pwc-archive/papers-with-abstracts huggingface dataset:
Building the Spanish-Croatian Parallel Corpus
Authors: Bojana Mikeleni{\'c}, Marko Tadi{\'c}
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: POS, Sentence, Translation

This paper describes the building of the first Spanish-Croatian unidirectional parallel corpus, which has been constructed at the Faculty of Humanities and Social Sciences of the University of Zagreb. The corpus is comprised of eleven Spanish novels and their translations to Croatian done by six different professional translators. All the texts were published between 1999 and 2012. The corpus has more than 2 Mw, with approximately 1 Mw for each language. It was automatically sentence segmented and aligned, as well as manually post-corrected, and contains 71,778 translation units. In order to protect the copyright and to make the corpus available under permissive CC-BY licence, the aligned translation units are shuffled. This limits the usability of the corpus for research of language units at sentence and lower language levels only. There are two versions of the corpus in TMX format that will be available for download through META-SHARE and CLARIN ERIC infrastructure. The former contains plain TMX, while the latter is lemmatised and POS-tagged and stored in the aTMX format.
DerivBase.Ru: a Derivational Morphology Resource for Russian
Authors: Daniil Vodolazsky
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Russian morphology has been studied for decades, but there is still no large high coverage resource that contains the derivational families (groups of words that share the same root) of Russian words. The number of words used in different areas of the language grows rapidly, thus the human-made dictionaries published long time ago cannot cover the neologisms and the domain-specific lexicons. To fill such resource gap, we have developed a rule-based framework for deriving words and we applied it to build a derivational morphology resource named DerivBase.Ru, which we introduce in this paper.
Machine Learning and Deep Neural Network-Based Lemmatization and Morphosyntactic Tagging for Serbian
Authors: Ranka Stankovic, {\v{S}}, Branislava rih, Cvetana Krstev, Milo{\v{s}} Utvi{\'c}, Mihailo Skoric
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: BIG-bench Machine Learning, Lemmatization, POS, POS Tagging

The training of new tagger models for Serbian is primarily motivated by the enhancement of the existing tagset with the grammatical category of a gender. The harmonization of resources that were manually annotated within different projects over a long period of time was an important task, enabled by the development of tools that support partial automation. The supporting tools take into account different taggers and tagsets. This paper focuses on TreeTagger and spaCy taggers, and the annotation schema alignment between Serbian morphological dictionaries, MULTEXT-East and Universal Part-of-Speech tagset. The trained models will be used to publish the new version of the Corpus of Contemporary Serbian as well as the Serbian literary corpus. The performance of developed taggers were compared and the impact of training set size was investigated, which resulted in around 98{\%} PoS-tagging precision per token for both new models. The sr{\_}basic annotated dataset will also be published.
Fine-grained Morphosyntactic Analysis and Generation Tools for More Than One Thousand Languages
Authors: Garrett Nicolai, Dylan Lewis, Arya D. McCarthy, Aaron Mueller, Winston Wu, David Yarowsky
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Reranking, Translation

Exploiting the broad translation of the Bible into the world{'}s languages, we train and distribute morphosyntactic tools for approximately one thousand languages, vastly outstripping previous distributions of tools devoted to the processing of inflectional morphology. Evaluation of the tools on a subset of available inflectional dictionaries demonstrates strong initial models, supplemented and improved through ensembling and dictionary-based reranking. Likewise, a novel type-to-token based evaluation metric allows us to confirm that models generalize well across rare and common forms alike
Cairo Student Code-Switch (CSCS) Corpus: An Annotated Egyptian Arabic-English Corpus
Authors: Mohamed Balabel, Injy Hamed, Slim Abdennadher, Ngoc Thang Vu, {\"O}zlem {\c{C}}etino{\u{g}}lu
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Code-switching has become a prevalent phenomenon across many communities. It poses a challenge to NLP researchers, mainly due to the lack of available data needed for training and testing applications. In this paper, we introduce a new resource: a corpus of Egyptian- Arabic code-switch speech data that is fully tokenized, lemmatized and annotated for part-of-speech tags. Beside the corpus itself, we provide annotation guidelines to address the unique challenges of annotating code-switch data. Another challenge that we address is the fact that Egyptian Arabic orthography and grammar are not standardized.
Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation
Authors: Alexey Sorokin
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Data Augmentation, Decoder, Morphological Inflection

We investigate how to improve quality of low-resource morphological inflection without annotating more data. We examine two methods, language models and data augmentation. We show that the model whose decoder that additionally uses the states of the langauge model improves the model quality by 1.5{\%} in combination with both baselines. We also demonstrate that the augmentation of data improves performance by 9{\%} in average when adding 1000 artificially generated word forms to the dataset.
Visual Modeling of Turkish Morphology
Authors: Berke {\"O}zen{\c{c}}, Ercan Solak
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Code Generation

In this paper, we describe the steps in a visual modeling of Turkish morphology using diagramming tools. We aimed to make modeling easier and more maintainable while automating much of the code generation. We released the resulting analyzer, MorTur, and the diagram conversion tool, DiaMor as free, open-source utilities. MorTur analyzer is also publicly available on its web page as a web service. MorTur and DiaMor are part of our ongoing efforts in building a set of natural language processing tools for Turkic languages under a consistent framework.
Morphological Segmentation for Low Resource Languages
Authors: Justin Mott, Ann Bies, Stephanie Strassel, Jordan Kodner, Caitlin Richter, Hongzhi Xu, Mitchell Marcus
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Diversity, Segmentation

This paper describes a new morphology resource created by Linguistic Data Consortium and the University of Pennsylvania for the DARPA LORELEI Program. The data consists of approximately 2000 tokens annotated for morphological segmentation in each of 9 low resource languages, along with root information for 7 of the languages. The languages annotated show a broad diversity of typological features. A minimal annotation scheme for segmentation was developed such that it could capture the patterns of a wide range of languages and also be performed reliably by non-linguist annotators. The basic annotation guidelines were designed to be language-independent, but included language-specific morphological paradigms and other specifications. The resulting annotated corpus is designed to support and stimulate the development of unsupervised morphological segmenters and analyzers by providing a gold standard for their evaluation on a more typologically diverse set of languages than has previously been available. By providing root annotation, this corpus is also a step toward supporting research in identifying richer morphological structures than simple morpheme boundaries.
Building an English-Chinese Parallel Corpus Annotated with Sub-sentential Translation Techniques
Authors: Yuming Zhai, Lufei Liu, Xinyi Zhong, Gbariel Illouz, Anne Vilnat
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, Sentence, Translation, Word Alignment

Human translators often resort to different non-literal translation techniques besides the literal translation, such as idiom equivalence, generalization, particularization, semantic modulation, etc., especially when the source and target languages have different and distant origins. Translation techniques constitute an important subject in translation studies, which help researchers to understand and analyse translated texts. However, they receive less attention in developing Natural Language Processing (NLP) applications. To fill this gap, one of our long term objectives is to have a better semantic control of extracting paraphrases from bilingual parallel corpora. Based on this goal, we suggest this hypothesis: it is possible to automatically recognize different sub-sentential translation techniques. For this original task, since there is no dedicated data set for English-Chinese, we manually annotated a parallel corpus of eleven genres. Fifty sentence pairs for each genre have been annotated in order to consolidate our annotation guidelines. Based on this data set, we conducted an experiment to classify between literal and non-literal translations. The preliminary results confirm our hypothesis. The corpus and code are available. We hope that this annotated corpus will be useful for linguistic contrastive studies and for fine-grained evaluation of NLP tasks, such as automatic word alignment and machine translation.
EMPAC: an English--Spanish Corpus of Institutional Subtitles
Authors: Iris Serrat Roozen, Jos{\'e} Manuel Mart{\'\i}nez Mart{\'\i}nez
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The EuroparlTV Multimedia Parallel Corpus (EMPAC) is a collection of subtitles in English and Spanish for videos from the EuropeanParliament{'}s Multimedia Centre. The corpus has been compiled with the EMPAC toolkit. The aim of this corpus is to provide a resource to study institutional subtitling on the one hand, and, on the other hand, facilitate the analysis of web accessibility to institutional multimedia content. The corpus covers a time span from 2009 to 2017, it is made up of 4,000 texts amounting to two and half millions of tokens for every language, corresponding to approximately 280 hours of video. This paper provides 1) a review of related corpora; 2) a revision of typical compilation methodologies of subtitle corpora; 3) a detailed account of the corpus compilation methodology followed; and, 4) a description of the corpus. In the conclusion, the key findings are summarised regarding formal aspects of the subtitles conditioning the accessibility to the multimedia content of the EuroparlTV.
How Universal are Universal Dependencies? Exploiting Syntax for Multilingual Clause-level Sentiment Detection
Authors: Hiroshi Kanayama, Ran Iwamoto
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Benchmarking, BIG-bench Machine Learning

This paper investigates clause-level sentiment detection in a multilingual scenario. Aiming at a high-precision, fine-grained, configurable, and non-biased system for practical use cases, we have designed a pipeline method that makes the most of syntactic structures based on Universal Dependencies, avoiding machine-learning approaches that may cause obstacles to our purposes. We achieved high precision in sentiment detection for 17 languages and identified the advantages of common syntactic structures as well as issues stemming from structural differences on Universal Dependencies. In addition to reusable tips for handling multilingual syntax, we provide a parallel benchmarking data set for further research.
SpiCE: A New Open-Access Corpus of Conversational Bilingual Speech in Cantonese and English
Authors: Khia A. Johnson, Molly Babel, Ivan Fong, Nancy Yiu
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentence, Speech-to-Text

This paper describes the design, collection, orthographic transcription, and phonetic annotation of SpiCE, a new corpus of conversational Cantonese-English bilingual speech recorded in Vancouver, Canada. The corpus includes high-quality recordings of 34 early bilinguals in both English and Cantonese{---}to date, 27 have been recorded for a total of 19 hours of participant speech. Participants completed a sentence reading task, storyboard narration, and conversational interview in each language. Transcription and annotation for the corpus are currently underway. Transcripts produced with Google Cloud Speech-to-Text are available for all participants, and will be included in the initial SpiCE corpus release. Hand-corrected orthographic transcripts and force-aligned phonetic transcripts will be released periodically, and upon completion for all recordings, comprise the second release of the corpus. As an open-access language resource, SpiCE will promote bilingualism research for a typologically distinct pair of languages, of which Cantonese remains understudied despite there being millions of speakers around the world. The SpiCE corpus is especially well-suited for phonetic research on conversational speech, and enables researchers to study cross-language within-speaker phenomena for a diverse group of early Cantonese-English bilinguals. These are areas with few existing high-quality resources.
Identifying Cognates in English-Dutch and French-Dutch by means of Orthographic Information and Cross-lingual Word Embeddings
Authors: Els Lefever, Sofie Labat, Pranaydeep Singh
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Cross-Lingual Word Embeddings, Word Embeddings

This paper investigates the validity of combining more traditional orthographic information with cross-lingual word embeddings to identify cognate pairs in English-Dutch and French-Dutch. In a first step, lists of potential cognate pairs in English-Dutch and French-Dutch are manually labelled. The resulting gold standard is used to train and evaluate a multi-layer perceptron that can distinguish cognates from non-cognates. Fifteen orthographic features capture string similarities between source and target words, while the cosine similarity between their word embeddings represents the semantic relation between these words. By adding domain-specific information to pretrained fastText embeddings, we are able to obtain good embeddings for words that did not yet have a pretrained embedding (e.g. Dutch compound nouns). These embeddings are then aligned in a cross-lingual vector space by exploiting their structural similarity (cf. adversarial learning). Our results indicate that although the classifier already achieves good results on the basis of orthographic information, the performance further improves by including semantic information in the form of cross-lingual word embeddings.
Lexicogrammatic translationese across two targets and competence levels
Authors: Maria Kunilovskaya, Ekaterina Lapshinova-Koltunski
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Specificity, Translation, Vocal Bursts Valence Prediction

This research employs genre-comparable data from a number of parallel and comparable corpora to explore the specificity of translations from English into German and Russian produced by students and professional translators. We introduce an elaborate set of human-interpretable lexicogrammatic translationese indicators and calculate the amount of translationese manifested in the data for each target language and translation variety. By placing translations into the same feature space as their sources and the genre-comparable non-translated reference texts in the target language, we observe two separate translationese effects: a shift of translations into the gap between the two languages and a shift away from either language. These trends are linked to the features that contribute to each of the effects. Finally, we compare the translation varieties and find out that the professionalism levels seem to have some correlation with the amount and types of translationese detected, while each language pair demonstrates a specific socio-linguistically determined combination of the translationese effects.
CanVEC - the Canberra Vietnamese-English Code-switching Natural Speech Corpus
Authors: Li Nguyen, Christopher Bryant
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: POS

This paper introduces the Canberra Vietnamese-English Code-switching corpus (CanVEC), an original corpus of natural mixed speech that we semi-automatically annotated with language information, part of speech (POS) tags and Vietnamese translations. The corpus, which was built to inform a sociolinguistic study on language variation and code-switching, consists of 10 hours of recorded speech (87k tokens) between 45 Vietnamese-English bilinguals living in Canberra, Australia. We describe how we collected and annotated the corpus by pipelining several monolingual toolkits to considerably speed up the annotation process. We also describe how we evaluated the automatic annotations to ensure corpus reliability. We make the corpus available for research purposes.
A Spelling Correction Corpus for Multiple Arabic Dialects
Authors: Fadhl Eryani, Nizar Habash, Houda Bouamor, Salam Khalifa
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Spelling Correction, Text Normalization

Arabic dialects are the non-standard varieties of Arabic commonly spoken {--} and increasingly written on social media {--} across the Arab world. Arabic dialects do not have standard orthographies, a challenge for natural language processing applications. In this paper, we present the MADAR CODA Corpus, a collection of 10,000 sentences from five Arabic city dialects (Beirut, Cairo, Doha, Rabat, and Tunis) represented in the Conventional Orthography for Dialectal Arabic (CODA) in parallel with their raw original form. The sentences come from the Multi-Arabic Dialect Applications and Resources (MADAR) Project and are in parallel across the cities (2,000 sentences from each city). This publicly available resource is intended to support research on spelling correction and text normalization for Arabic dialects. We present results on a bootstrapping technique we use to speed up the CODA annotation, as well as on the degree of similarity across the dialects before and after CODA annotation.
A Dataset for Multi-lingual Epidemiological Event Extraction
Authors: Stephen Mutuvi, Antoine Doucet, Ga{\"e}l Lejeune, Moses Odeo
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles, Event Extraction, text-classification, Text Classification

This paper proposes a corpus for the development and evaluation of tools and techniques for identifying emerging infectious disease threats in online news text. The corpus can not only be used for information extraction, but also for other natural language processing (NLP) tasks such as text classification. We make use of articles published on the Program for Monitoring Emerging Diseases (ProMED) platform, which provides current information about outbreaks of infectious diseases globally. Among the key pieces of information present in the articles is the uniform resource locator (URL) to the online news sources where the outbreaks were originally reported. We detail the procedure followed to build the dataset, which includes leveraging the source URLs to retrieve the news reports and subsequently pre-processing the retrieved documents. We also report on experimental results of event extraction on the dataset using the Data Analysis for Information Extraction in any Language(DAnIEL) system. DAnIEL is a multilingual news surveillance system that leverages unique attributes associated with news reporting to extract events: repetition and saliency. The system has wide geographical and language coverage, including low-resource languages. In addition, we compare different classification approaches in terms of their ability to differentiate between epidemic-related and unrelated news articles that constitute the corpus.
Swiss-AL: A Multilingual Swiss Web Corpus for Applied Linguistics
Authors: Julia Krasselt, Philipp Dressen, Matthias Fluor, Cerstin Mahlow, Klaus Rothenh{\"a}usler, Maren Runte
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The Swiss Web Corpus for Applied Linguistics (Swiss-AL) is a multilingual (German, French, Italian) collection of texts from selected web sources. Unlike most other web corpora it is not intended for NLP purposes, but rather designed to support data-based and data-driven research on societal and political discourses in Switzerland. It currently contains 8 million texts (approx. 1.55 billion tokens), including news and specialist publications, governmental opinions, and parliamentary records, web sites of political parties, companies, and universities, statements from industry associations and NGOs, etc. A flexible processing pipeline using state-of-the-art components allows researchers in applied linguistics to create tailor-made subcorpora for studying discourse in a wide range of domains. So far, Swiss-AL has been used successfully in research on Swiss public discourses on energy and on antibiotic resistance.
Analysis of GlobalPhone and Ethiopian Languages Speech Corpora for Multilingual ASR
Authors: Martha Yifiru Tachbelie, Solomon Teferra Abate, Tanja Schultz
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

In this paper, we present the analysis of GlobalPhone (GP) and speech corpora of Ethiopian languages (Amharic, Tigrigna, Oromo and Wolaytta). The aim of the analysis is to select speech data from GP for the development of multilingual Automatic Speech Recognition (ASR) system for the Ethiopian languages. To this end, phonetic overlaps among GP and Ethiopian languages have been analyzed. The result of our analysis shows that there is much phonetic overlap among Ethiopian languages although they are from three different language families. From GP, Turkish, Uyghur and Croatian are found to have much overlap with the Ethiopian languages. On the other hand, Korean has less phonetic overlap with the rest of the languages. Moreover, morphological complexity of the GP and Ethiopian languages, reflected by type to token ration (TTR) and out of vocabulary (OOV) rate, has been analyzed. Both metrics indicated the morphological complexity of the languages. Korean and Amharic have been identified as extremely morphologically complex compared to the other languages. Tigrigna, Russian, Turkish, Polish, etc. are also among the morphologically complex languages.
Multilingualization of Medical Terminology: Semantic and Structural Embedding Approaches
Authors: Long-Huei Chen, Kyo Kageura
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Term Extraction, Translation

The multilingualization of terminology is an essential step in the translation pipeline, to ensure the correct transfer of domain-specific concepts. Many institutions and language service providers construct and maintain multilingual terminologies, which constitute important assets. However, the curation of such multilingual resources requires significant human effort; though automatic multilingual term extraction methods have been proposed so far, they are of limited success as term translation cannot be satisfied by simply conveying meaning, but requires the terminologists and domain experts{'} knowledge to fit the term within the existing terminology. Here we propose a method to encode the structural property of a term by aligning their embeddings using graph convolutional networks trained from separate languages. We observe that the structural information can augment the semantic methods also explored in this work, and recognize the unique nature of terminologies allows our method to fully take advantage and produce superior results.