible until now for this number of knowledge graphs.


Eye4Ref: A Multimodal Eye Movement Dataset of Referentially Complex Situations

Özge Alacam, Eugen Ruppert, Amr Rekaby Salama, Tobias Staron and Wolfgang Menzel

Eye4Ref is a rich multimodal dataset of eye-movement recordings collected from referentially complex situated settings where the linguistic utterances and their visual referential world were available to the listener. It consists of not only fixation parameters but also saccadic movement parameters that are time-locked to accompanying German utterances (with English translations). Additionally, it also contains symbolic knowledge (contextual) representations of the images to map the referring expressions onto the objects in corresponding images. Overall, the data was collected from 62 participants in three different experimental setups (86 systematically controlled sentence–image pairs and 1844 eye-movement recordings). Referential complexity was controlled by visual manipulations (e.g. number of objects, visibility of the target items, etc.), and by linguistic manipulations (e.g., the position of the disambiguating word in a sentence).  This multimodal dataset,  in which the three different sources of information namely eye-tracking, language, and visual environment are aligned, offers a test of various research questions not from only language perspective but also computer vision.


Language Modelling

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SiBert: Enhanced Chinese Pre-trained Language Model with Sentence Insertion

Jiahao Chen, Chenjie Cao and Xiuyan Jiang

Pre-trained models have achieved great success in learning unsupervised language representations by self-supervised tasks on large-scale corpora. Recent studies mainly focus on how to fine-tune different downstream tasks from a general pre-trained model. However, some studies show that customized self-supervised tasks for a particular type of downstream task can effectively help the pre-trained model to capture more corresponding knowledge and semantic information. Hence a new pre-training task called Sentence Insertion (SI) is proposed in this paper for Chinese query-passage pairs NLP tasks including answer span prediction, retrieval question answering and sentence level cloze test. The related experiment results indicate that the proposed SI can improve the performance of the Chinese Pre-trained models significantly. Moreover, a word segmentation method called SentencePiece is utilized to further enhance Chinese Bert performance for tasks with long texts. The complete source code is available at\_tensorflow.


Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset

Brian Roark, Lawrence Wolf-Sonkin, Christo Kirov, Sabrina J. Mielke, Cibu Johny, Isin Demirsahin and Keith Hall

This paper describes the Dakshina dataset, a new resource consisting of  text in both the Latin and native scripts for 12 South Asian languages.  The dataset includes, for each language: 1) native script Wikipedia text; 2) a romanization lexicon; and 3) full sentence parallel data in both a native script of the language and the basic Latin alphabet.  We document the methods used for preparation and selection of the Wikipedia text in each language; collection of attested romanizations for sampled lexicons; and manual romanization of held-out sentences from the native script collections.  We additionally provide baseline results on several tasks made possible by the dataset, including single word transliteration, full sentence transliteration, and language modeling of native script and romanized text.


GM-RKB WikiText Error Correction Task and Baselines

Gabor Melli, Abdelrhman Eldallal, Bassim Lazem and Olga Moreira

We introduce the GM-RKB WikiText Error Correction Task for the automatic detection and correction of typographical errors in WikiText annotated pages. The included corpus is based on a snapshot of the GM-RKB domain-specific semantic wiki consisting of a large collection of concepts, personages, and publications primary centered on data mining and machine learning research topics. Numerous Wikipedia pages were also included as additional training data in the task’s evaluation process. The corpus was then automatically updated to synthetically include realistic errors to produce a training and evaluation ground truth comparison. We designed and evaluated two supervised baseline WikiFixer error correction methods: (1) a naive approach based on a maximum likelihood character-level language model; (2) and an advanced model based on a sequence-to-sequence (seq2seq) neural network architecture. Both error correction models operated at a character level. When compared against an off-the-shelf word-level spell checker these methods showed a significant improvement in the task’s performance — with the seq2seq-based model correcting a higher number of errors than it introduced. Finally, we published our data and code.


Embedding Space Correlation as a Measure of Domain Similarity

Anne Beyer, Göran Kauermann and Hinrich Schütze

Prior work has determined domain similarity using text-based features of a corpus. However, when using pre-trained word embeddings, the underlying text corpus might not be accessible anymore. Therefore, we propose the CCA measure, a new measure of domain similarity based directly on the dimension-wise correlations between corresponding embedding spaces. Our results suggest that an inherent notion of domain can be captured this way, as we are able to reproduce our findings for different domain comparisons for English, German, Spanish and Czech as well as in cross-lingual comparisons. We further find a threshold at which the CCA measure indicates that two corpora come from the same domain in a monolingual setting by applying permutation tests. By evaluating the usability of the CCA measure in a domain adaptation application, we also show that it can be used to determine which corpora are more similar to each other in a cross-domain sentiment detection task.


Wiki-40B: Multilingual Language Model Dataset

Mandy Guo, Zihang Dai, Denny Vrandečić and Rami Al-Rfou

We propose a new multilingual language model benchmark that is composed of 40+ languages spanning several scripts and linguistic families. With around 40 billion characters, we hope this new resource will accelerate the research of multilingual modeling. We train monolingual causal language models using a state-of-the-art model (Transformer-XL) establishing baselines for many languages. We also introduce the task of multilingual causal language modeling where we train our model on the combined text of 40+ languages from Wikipedia with different vocabulary sizes and evaluate on the languages individually. We released the cleaned-up text of 40+ Wikipedia language editions, the corresponding trained monolingual language models, and several multilingual language models with different fixed vocabulary sizes.


Know thy Corpus! Robust Methods for Digital Curation of Web corpora

Serge Sharoff

This paper proposes a novel framework for digital curation of Web corpora in order to provide robust estimation of their parameters, such as their composition and the lexicon.  In recent years language models pre-trained on large corpora emerged as clear winners in numerous NLP tasks, but no proper analysis of the corpora which led to their success has been conducted.  The paper presents a procedure for robust frequency estimation, which helps in establishing the core lexicon for a given corpus, as well as a procedure for estimating the corpus composition via unsupervised topic models and via supervised  genre classification of Web pages.  The results of the digital curation study applied to several Web-derived corpora demonstrate their considerable differences.  First, this concerns different frequency bursts which impact the core lexicon obtained from each corpus.  Second, this concerns the kinds of texts they contain.  For example, OpenWebText contains considerably more topical news and political argumentation in comparison to ukWac or Wikipedia.  The tools and the results of analysis have been released.


Evaluating Approaches to Personalizing Language Models

Milton King and Paul Cook

In this work, we consider the problem of personalizing language models, that is, building language models that are tailored to the writing style of an individual. Because training language models requires a large amount of text, and individuals do not necessarily possess a large corpus of their writing that could be used for training, approaches to personalizing language models must be able to rely on only a small amount of text from any one user. In this work, we compare three approaches to personalizing a language model that was trained on a large background corpus using a relatively small amount of text from an individual user. We evaluate these approaches using perplexity, as well as two measures based on next word prediction for smartphone soft keyboards. Our results show that when only a small amount of user-specific text is available, an approach based on priming gives the most improvement, while when larger amounts of user-specific text are available, an approach based on language model interpolation performs best. We carry out further experiments to show that these approaches to personalization outperform language model adaptation based on demographic factors.


Class-based LSTM Russian Language Model with Linguistic Information

Irina Kipyatkova and Alexey Karpov

In the paper, we present class-based LSTM Russian language models (LMs) with classes generated with the use of both word frequency and linguistic information data, obtained with the help of the “VisualSynan” software from the AOT project. We have created LSTM LMs with various numbers of classes and compared them with word-based LM and class-based LM with word2vec class generation in terms of perplexity, training time, and WER. In addition, we performed a linear interpolation of LSTM language models with the baseline 3-gram language model. The LSTM language models were used for very large vocabulary continuous Russian speech recognition at an N-best list rescoring stage. We achieved significant progress in training time reduction with only slight degradation in recognition accuracy comparing to the word-based LM. In addition, our LM with classes generated using linguistic information outperformed LM with classes generated using word2vec. We achieved WER of 14.94 % at our own speech corpus of continuous Russian speech that is 15 % relative reduction with respect to the baseline 3-gram model.


Adaptation of Deep Bidirectional Transformers for Afrikaans Language

Sello Ralethe

The recent success of pretrained language models in Natural Language Processing has sparked interest in training such models for languages other than English. Currently, training of these models can either be monolingual or multilingual based. In the case of multilingual models, such models are trained on concatenated data of multiple languages. We introduce AfriBERT, a language model for the Afrikaans language based on Bidirectional Encoder Representation from Transformers (BERT). We compare the performance of AfriBERT against multilingual BERT in multiple downstream tasks, namely part-of-speech tagging, named-entity recognition, and dependency parsing. Our results show that AfriBERT improves the current state-of-the-art in most of the tasks we considered, and that transfer learning from multilingual to monolingual model can have a significant performance improvement on downstream tasks. We release the pretrained model for AfriBERT.


FlauBERT: Unsupervised Language Model Pre-training for French

Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoit Crabbé, Laurent Besacier and Didier Schwab

Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their contextualization at the sentence level. This has been widely demonstrated for English using contextualized representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of the time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research community for further reproducible experiments in French NLP.


Accelerated High-Quality Mutual-Information Based Word Clustering

Manuel R. Ciosici, Ira Assent and Leon Derczynski

Word clustering groups words that exhibit similar properties. One popular method for this is Brown clustering, which uses short-range distributional information to construct clusters. Specifically, this is a hard hierarchical clustering with a fixed-width beam that employs bi-grams and greedily minimizes global mutual information loss. The result is word clusters that tend to outperform or complement other word representations, especially when constrained by small datasets. However, Brown clustering has high computational complexity and does not lend itself to parallel computation. This, together with the lack of efficient implementations, limits their applicability in NLP. We present efficient implementations of Brown clustering and the alternative Exchange clustering as well as a number of methods to accelerate the computation of both hierarchical and flat clusters. We show empirically that clusters obtained with the accelerated method match the performance of clusters computed using the original methods.


Rhythmic Proximity Between Natives And Learners Of French – Evaluation of a metric based on the CEFC corpus

Sylvain Coulange and Solange Rossato

This work aims to better understand the role of rhythm in foreign accent, and its modelling. We made a model of rhythm in French taking into account its variability, thanks to the Corpus pour l’Étude du Français Contemporain (CEFC), which contains up to 300 hours of speech of a wide variety of speaker profiles and situations. 16 parameters were computed, each of them being based on segment duration, such as voicing and intersyllabic timing. All the parameters are fully automatically detected from signal, without ASR or transcription. A gaussian mixture model was trained on 1,340 native speakers of French; any 30-second minimum speech may be computed to get the probability of its belonging to this model. We tested it with 146 test native speakers (NS), 37 non-native speakers (NNS) from the same corpus, and 29 non-native Japanese learners of French (JpNNS) from an independent corpus. The probability of NNS having inferior log-likelihood to NS was only a tendency (p=.067), maybe due to the heterogeneity of French proficiency of the speakers; but a much bigger probability was obtained for JpNNS (p<.0001), where all speakers were A2 level. Eta-squared test showed that most efficient parameters were intersyllabic mean duration and variation coefficient, along with speech rate for NNS; and speech rate and phonation ratio for JpNNS.


From Linguistic Resources to Ontology-Aware Terminologies: Minding the Representation Gap

Giulia Speranza, Maria Pia di Buono, Johanna Monti and Federico Sangati

Terminological resources have proven crucial in many applications ranging from Computer-Aided Translation tools to authoring softwares and multilingual and cross-lingual information retrieval systems. Nonetheless, with the exception of a few felicitous examples, such as the IATE (Interactive Terminology for Europe) Termbank, many terminological resources are not available in standard formats, such as Term Base eXchange (TBX), thus preventing their sharing and reuse. Yet, these terminologies could be improved associating the correspondent ontology-based information. The research described in the present contribution demonstrates the process and the methodologies adopted in the automatic conversion into TBX of such type of resources, together with their semantic enrichment based on the formalization of ontological information into terminologies. We present a proof-of-concept using the Italian Linguistic Resource for the Archaeological domain (developed according to Thesauri and Guidelines of the Italian Central Institute for the Catalogue and Documentation). Further, we introduce the conversion tool developed to support the process of creating ontology-aware terminologies for improving interoperability and sharing of existing language technologies and data sets.


Modeling Factual Claims with Semantic Frames

Fatma Arslan, Josue Caraballo, Damian Jimenez and Chengkai Li

In this paper, we introduce an extension of the Berkeley FrameNet for the structured and semantic modeling of factual claims. Modeling is a robust tool that can be leveraged in many different tasks such as matching claims to existing fact-checks and translating claims to structured queries. Our work introduces 11 new manually crafted frames along with 9 existing FrameNet frames, all of which have been selected with fact-checking in mind. Along with these frames, we are also providing 2,540 fully annotated sentences, which can be used to understand how these frames are intended to work and to train machine learning models. Finally, we are also releasing our annotation tool to facilitate other researchers to make their own local extensions to FrameNet.


Less-Resourced and Endangered Languages

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Automatic Transcription Challenges for Inuktitut, a Low-Resource Polysynthetic Language

Vishwa Gupta and Gilles Boulianne

We introduce the first attempt at automatic speech recognition (ASR) in Inuktitut, as a representative for polysynthetic, low-resource languages, like many of the 900 Indigenous languages spoken in the Americas.  As most previous work on Inuktitut, we use texts from parliament proceedings, but in addition we have access to 23 hours of transcribed oral stories.  With this corpus, we show that Inuktitut displays a much higher degree of polysynthesis than other agglutinative languages usually considered in ASR, such as Finnish or Turkish.  Even with a vocabulary of 1.3 million words derived from proceedings and stories, held-out stories have more than 60% of words out-of-vocabulary.  We train bi-directional LSTM acoustic models, then investigate word and subword units, morphemes and syllables, and a deep neural network that finds word boundaries in subword  sequences.  We show that acoustic decoding using syllables decorated with word boundary markers results in the lowest word error rate.


Geographically-Balanced Gigaword Corpora for 50 Language Varieties

Jonathan Dunn and Ben Adams

While text corpora have been steadily increasing in overall size, even very large corpora are not designed to represent global population demographics. For example, recent work has shown that existing English gigaword corpora over-represent inner-circle varieties from the US and the UK. To correct implicit geographic and demographic biases, this paper uses country-level population demographics to guide the construction of gigaword web corpora. The resulting corpora explicitly match the ground-truth geographic distribution of each language, thus equally representing language users from around the world. This is important because it ensures that speakers of under-resourced language varieties (i.e., Indian English or Algerian French) are represented, both in the corpora themselves but also in derivative resources like word embeddings.


Data Augmentation using Machine Translation for Fake News Detection in the Urdu Language

Maaz Amjad, Grigori Sidorov and Alisa Zhila

The task of fake news detection is to distinguish legitimate news articles that describe real facts from those which convey deceiving and fictitious information. As the fake news phenomenon is omnipresent across all languages, it is crucial to be able to efficiently solve this problem for languages other than English. A common approach to this task is supervised classification using features of various complexity. Yet supervised machine learning requires substantial amount of annotated data. For English and a small number of other languages, annotated data availability is much higher, whereas for the vast majority of languages, it is almost scarce. We investigate whether machine translation at its present state could be successfully used as an automated technique for annotated corpora creation and augmentation for fake news detection focusing on the English-Urdu language pair. We train a fake news classifier for Urdu on (1) the manually annotated dataset originally in Urdu and (2) the machine-translated version of an existing annotated fake news dataset originally in English. We show that at the present state of machine translation quality for the English-Urdu language pair, the fully automated data augmentation through machine translation did not provide improvement for fake news detection in Urdu.


Evaluation of Greek Word Embeddings

Stamatis Outsios, Christos Karatsalos, Konstantinos Skianis and Michalis Vazirgiannis

Since word embeddings have been the most popular input for many NLP tasks, evaluating their quality is critical. Most research efforts are focusing on English word embeddings. This paper addresses the problem of training and evaluating such models for the Greek language. We present a new word analogy test set considering the original English Word2vec analogy test set and some specific linguistic aspects of the Greek language as well. Moreover, we create a Greek version of WordSim353 test collection for a basic evaluation of word similarities. Produced resources are available for download. We test seven word vector models and our evaluation shows that we are able to create meaningful representations. Last, we discover that the morphological complexity of the Greek language and polysemy can influence the quality of the resulting word embeddings.


A Dataset of Mycenaean Linear B Sequences

Katerina Papavassiliou, Gareth Owens and Dimitrios Kosmopoulos

We present our work towards a dataset of Mycenaean Linear B sequences gathered from the Mycenaean inscriptions written in the 13th and 14th century B.C. (c. 1400-1200 B.C.). The dataset contains sequences of Mycenaean words and ideograms according to the rules of the Mycenaean Greek language in the Late Bronze Age. Our ultimate goal is to contribute to the study, reading and understanding of ancient scripts and languages. Focusing on sequences, we seek to exploit the structure of the entire language, not just the Mycenaean vocabulary, to analyse sequential patterns. We use the dataset to experiment on estimating the missing symbols in damaged inscriptions.


The Nunavut Hansard Inuktitut–English Parallel Corpus 3.0 with Preliminary Machine Translation Results

Eric Joanis, Rebecca Knowles, Roland Kuhn, Samuel Larkin, Patrick Littell, Chi-kiu Lo, Darlene Stewart and Jeffrey Micher

The  Inuktitut  language,  a  member  of  the  Inuit-Yupik-Unangan  language  family,  is  spoken  across  Arctic  Canada  and  noted  for  its morphological complexity.  It is an official language of two territories, Nunavut and the Northwest Territories, and has recognition in additional regions.  This paper describes a newly released sentence-aligned Inuktitut–English corpus based on the proceedings of the Legislative Assembly of Nunavut, covering sessions from April 1999 to June 2017.  With approximately 1.3 million aligned sentence pairs, this is, to our knowledge, the largest parallel corpus of a polysynthetic language or an Indigenous language of the Americas released to date.  The paper describes the alignment methodology used, the evaluation of the alignments, and preliminary experiments on statistical and neural machine translation (SMT and NMT) between Inuktitut and English, in both directions.


Exploring Bilingual Word Embeddings for Hiligaynon, a Low-Resource Language

Leah Michel, Viktor Hangya and Alexander Fraser

This paper investigates the use of bilingual word embeddings for mining Hiligaynon translations of English words. There is very little research on Hiligaynon, an extremely low-resource language of Malayo-Polynesian origin with over 9 million speakers in the Philippines (we found just one paper). We use a publicly available Hiligaynon corpus with only 300K words, and match it with a comparable corpus in English. As there are no bilingual resources available, we manually develop a English-Hiligaynon lexicon and use this to train bilingual word embeddings. But we fail to mine accurate translations due to the small amount of data. To find out if the same holds true for a related language pair, we simulate the same low-resource setup on English to German and arrive at similar results. We then vary the size of the comparable English and German corpora to determine the minimum corpus size necessary to achieve competitive results. Further, we investigate the role of the seed lexicon. We show that with the same corpus size but with a smaller seed lexicon, performance can surpass results of previous studies. We release the lexicon of 1,200 English-Hiligaynon word pairs we created to encourage further investigation.


A Finite-State Morphological Analyser for Evenki

Anna Zueva, Anastasia Kuznetsova and Francis Tyers

It has been widely admitted that morphological analysis is an important step in automated text processing for morphologically rich languages. Evenki is a language with rich morphology, therefore a morphological analyser is highly desirable for processing Evenki texts and developing applications for Evenki. Although  two morphological analysers for Evenki have already been developed, they are able to analyse less than a half of the available Evenki corpora. The aim of this paper is to create a new morphological analyser for Evenki. It is implemented using the Helsinki Finite-State Transducer toolkit (HFST). The lexc formalism is used to specify the morphotactic rules, which define the valid orderings of morphemes in a word. Morphophonological alternations  and orthographic  rules are described using the twol formalism. The lexicon is extracted from available machine-readable dictionaries. Since a part of the corpora belongs to texts in Evenki dialects, a version of the analyser with relaxed rules is developed for processing dialectal features. We evaluate the analyser on available Evenki corpora and estimate precision, recall and F-score. We obtain coverage scores of between 61% and 87% on the available Evenki corpora.


Morphology-rich Alphasyllabary Embeddings

Amanuel Mersha and Stephen Wu

Word embeddings have been successfully trained in many languages. However, both intrinsic and extrinsic metrics are variable across languages, especially for languages that depart significantly from English in morphology and orthography. This study focuses on building a word embedding model suitable for the Semitic language of Amharic (Ethiopia), which is both morphologically rich and written as an alphasyllabary (abugida) rather than an alphabet. We compare embeddings from tailored neural models, simple pre-processing steps, off-the-shelf baselines, and parallel tasks on a better-resourced Semitic language – Arabic. Experiments show our model’s performance on word analogy tasks, illustrating the divergent objectives of morphological vs. semantic analogies.


Localization of Fake News Detection via Multitask Transfer Learning

Jan Christian Blaise Cruz, Julianne Agatha Tan and Charibeth Cheng

The use of the internet as a fast medium of spreading fake news reinforces the need for computational tools that combat it. Techniques that train fake news classifiers exist, but they all assume an abundance of resources including large labeled datasets and expert-curated corpora, which low-resource languages may not have. In this work, we make two main contributions: First, we alleviate resource scarcity by constructing the first expertly-curated benchmark dataset for fake news detection in Filipino, which we call “Fake News Filipino.” Second, we benchmark Transfer Learning (TL) techniques and show that they can be used to train robust fake news classifiers from little data, achieving 91% accuracy on our fake news dataset, reducing the error by 14% compared to established few-shot baselines. Furthermore, lifting ideas from multitask learning,  we show that augmenting transformer-based transfer techniques with auxiliary language modeling losses improves their performance by adapting to writing style. Using this, we improve TL performance by 4-6%, achieving an accuracy of 96% on our best model. Lastly, we show that our method generalizes well to different types of news articles, including political news, entertainment news, and opinion articles.


Evaluating Sentence Segmentation in Different Datasets of Neuropsychological Language Tests in Brazilian Portuguese

Edresson Casanova, Marcos Treviso, Lilian Hübner and Sandra Aluísio

Automatic analysis of connected speech by natural language processing techniques is a promising direction for diagnosing cognitive impairments. However, some difficulties still remain: the time required for manual narrative transcription and the decision on how transcripts should be divided into sentences for successful application of parsers used in metrics, such as Idea Density, to analyze the transcripts. The main goal of this paper was to develop a generic segmentation system for narratives of neuropsychological language tests. We explored the performance of our previous single-dataset-trained sentence segmentation architecture in a richer scenario involving three new datasets used to diagnose cognitive impairments, comprising different stories and two types of stimulus presentation for eliciting narratives — visual and oral — via illustrated story-book and sequence of scenes, and by retelling.  Also, we proposed and evaluated three modifications to our previous RCNN architecture: (i) the inclusion of a Linear Chain CRF; (ii) the inclusion of a self-attention mechanism; and (iii) the replacement of the LSTM recurrent layer by a Quasi-Recurrent Neural Network layer. Our study allowed us to develop two new models for segmenting impaired speech transcriptions, along with an ideal combination of datasets and specific groups of narratives to be used as the training set.


Jejueo Datasets for Machine Translation and Speech Synthesis

Kyubyong Park, Yo Joong Choe and Jiyeon Ham

Jejueo was classified as critically endangered by UNESCO in 2010. Although diverse efforts to revitalize it have been made, there have been few computational approaches. Motivated by this, we construct two new Jejueo datasets: Jejueo Interview Transcripts (JIT) and Jejueo Single Speaker Speech (JSS). The JIT dataset is a parallel corpus containing 170k+ Jejueo-Korean sentences, and the JSS dataset consists of 10k high-quality audio files recorded by a native Jejueo speaker and a transcript file. Subsequently, we build neural systems of machine translation and speech synthesis using them. All resources are publicly available via our GitHub repository. We hope that these datasets will attract interest of both language and machine learning communities.


Speech Corpus of Ainu Folklore and End-to-end Speech Recognition for Ainu Language

Kohei Matsuura, Sei Ueno, Masato Mimura, Shinsuke Sakai and Tatsuya Kawahara

Ainu is an unwritten language that has been spoken by Ainu people who are one of the ethnic groups in Japan. It is recognized as critically endangered by UNESCO and archiving and documentation of its language heritage is of paramount importance. Although a considerable amount of voice recordings of Ainu folklore has been produced and accumulated to save their culture, only a quite limited parts of them are transcribed so far. Thus, we started a project of automatic speech recognition (ASR) for the Ainu language in order to contribute to the development of annotated language archives. In this paper, we report speech corpus development and the structure and performance of end-to-end ASR for Ainu. We investigated four modeling units (phone, syllable, word piece, and word) and found that the syllable-based model performed best in terms of both word and phone recognition accuracy, which were about 60% and over 85% respectively in speaker-open condition. Furthermore, word and phone accuracy of 80% and 90% has been achieved in a speaker-closed setting. We also found out that a multilingual ASR training with additional speech corpora of English and Japanese further improves the speaker-open test accuracy.


Development of a Guarani – Spanish Parallel Corpus

Luis Chiruzzo, Pedro Amarilla, Adolfo Ríos and Gustavo Giménez Lugo

This paper presents the development of a Guarani – Spanish parallel corpus with sentence-level alignment. The Guarani sentences of the corpus use the Jopara Guarani dialect, the dialect of Guarani spoken in Paraguay, which is based on Guarani grammar and may include several Spanish loanwords or neologisms. The corpus has around 14,500 sentence pairs aligned using a semi-automatic process, containing 228,000 Guarani tokens and 336,000 Spanish tokens extracted from web sources.


AR-ASAG An ARabic Dataset for Automatic Short Answer Grading Evaluation

Leila Ouahrani and Djamal Bennouar

Automatic short answer grading is a significant problem in E-assessment. Several models have been proposed to deal with it. Evaluation and comparison of such solutions need the availability of Datasets with manual examples. In this paper, we introduce AR-ASAG, an Arabic Dataset for automatic short answer grading. The Dataset contains 2133 pairs of (Model Answer, Student Answer) in several versions (txt, xml, Moodle xml and .db). We explore then an unsupervised corpus based approach for automatic grading adapted to the Arabic Language. We use COALS (Correlated Occurrence Analogue to Lexical Semantic) algorithm to create semantic space for word distribution. The summation vector model is combined to term weighting and common words to achieve similarity between a teacher model answer and a student answer. The approach is particularly suitable for languages with scarce resources such as Arabic language where robust specific resources are not yet available.  A set of experiments were conducted to analyze the effect of domain specificity, semantic space dimension and stemming techniques on the effectiveness of the grading model. The proposed approach gives promising results for Arabic language. The reported results may serve as baseline for future research work evaluation


Processing Language Resources of Under-Resourced and Endangered Languages for the Generation of Augmentative Alternative Communication Boards

Anne Ferger

Under-resourced and endangered or small languages yield problems for automatic processing and exploiting because of the small amount of available data. This paper shows an approach using different annotations of enriched linguistic research data to create communication boards commonly used in Alternative Augmentative Communication (AAC). Using manually created lexical analysis and rich annotation (instead of high data quantity) allows for an automated creation of AAC communication boards. The example presented in this paper uses data of the indigenous language Dolgan (an endangered Turkic language of Northern Siberia) created in the project INEL(Arkhipov and Däbritz, 2018) to generate a basic communication board with audio snippets to be used in e.g. hospital communication or for multilingual settings. The created boards can be importet into various AAC software. In addition, the usage of standard formats makes this approach applicable to various different use cases.


The Nisvai Corpus of Oral Narrative Practices from Malekula (Vanuatu) and its Associated Language Resources

Jocelyn Aznar and Núria Gala

In this paper, we present a corpus of oral narratives from the Nisvai linguistic community and four associated language resources. Nisvai is an oral language spoken by 200 native speakers in the South-East of Malekula, an island of Vanuatu, Oceania. This language had never been the focus of a research before the one leading to this article. The corpus we present is made of 32 annotated narratives segmented into intonation units. The audio records were transcribed using the written conventions specifically developed for the language and translated into French. Four associated language resources have been generated by organizing the annotations into written documents: two of them are available online and two in paper format. The online resources allow the users to listen to the audio recordings whilereading the annotations. They were built to share the results of our fieldwork and to communicate on the Nisvai narrative practices with the researchers as well as with a more general audience. The bilingual paper resources, a booklet of narratives and a Nisvai-French French-Nisvai lexicon, were designed for the Nisvai community by taking into account their future uses (i.e. primary school).


Building a Time-Aligned Cross-Linguistic Reference Corpus from Language Documentation Data (DoReCo)

Ludger Paschen, François Delafontaine, Christoph Draxler, Susanne Fuchs, Matthew Stave and Frank Seifart

Natural speech data on many languages have been collected by language documentation projects aiming to preserve lingustic and cultural traditions in audivisual records. These data hold great potential for large-scale cross-linguistic research into phonetics and language processing. Major obstacles to utilizing such data for typological studies include the non-homogenous nature of file formats and annotation conventions found both across and within archived collections. Moreover, time-aligned audio transcriptions are typically only available at the level of broad (multi-word) phrases but not at the word and segment levels. We report on solutions developed for these issues within the DoReCo (DOcumentation REference COrpus) project. DoReCo aims at providing time-aligned transcriptions for at least 50 collections of under-resourced languages. This paper gives a preliminary overview of the current state of the project and details our workflow, in particular standardization of formats and conventions, the addition of segmental alignments with WebMAUS, and DoReCo’s applicability for subsequent research programs. By making the data accessible to the scientific community, DoReCo is designed to bridge the gap between language documentation and linguistic inquiry.


Benchmarking Neural and Statistical Machine Translation on Low-Resource African Languages

Kevin Duh, Paul McNamee, Matt Post and Brian Thompson

Research in machine translation (MT) is developing at a rapid pace. However, most work in the community has focused on languages where large amounts of digital resources are available. In this study, we benchmark state of the art statistical and neural machine translation systems on two African languages which do not have large amounts of resources: Somali and Swahili. These languages are of social importance and serve as test-beds for developing technologies that perform reasonably well despite the low-resource constraint. Our findings suggest that statistical machine translation (SMT) and neural machine translation (NMT) can perform similarly in low-resource scenarios, but neural systems require more careful tuning to match performance. We also investigate how to exploit additional data, such as bilingual text harvested from the web, or user dictionaries; we find that NMT can significantly improve in performance with the use of these additional data. Finally, we survey the landscape of machine translation resources for the languages of Africa and provide some suggestions for promising future research directions.


Improved Finite-State Morphological Analysis for St. Lawrence Island Yupik Using Paradigm Function Morphology

Emily Chen, Hyunji Hayley Park and Lane Schwartz

St. Lawrence Island Yupik is an endangered polysynthetic language of the Bering Strait region. While conducting linguistic fieldwork between 2016 and 2019, we observed substantial support within the Yupik community for language revitalization and for resource development to support Yupik education. To that end, Chen & Schwartz (2018) implemented a finite-state morphological analyzer as a critical enabling technology for use in Yupik language education and technology. Chen & Schwartz (2018) reported a morphological analysis coverage rate of approximately 75% on a dataset of 60K Yupik tokens, leaving considerable room for improvement. In this work, we present a re-implementation of the Chen & Schwartz (2018) finite-state morphological analyzer for St. Lawrence Island Yupik that incorporates new linguistic insights; in particular, in this implementation we make use of the Paradigm Function Morphology (PFM) theory of morphology. We evaluate this new PFM-based morphological analyzer, and demonstrate that it consistently outperforms the existing analyzer of Chen & Schwartz (2018) with respect to accuracy and coverage rate across multiple datasets.


Towards a Spell Checker for Zamboanga Chavacano Orthography

Marcelo Yuji Himoro and Antonio Pareja-Lora

Zamboanga Chabacano (ZC) is the most vibrant variety of Philippine Creole Spanish, with over 400,000 native speakers in the Philippines (as of 2010). Following its introduction as a subject and a medium of instruction in the public schools of Zamboanga City from Grade 1 to 3 in 2012, an official orthography for this variety – the so-called “Zamboanga Chavacano Orthography” – has been approved in 2014. Its complexity, however, is a barrier to most speakers, since it does not necessarily reflect the particular phonetic evolution in ZC, but favours etymology instead. The distance between the correct spelling and the different spelling variations is often so great that delivering acceptable performance with the current de facto spell checking technologies may be challenging. The goals of this research have been to propose i) a spelling error taxonomy for ZC, formalised as an ontology and ii) an adaptive spell checking approach using Character-Based Statistical Machine Translation to correct spelling errors in ZC. Our results show that this approach is suitable for the goals mentioned and that it could be combined with other current spell checking technologies to achieve even higher performance.


Identifying Sentiments in Algerian Code-switched User-generated Comments

Wafia Adouane, Samia Touileb and Jean-Philippe Bernardy

We present in this paper our work on Algerian language, an under-resourced North African colloquial Arabic variety, for which we built a comparably large corpus of more than 36,000 code-switched user-generated comments annotated for sentiments. We opted for this data domain because Algerian is a colloquial language with no existing freely available corpora. Moreover, we compiled sentiment lexicons of positive and negative unigrams and bigrams reflecting the code-switches present in the language. We compare the performance of four models on the task of identifying sentiments, and the results indicate that a CNN model trained end-to-end fits better our unedited code-switched and unbalanced data across the predefined sentiment classes. Additionally, injecting the lexicons as background knowledge to the model boosts its performance on the minority class with a gain of 10.54 points on the F-score. The results of our experiments can be used as a baseline for future research for Algerian sentiment analysis.


Automatic Creation of Text Corpora for Low-Resource Languages from the Internet: The Case of Swiss German

Lucy Linder, Michael Jungo, Jean Hennebert, Claudiu Cristian Musat and Andreas Fischer

This paper presents SwissCrawl, the largest Swiss German text corpus to date. Composed of more than half a million sentences, it was generated using a customized web scraping tool that could be applied to other low-resource languages as well. The approach demonstrates  how freely available web pages can be used to construct comprehensive text corpora, which are of fundamental importance for natural language processing. In an experimental evaluation, we show that using the new corpus leads to significant improvements for the task of language modeling.


Evaluating Sub-word Embeddings in Cross-lingual Models

Ali Hakimi Parizi and Paul Cook

Cross-lingual word embeddings create a shared space for embeddings in two languages, and enable knowledge to be transferred between languages for tasks such as bilingual lexicon induction. One problem, however, is out-of-vocabulary (OOV) words, for which no embeddings are available. This is particularly problematic for low-resource and morphologically-rich languages, which often have relatively high OOV rates. Approaches to learning sub-word embeddings have been proposed to address the problem of OOV words, but most prior work has not considered sub-word embeddings in cross-lingual models. In this paper, we consider whether sub-word embeddings can be leveraged to form cross-lingual embeddings for OOV words. Specifically, we consider a novel bilingual lexicon induction task focused on OOV words, for language pairs covering several language families. Our results indicate that cross-lingual representations for OOV words can indeed be formed from sub-word embeddings, including in the case of a truly low-resource morphologically-rich language.


A Swiss German Dictionary: Variation in Speech and Writing

Larissa Schmidt, Lucy Linder, Sandra Djambazovska, Alexandros Lazaridis, Tanja Samardžić and Claudiu Musat

We introduce a dictionary containing normalized forms of common words in various Swiss German dialects into High German. As Swiss German is, for now, a predominantly spoken language, there is a significant variation in the written forms, even between speakers of the same dialect. To alleviate the uncertainty associated with this diversity, we complement the pairs of Swiss German – High German words with the Swiss German phonetic transcriptions (SAMPA). This dictionary becomes thus the first resource to combine large-scale spontaneous translation with phonetic transcriptions. Moreover, we control for the regional distribution and insure the equal representation of the major Swiss dialects. The coupling of the phonetic and written Swiss German forms is powerful. We show that they are sufficient to train a Transformer-based phoneme to grapheme model that generates credible novel Swiss German writings. In addition, we show that the inverse mapping – from graphemes to phonemes – can be modeled with a transformer trained with the novel dictionary. This generation of pronunciations for previously unknown words is key in training extensible automated speech recognition (ASR) systems, which are key beneficiaries of this dictionary.


Towards a Corsican Basic Language Resource Kit

Laurent Kevers and Stella Retali-Medori

The current situation regarding the existence of natural language processing (NLP) resources and tools for Corsican reveals their virtual non-existence. Our inventory contains only a few rare digital resources, lexical or corpus databases, requiring adaptation work. Our objective is to use the Banque de Données Langue Corse project (BDLC) to improve the availability of resources and tools for the Corsican language and, in the long term, provide a complete Basic Language Ressource Kit (BLARK). We have defined a roadmap setting out the actions to be undertaken: the collection of corpora and the setting up of a consultation interface (concordancer), and of a language detection tool, an electronic dictionary and a part-of-speech tagger. The first achievements regarding these topics have already been reached and are presented in this article. Some elements are also available on our project page (


Evaluating the Impact of Sub-word Information and Cross-lingual Word Embeddings on Mi’kmaq

Jeremie Boudreau, Akankshya Patra, Ashima Suvarna and Paul Cook

Mi’kmaq is an Indigenous language spoken primarily in Eastern Canada. It is polysynthetic and low-resource. In this paper we consider a range of n-gram and RNN language models for Mi’kmaq. We find that an RNN language model, initialized with pre-trained fastText embeddings, performs best, highlighting the importance of sub-word information for Mi’kmaq. We further consider approaches to that incorporate cross-lingual word embeddings, but do not see improvements with these models. Finally we consider language models that operate over segmentations produced by SentencePiece — which include sub-word units as tokens — as opposed to word-level models. We see improvements for this approach over word-level language models, again indicating that sub-word modelling is important for Mi’kmaq


Exploring a Choctaw Language Corpus with Word Vectors and Minimum Distance Length

Jacqueline Brixey, David Sides, Timothy Vizthum, David Traum and Khalil Iskarous

This work introduces additions to the corpus ChoCo, a multimodal corpus for the American indigenous language Choctaw. Using texts from the corpus, we develop new computational resources by using two off-the-shelf tools: word2vec and Linguistica. Our work illustrates how these tools can be successfully implemented with a small corpus.


Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yorùbá and Twi

Jesujoba Alabi, Kwabena Amponsah-Kaakyire, David Adelani and Cristina España-Bonet

The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. For low-resourced languages, the evaluation is more difficult and normally ignored, with the hope that the impressive capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced setting too. In this paper we focus on two African languages, Yorùbá and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing. We analyse the noise in the publicly available corpora, collect high quality and noisy data for the two languages and quantify the improvements that depend not only on the amount of data but on the quality too. We also use different architectures that learn word representations both from surface forms and characters to further exploit all the available information which showed to be important for these languages. For the evaluation, we manually translate the wordsim-353 word pairs dataset from English into Yorùbá and Twi. We extend the analysis to contextual word embeddings and evaluate multilingual BERT on a named entity recognition task. For this, we annotate with named entities the Global Voices corpus for Yorùbá. As output of the work, we provide corpora, embeddings and the test suits for both languages.


TRopBank: Turkish PropBank V2.0

Neslihan Kara, Deniz Baran Aslan, Büşra Marşan, Özge Bakay, Koray Ak and Olcay Taner Yıldız

In this paper, we present and explain TRopBank “Turkish PropBank v2.0”. PropBank is a hand-annotated corpus of propositions which is used to obtain the predicate-argument information of a language. Predicate-argument information of a language can help understand semantic roles of arguments. “Turkish PropBank v2.0”, unlike PropBank v1.0, has a much more extensive list of Turkish verbs, with 17.673 verbs in total.


Collection and Annotation of the Romanian Legal Corpus

Dan Tufiș, Maria Mitrofan, Vasile Păiș, Radu Ion and Andrei Coman

We present the Romanian legislative corpus which is a valuable linguistic asset for the development of machine translation systems, especially for under-resourced languages. The knowledge that can be extracted from this resource is necessary for a deeper understanding of how law terminology is used and how it can be made more consistent. At this moment the corpus contains more than 140k documents representing the legislative body of Romania. This corpus is processed and annotated at different levels: linguistically (tokenized, lemmatized and pos-tagged), dependency parsed, chunked, named entities identified and labeled with IATE terms and EUROVOC descriptors. Each annotated document has a CONLL-U Plus format consisting in 14 columns, in addition to the standard 10-column format, four other types of annotations were added. Moreover the repository will be periodically updated as new legislative texts are published. These will be automatically collected and transmitted to the processing and annotation pipeline. The access to the corpus will be done through ELRC infrastructure.


An Empirical Evaluation of Annotation Practices in Corpora from Language Documentation

Kilu von Prince and Sebastian Nordhoff

For most of the world’s languages, no primary data are available, even as many languages are disappearing. Throughout the last two decades, however, language documentation projects have produced substantial amounts of primary data from a wide variety of endangered languages. These resources are still in the early days of their exploration. One of the factors that makes them hard to use is a relative lack of standardized annotation conventions. In this paper, we will describe common practices in existing corpora in order to facilitate their future processing. After a brief introduction of the main formats used for annotation files, we will focus on commonly used tiers in the widespread ELAN and Toolbox formats. Minimally, corpora from language documentation contain a transcription tier and an aligned translation tier, which means they constitute parallel corpora. Additional common annotations include named references, morpheme separation, morpheme-by-morpheme glosses, part-of-speech tags and notes.


Annotated Corpus for Sentiment Analysis in Odia Language

Gaurav Mohanty, Pruthwik Mishra and Radhika Mamidi

Given the lack of an annotated corpus of non-traditional Odia literature which serves as the standard when it comes sentiment analysis, we have created an annotated corpus of Odia sentences and made it publicly available to promote research in the field. Secondly, in order to test the usability of currently available Odia sentiment lexicon, we experimented with various classifiers by training and testing on the sentiment annotated corpus while using identified affective words from the same as features. Annotation and classification are done at sentence level as the usage of sentiment lexicon is best suited to sentiment analysis at this level. The created corpus contains 2045 Odia sentences from news domain annotated with sentiment labels using a well-defined annotation scheme. An inter-annotator agreement score of 0.79 is reported for the corpus.


Building a Task-oriented Dialog System for Languages with no Training Data: the Case for Basque

Maddalen López de Lacalle, Xabier Saralegi and Iñaki San Vicente

This paper presents an approach for developing a task-oriented dialog system for less-resourced languages in scenarios where training data is not available. Both intent classification and slot filling are tackled. We project the existing annotations in rich-resource languages by means of Neural Machine Translation (NMT) and posterior word alignments. We then compare training on the projected monolingual data with direct model transfer alternatives. Intent Classifiers and slot filling sequence taggers are implemented using a BiLSTM architecture or by fine-tuning BERT transformer models. Models learnt exclusively from Basque projected data provide better accuracies for slot filling. Combining Basque projected train data with rich-resource languages data outperforms consistently models trained solely on projected data for intent classification. At any rate, we achieve competitive performance in both tasks, with accuracies of 81% for intent classification and 77% for slot filling.


SENCORPUS: A French-Wolof Parallel Corpus

Elhadji Mamadou Nguer, Alla Lo, Cheikh M. Bamba Dione, Sileye O. Ba and Moussa Lo

In this paper, we report efforts towards the acquisition and construction of a bilingual parallel corpus between French and Wolof, a Niger-Congo language belonging to the Northern branch of the Atlantic group. The corpus is constructed as part of the SYSNET3LOc project. It currently contains about 70,000 French-Wolof parallel sentences drawn on various sources from different domains. The paper discusses the data collection procedure, conversion, and alignment of the corpus as well as it’s application as training data for neural machine translation. In fact, using this corpus, we were able to create word embedding models for Wolof with relatively good results. Currently, the corpus is being used to develop a neural machine translation model to translate French sentences into Wolof.


A Major Wordnet for a Minority Language: Scottish Gaelic

Gábor Bella, Fiona McNeill, Rody Gorman, Caoimhin O Donnaile, Kirsty MacDonald, Yamini Chandrashekar, Abed Alhakim Freihat and Fausto Giunchiglia

We present a new wordnet resource for Scottish Gaelic, a Celtic minority language spoken by about 60,000 speakers, most of whom live in Northwestern Scotland. The wordnet contains over 15 thousand word senses and was constructed by merging ten thousand new, high-quality translations, provided and validated by language experts, with an existing wordnet derived from Wiktionary. This new, considerably extended wordnet—currently among the 30 largest in the world—targets multiple communities: language speakers and learners; linguists; computer scientists solving problems related to natural language processing. By publishing it as a freely downloadable resource, we hope to contribute to the long-term preservation of Scottish Gaelic as a living language, both offline and on the Web.


Crowdsourcing Speech Data for Low-Resource Languages from Low-Income Workers

Basil Abraham, Danish Goel, Divya Siddarth, Kalika Bali, Manu Chopra, Monojit Choudhury, Pratik Joshi, Preethi Jyoti, Sunayana Sitaram and Vivek Seshadri

Voice-based technologies are essential to cater to the hundreds of millions of new smartphone users. However, most of the languages spoken by these new users have little to no labelled speech data. Unfortunately, collecting labelled speech data in any language is an expensive and resource-intensive task. Moreover, existing platforms typically collect speech data only from urban speakers familiar with digital technology whose dialects are often very different from low-income users. In this paper, we explore the possibility of collecting labelled speech data directly from low-income workers. In addition to providing diversity to the speech dataset, we believe this approach can also provide valuable supplemental earning opportunities to these communities. To this end, we conducted a study where we collected labelled speech data in the Marathi language from three different user groups: low-income rural users, low-income urban users, and university students. Overall, we collected 109 hours of data from 36 participants. Our results show that the data collected from low-income participants is of comparable quality to the data collected from university students (who are typically employed to do this work) and that crowdsourcing speech data from low-income rural and urban workers is a viable method of gathering speech data.


A Resource for Studying Chatino Verbal Morphology

Hilaria Cruz, Antonios Anastasopoulos and Gregory Stump

We present the first resource focusing on the verbal inflectional morphology of San Juan Quiahije Chatino, a tonal mesoamerican language spoken in Mexico. We provide a collection of complete inflection tables of 198 lemmata, with morphological tags based on the UniMorph schema. We also provide baseline results on three core NLP tasks: morphological analysis, lemmatization, and morphological inflection.


Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi

Devansh Mehta, Sebastin Santy, Ramaravind Kommiya Mothilal, Brij Mohan Lal Srivastava, Alok Sharma, Anurag Shukla, Vishnu Prasad, Venkanna U, Amit Sharma and Kalika Bali

The primary obstacle to developing technologies for low-resource languages is the lack of usable data. In this paper, we report the adaption and deployment of 4 technology-driven methods of data collection for Gondi, a low-resource vulnerable language spoken by around 2.3 million tribal people in south and central India. In the process of data collection, we also help in its revival by expanding access to information in Gondi through the creation of linguistic resources that can be used by the community, such as a dictionary, children’s stories, an app with Gondi content from multiple sources and an Interactive Voice Response (IVR) based mass awareness platform. At the end of these interventions, we collected a little less than 12,000 translated words and/or sentences and identified more than 650 community members whose help can be solicited for future translation efforts. The larger goal of the project is collecting enough data in Gondi to build and deploy viable language technologies like machine translation and speech to text systems that can help take the language onto the internet.


Irony Detection in Persian Language: A Transfer Learning Approach Using Emoji Prediction

Preni Golazizian, Behnam Sabeti, Seyed Arad Ashrafi Asli, Zahra Majdabadi, Omid Momenzadeh and reza fahmi

Irony is a linguistic device used to intend an idea while articulating an opposing expression. Many text analytic algorithms used for emotion extraction or sentiment analysis, produce invalid results due to the use of irony. Persian speakers use this device more often due to the language’s nature and some cultural reasons. This phenomenon also appears in social media platforms such as Twitter where users express their opinions using ironic or sarcastic posts. In the current research, which is the first attempt at irony detection in Persian language, emoji prediction is used to build a pretrained model. The model is finetuned utilizing a set of hand labeled tweets with irony tags. A bidirectional LSTM (BiLSTM) network is employed as the basis of our model which is improved by attention mechanism. Additionally, a Persian corpus for irony detection containing 4339 manually-labeled tweets is introduced. Experiments show the proposed approach outperforms the adapted state-of-the-art method tested on Persian dataset with an accuracy of 83.1%, and offers a strong baseline for further research in Persian language.


Towards Computational Resource Grammars for Runyankore and Rukiga

David Bamutura, Peter Ljunglöf and Peter Nebende

In this paper, we present computational resource grammars of Runyankore and Rukiga (R&R) languages. Runyankore and Rukiga are two under-resourced Bantu Languages spoken by about 6 million people indigenous to South- Western Uganda, East Africa. We used  Grammatical Framework (GF), a multilingual grammar formalism and a special- purpose functional programming language to formalise the descriptive grammar of these languages. To the best of our knowledge, these computational resource grammars are the first attempt to the creation of language resources for R&R. In Future Work, we plan to use these grammars to bootstrap the generation of other linguistic resources such as multilingual corpora that make use of data-driven approaches to natural language processing feasible. In the meantime, they can be used to build Computer-Assisted Language Learning (CALL) applications for these languages among others.


Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in Persian

Seyed Arad Ashrafi Asli, Behnam Sabeti, Zahra Majdabadi, Preni Golazizian, reza fahmi and Omid Momenzadeh

Deep learning models are the current State-of-the-art methodologies towards many real-world problems. However, they need a substantial amount of labeled data to be trained appropriately. Acquiring labeled data can be challenging in some particular domains or less-resourced languages. There are some practical solutions regarding these issues, such as Active Learning and Transfer Learning. Active learning’s idea is simple: let the model choose the samples for annotation instead of labeling the whole dataset. This method leads to a more efficient annotation process. Active Learning models can achieve the baseline performance (the accuracy of the model trained on the whole dataset), with a considerably lower amount of labeled data. Several active learning approaches are tested in this work, and their compatibility with Persian is examined using a brand-new sentiment analysis dataset that is also introduced in this work. MirasOpinion, which to our knowledge is the largest Persian sentiment analysis dataset, is crawled from a Persian e-commerce website and annotated using a crowd-sourcing policy. LDA sampling, which is an efficient Active Learning strategy using Topic Modeling, is proposed in this research. Active Learning Strategies have shown promising results in the Persian language, and LDA sampling showed a competitive performance compared to other approaches.


BanFakeNews: A Dataset for Detecting Fake News in Bangla

Md Zobaer Hossain, Md Ashraful Rahman, Md Saiful Islam and Sudipta Kar

Observing the damages that can be done by the rapid propagation of fake news in various sectors like politics and finance, automatic identification of fake news using linguistic analysis has drawn the attention of the research community. However, such methods are largely being developed for English where low resource languages remain out of the focus. But the risks spawned by fake and manipulative news are not confined by languages. In this work, we propose an annotated dataset of ≈ 50K news that can be used for building automated fake news detection systems for a low resource language like Bangla. Additionally, we provide an analysis of the dataset and develop a benchmark system with state of the art NLP techniques to identify Bangla fake news. To create this system, we explore traditional linguistic features and neural network based methods. We expect this dataset will be a valuable resource for building technologies to prevent the spreading of fake news and contribute in research with low resource languages. The dataset and source code are publicly available at


A Resource for Computational Experiments on Mapudungun

Mingjun Duan, Carlos Fasola, Sai Krishna Rallabandi, Rodolfo Vega, Antonios Anastasopoulos, Lori Levin and Alan W Black

We present a resource for computational experiments on Mapudungun, a polysynthetic indigenous language spoken in Chile with upwards of 200 thousand speakers.  We provide 142 hours of culturally significant conversations in the domain of medical treatment. The conversations are fully transcribed and translated into Spanish. The transcriptions also include annotations for code-switching and non-standard pronunciations. We also provide baseline results on three core NLP tasks: speech recognition, speech synthesis, and machine translation between Spanish and Mapudungun. We further explore other applications for which the corpus will be suitable, including the study of code-switching, historical orthography change, linguistic structure, and sociological and anthropological studies.


Automated Parsing of Interlinear Glossed Text from Page Images of Grammatical Descriptions

Erich Round, Mark Ellison, Jayden Macklin-Cordes and Sacha Beniamine

Linguists seek insight from all human languages, however accessing information from most of the full store of extant global linguistic descriptions is not easy. One of the most common kinds of information that linguists have documented is vernacular sentences, as recorded in descriptive grammars. Typically these sentences are formatted as interlinear glossed text (IGT). Most descriptive grammars, however, exist only as hardcopy or scanned pdf documents. Consequently, parsing IGTs in scanned grammars is a priority, in order to significantly increase the volume of documented linguistic information that is readily accessible. Here we demonstrate fundamental viability for a technology that can assist in making a large number of linguistic data sources machine readable: the automated identification and parsing of interlinear glossed text from scanned page images. For example, we attain high median precision and recall (>0.95) in the identification of examples sentences in IGT format. Our results will be of interest to those who are keen to see more of the existing documentation of human language, especially for, become more readily accessible.


The Johns Hopkins University Bible Corpus: 1600+ Tongues for Typological Exploration

Arya D. McCarthy, Rachel Wicks, Dylan Lewis, Aaron Mueller, Winston Wu, Oliver Adams, Garrett Nicolai, Matt Post and David Yarowsky

We present findings from the creation of a massively parallel corpus in over 1600 languages, the Johns Hopkins University Bible Corpus (JHUBC). The corpus consists of over 4000 unique translations of the Christian Bible and counting. Our data is derived from scraping several online resources and merging them with existing corpora, combining them under a common scheme that is verse-parallel across all translations. We detail our effort to scrape, clean, align, and utilize this ripe multilingual dataset. The corpus captures the great typological variety of the world’s languages. We catalog this by showing highly similar proportions of representation of Ethnologue’s typological features in our corpus. We also give an example application: projecting pronoun features like clusivity across alignments to richly annotate languages which do not mark the distinction.


Towards Building an Automatic Transcription System for Language Documentation: Experiences from Muyu

Alexander Zahrer, Andrej Zgank and Barbara Schuppler

Since at least half of the world’s 6000 plus languages will vanish during the 21st century, language documentation has become a rapidly growing field in linguistics. A fundamental challenge for language documentation is the ”transcription bottleneck”. Speech technology may deliver the decisive breakthrough for overcoming the transcription bottleneck. This paper presents first experiments from the development of ASR4LD, a new automatic speech recognition (ASR) based tool for language documentation (LD). The experiments are based on recordings from an ongoing documentation project for the endangered Muyu language in New Guinea. We compare phoneme recognition experiments with American English, Austrian German and Slovenian as source language and Muyu as target language. The Slovenian acoustic models achieve the by far best performance (43.71% PER) in comparison to 57.14% PER with American English, and 89.49% PER with Austrian German. Whereas part of the errors can be explained by phonetic variation, the recording mismatch poses a major problem. On the long term, ASR4LD will not only be an integral part of the ongoing documentation project of Muyu, but will be further developed in order to facilitate also the language documentation process of other language groups.


Towards Flexible Cross-Resource Exploitation of Heterogeneous Language Documentation Data

Daniel Jettka and Timm Lehmberg

This paper reports on challenges and solution approaches in the development of methods for language resource overarching data analysis in the field of language documentation. It is based on the successful outcomes of the initial phase of an 18 year long-term project on lesser resourced and mostly endangered indigenous languages of the Northern Eurasian area, which included the finalization and publication of multiple language corpora and additional language resources. While aiming at comprehensive cross-resource data analysis, the project at the same time is confronted with a dynamic and complex resource landscape, especially resulting from a vast amount of multi-layered information stored in the form of analogue primary data in different widespread archives on the territory of the Russian Federation. The methods described aim at solving the tension between unification of data sets and vocabularies on the one hand and maximum openness for the integration of future resources and adaption of external information on the other hand.


CantoMap: a Hong Kong Cantonese MapTask Corpus

Grégoire Winterstein, Carmen Tang and Regine Lai

This work reports on the construction of a corpus of connected spoken Hong Kong Cantonese. The corpus aims at providing an additional resource for the study of modern (Hong Kong) Cantonese and also involves several controlled elicitation tasks which will serve different projects related to the phonology and semantics of Cantonese. The word-segmented corpus offers recordings, phonemic transcription, and Chinese characters transcription. The corpus contains a total of 768 minutes of recordings and transcripts of forty speakers. All the audio material has been aligned at utterance level with the transcriptions, using the ELAN transcription and annotation tool. The controlled elicitation task was based on the design of HCRC MapTask corpus (Anderson et al., 1991), in which participants had to communicate using solely verbal means as eye contact was restricted. In this paper, we outline the design of the maps and their landmarks and the basic segmentation principles of the data and various transcription conventions we adopted. We also compare the contents of Cantomap to those of comparable Cantonese corpora.


No Data to Crawl? Monolingual Corpus Creation from PDF Files of Truly low-Resource Languages in Peru

Gina Bustamante, Arturo Oncevay and Roberto Zariquiey

We introduce new monolingual corpora for four indigenous and endangered languages from Peru: Shipibo-konibo, Ashaninka, Yanesha and Yine. Given the total absence of these languages in the web, the extraction and processing of texts from PDF files is relevant in a truly low-resource language scenario. Our procedure for monolingual corpus creation considers language-specific and language-agnostic steps, and focuses on educational PDF files with multilingual sentences,  noisy pages and low-structured content. Through an evaluation based on  and character-level perplexity on a subset of manually extracted sentences, we determine that our method allows the creation of clean corpora for the four languages, a key resource for natural language processing tasks nowadays.


Creating a Parallel Icelandic Dependency Treebank from Raw Text to Universal Dependencies

Hildur Jónsdóttir and Anton Karl Ingason

Making the low-resource language, Icelandic, accessible and usable in Language Technology is a work in progress and is supported by the Icelandic government. Creating resources and suitable training data (e.g., a dependency treebank) is a fundamental part of that work. We describe work on a parallel Icelandic dependency treebank based on Universal Dependencies (UD). This is important because it is the first parallel treebank resource for the language and since several other languages already have a resource based on the same text. Two Icelandic treebanks based on phrase-structure grammar have been  built and ongoing work aims to convert them to UD. Previously, limited work has been done on dependency grammar for Icelandic. The current project aims to ameliorate this situation by creating a small dependency treebank from scratch. Creating a treebank is a laborious task so the process was implemented in an accessible manner using freely available tools and resources. The parallel data in the UD project was chosen as a source because this would furthermore give us the first parallel treebank for Icelandic. The Icelandic parallel UD corpus will be published as part of UD version 2.6.


Building a Universal Dependencies Treebank for Occitan

Aleksandra Miletic, Myriam Bras, Marianne Vergez-Couret, Louise Esher, Clamença Poujade and Jean Sibille

This paper outlines the ongoing effort of creating the first treebank for Occitan, a low-ressourced regional language spoken mainly in the south of France. We briefly present the global context of the project and report on its current status. We adopt the Universal Dependencies framework for this project. Our methodology is based on two main principles. Firstly, in order to guarantee the annotation quality, we use the agile annotation approach. Secondly, we rely on pre-processing using existing tools (taggers and parsers) to facilitate the work of human annotators, mainly through a delexicalized cross-lingual parsing approach. We present the results available at this point (annotation guidelines and a sub-corpus annotated with PoS tags and lemmas) and give the timeline for the rest of the work.


Building the Old Javanese Wordnet

David Moeljadi and Zakariya Pamuji Aminullah

This paper discusses the construction and the ongoing development of the Old Javanese Wordnet. The words were extracted from the digitized version of the Old Javanese–English Dictionary (Zoetmulder, 1982). The wordnet is built using the ‘expansion’ approach (Vossen, 1998), leveraging on the Princeton Wordnet’s core synsets and semantic hierarchy, as well as scientific names. The main goal of our project was to produce a high quality, human-curated resource. As of December 2019, the Old Javanese Wordnet contains 2,054 concepts or synsets and 5,911 senses. It is released under a Creative Commons Attribution 4.0 International License (CC BY 4.0). We are still developing it and adding more synsets and senses. We believe that the lexical data made available by this wordnet will be useful for a variety of future uses such as the development of Modern Javanese Wordnet and many language processing tasks and linguistic research on Javanese.


CPLM, a Parallel Corpus for Mexican Languages: Development and Interface

Gerardo Sierra Martínez, Cynthia Montaño, Gemma Bel-Enguix, Diego Córdova and Margarita Mota Montoya

Mexico is a Spanish speaking country that has a great language diversity, with 68 linguistic groups and 364 varieties. As they  face  a  lack  of  representation in education,  government,  public services and media, they present high levels of endangerment. Due to the lack of data available on social media and the internet, few technologies have been developed for these languages. To analyze different linguistic phenomena in the country, the Language Engineering Group developed the Corpus Paralelo de Lenguas Mexicanas (CPLM) [The Mexican Languages Parallel Corpus], a collaborative parallel corpus for the low-resourced languages of Mexico. The CPLM aligns Spanish with six indigenous languages: Maya, Ch’ol, Mazatec, Mixtec, Otomi, and Nahuatl. First, this paper describes the process of building the CPLM: text searching, digitalization and alignment process. Furthermore, we present some difficulties regarding dialectal and orthographic variations. Second, we present the interface and types of searching as well as the use of filters.


SiNER: A Large Dataset for Sindhi Named Entity Recognition

Wazir Ali, Junyu Lu and Zenglin Xu

We introduce the SiNER: a named entity recognition (NER) dataset for low-resourced Sindhi language with quality baselines. It contains 1,338 news articles and more than 1.35 million tokens collected from Kawish and Awami Awaz Sindhi newspapers using the begin-inside-outside (BIO) tagging scheme. The proposed dataset is likely to be a significant resource for statistical Sindhi language processing. The ultimate goal of developing SiNER is to present a gold-standard dataset for Sindhi NER along with quality baselines. We implement several baseline approaches of conditional random field (CRF) and recent popular state-of-the-art bi-directional long-short term memory (Bi-LSTM) models. The promising F1-score of 89.16 outputted by the Bi-LSTM-CRF model with character-level representations demonstrates the quality of our proposed SiNER dataset.

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  • LREC 2020 Paper Dissemination (10/10)