Ekaterina Vylomova


2024

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Proceedings of the 3rd Workshop on NLP Applications to Field Linguistics (Field Matters 2024)
Oleg Serikov | Ekaterina Voloshina | Anna Postnikova | Saliha Muradoglu | Eric Le Ferrand | Elena Klyachko | Ekaterina Vylomova | Tatiana Shavrina | Francis Tyers
Proceedings of the 3rd Workshop on NLP Applications to Field Linguistics (Field Matters 2024)

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Proceedings of the Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)
Atul Kr. Ojha | Chao-hong Liu | Ekaterina Vylomova | Flammie Pirinen | Jade Abbott | Jonathan Washington | Nathaniel Oco | Valentin Malykh | Varvara Logacheva | Xiaobing Zhao
Proceedings of the Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)

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Low-Resource Machine Translation through Retrieval-Augmented LLM Prompting: A Study on the Mambai Language
Raphaël Merx | Aso Mahmudi | Katrina Langford | Leo Alberto de Araujo | Ekaterina Vylomova
Proceedings of the 2nd Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia (EURALI) @ LREC-COLING 2024

This study explores the use of large language models (LLMs) for translating English into Mambai, a low-resource Austronesian language spoken in Timor-Leste, with approximately 200,000 native speakers. Leveraging a novel corpus derived from a Mambai language manual and additional sentences translated by a native speaker, we examine the efficacy of few-shot LLM prompting for machine translation (MT) in this low-resource context. Our methodology involves the strategic selection of parallel sentences and dictionary entries for prompting, aiming to enhance translation accuracy, using open-source and proprietary LLMs (LlaMa 2 70b, Mixtral 8x7B, GPT-4). We find that including dictionary entries in prompts and a mix of sentences retrieved through TF-IDF and semantic embeddings significantly improves translation quality. However, our findings reveal stark disparities in translation performance across test sets, with BLEU scores reaching as high as 21.2 on materials from the language manual, in contrast to a maximum of 4.4 on a test set provided by a native speaker. These results underscore the importance of diverse and representative corpora in assessing MT for low-resource languages. Our research provides insights into few-shot LLM prompting for low-resource MT, and makes available an initial corpus for the Mambai language.

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Proceedings of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
Michael Hahn | Alexey Sorokin | Ritesh Kumar | Andreas Shcherbakov | Yulia Otmakhova | Jinrui Yang | Oleg Serikov | Priya Rani | Edoardo M. Ponti | Saliha Muradoğlu | Rena Gao | Ryan Cotterell | Ekaterina Vylomova
Proceedings of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

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A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applications
Naomi Baes | Nick Haslam | Ekaterina Vylomova
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Historical linguists have identified multiple forms of lexical semantic change. We present a three-dimensional framework for integrating these forms and a unified computational methodology for evaluating them concurrently. The dimensions represent increases or decreases in semantic 1) sentiment (valence of a target word’s collocates), 2) intensity (emotional arousal of collocates or the frequency of intensifiers), and 3) breadth (diversity of contexts in which the target word appears). These dimensions can be complemented by evaluation of shifts in the frequency of the target words and the thematic content of its collocates. This framework enables lexical semantic change to be mapped economically and systematically and has applications in computational social science. We present an illustrative analysis of semantic shifts in mental health and mental illness in two corpora, demonstrating patterns of semantic change that illuminate contemporary concerns about pathologization, stigma, and concept creep.

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Simpson’s Paradox and the Accuracy-Fluency Tradeoff in Translation
Zheng Wei Lim | Ekaterina Vylomova | Trevor Cohn | Charles Kemp
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

A good translation should be faithful to the source and should respect the norms of the target language. We address a theoretical puzzle about the relationship between these objectives. On one hand, intuition and some prior work suggest that accuracy and fluency should trade off against each other, and that capturing every detail of the source can only be achieved at the cost of fluency. On the other hand, quality assessment researchers often suggest that accuracy and fluency are highly correlated and difficult for human raters to distinguish (Callison-Burch et al., 2007). We show that the tension between these views is an instance of Simpson’s paradox, and that accuracy and fluency are positively correlated at the level of the corpus but trade off at the level of individual source segments. We further suggest that the relationship between accuracy and fluency is best evaluated at the segment (or sentence) level, and that the trade off between these dimensions has implications both for assessing translation quality and developing improved MT systems.

2023

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Semantic Shifts in Mental Health-Related Concepts
Naomi Baes | Nick Haslam | Ekaterina Vylomova
Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change

The present study evaluates semantic shifts in mental health-related concepts in two diachronic corpora spanning 1970-2016, one academic and one general. It evaluates whether their meanings have broadened to encompass less severe phenomena and whether they have become more pathology related. It applies a recently proposed methodology (Baes et al., 2023) to examine whether words collocating with a sample of mental health concepts have become less emotionally intense and develops a new way to examine whether the concepts increasingly co-occur with pathology-related terms. In support of the first hypothesis, mental health-related concepts became associated with less emotionally intense language in the psychology corpus (addiction, anger, stress, worry) and in the general corpus (addiction, grief, stress, worry). In support of the second hypothesis, mental health-related concepts came to be more associated with pathology-related language in psychology (addiction, grief, stress, worry) and in the general corpus (grief, stress). Findings demonstrate that some mental health concepts have become normalized and/or pathologized, a conclusion with important social and cultural implications.

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Predicting Human Translation Difficulty Using Automatic Word Alignment
Zheng Wei Lim | Trevor Cohn | Charles Kemp | Ekaterina Vylomova
Findings of the Association for Computational Linguistics: ACL 2023

Translation difficulty arises when translators are required to resolve translation ambiguity from multiple possible translations. Translation difficulty can be measured by recording the diversity of responses provided by human translators and the time taken to provide these responses, but these behavioral measures are costly and do not scale. In this work, we use word alignments computed over large scale bilingual corpora to develop predictors of lexical translation difficulty. We evaluate our approach using behavioural data from translations provided both in and out of context, and report results that improve on a previous embedding-based approach (Thompson et al., 2020). Our work can therefore contribute to a deeper understanding of cross-lingual differences and of causes of translation difficulty.

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SIGMORPHONUniMorph 2023 Shared Task 0: Typologically Diverse Morphological Inflection
Omer Goldman | Khuyagbaatar Batsuren | Salam Khalifa | Aryaman Arora | Garrett Nicolai | Reut Tsarfaty | Ekaterina Vylomova
Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology

The 2023 SIGMORPHON–UniMorph shared task on typologically diverse morphological inflection included a wide range of languages: 26 languages from 9 primary language families. The data this year was all lemma-split, to allow testing models’ generalization ability, and structured along the new hierarchical schema presented in (Batsuren et al., 2022). The systems submitted this year, 9 in number, showed ingenuity and innovativeness, including hard attention for explainability and bidirectional decoding. Special treatment was also given by many participants to the newly-introduced data in Japanese, due to the high abundance of unseen Kanji characters in its test set.

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Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
Lisa Beinborn | Koustava Goswami | Saliha Muradoğlu | Alexey Sorokin | Ritesh Kumar | Andreas Shcherbakov | Edoardo M. Ponti | Ryan Cotterell | Ekaterina Vylomova
Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

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Does Topological Ordering of Morphological Segments Reduce Morphological Modeling Complexity? A Preliminary Study on 13 Languages
Andreas Shcherbakov | Ekaterina Vylomova
Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

Generalization to novel forms and feature combinations is the key to efficient learning. Recently, Goldman et al. (2022) demonstrated that contemporary neural approaches to morphological inflection still struggle to generalize to unseen words and feature combinations, even in agglutinative languages. In this paper, we argue that the use of morphological segmentation in inflection modeling allows decomposing the problem into sub-problems of substantially smaller search space. We suggest that morphological segments may be globally topologically sorted according to their grammatical categories within a given language. Our experiments demonstrate that such segmentation provides all the necessary information for better generalization, especially in agglutinative languages.

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Proceedings of the Sixth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2023)
Atul Kr. Ojha | Chao-hong Liu | Ekaterina Vylomova | Flammie Pirinen | Jade Abbott | Jonathan Washington | Nathaniel Oco | Valentin Malykh | Varvara Logacheva | Xiaobing Zhao
Proceedings of the Sixth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2023)

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Proceedings of the Second Workshop on NLP Applications to Field Linguistics
Oleg Serikov | Ekaterina Voloshina | Anna Postnikova | Elena Klyachko | Ekaterina Vylomova | Tatiana Shavrina | Eric Le Ferrand | Valentin Malykh | Francis Tyers | Timofey Arkhangelskiy | Vladislav Mikhailov
Proceedings of the Second Workshop on NLP Applications to Field Linguistics

2022

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Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)
Atul Kr. Ojha | Chao-Hong Liu | Ekaterina Vylomova | Jade Abbott | Jonathan Washington | Nathaniel Oco | Tommi A Pirinen | Valentin Malykh | Varvara Logacheva | Xiaobing Zhao
Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)

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Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
Ekaterina Vylomova | Edoardo Ponti | Ryan Cotterell
Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

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The SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes
Johann-Mattis List | Ekaterina Vylomova | Robert Forkel | Nathan Hill | Ryan Cotterell
Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

This study describes the structure and the results of the SIGTYP 2022 shared task on the prediction of cognate reflexes from multilingual wordlists. We asked participants to submit systems that would predict words in individual languages with the help of cognate words from related languages. Training and surprise data were based on standardized multilingual wordlists from several language families. Four teams submitted a total of eight systems, including both neural and non-neural systems, as well as systems adjusted to the task and systems using more general settings. While all systems showed a rather promising performance, reflecting the overwhelming regularity of sound change, the best performance throughout was achieved by a system based on convolutional networks originally designed for image restoration.

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The SIGMORPHON 2022 Shared Task on Morpheme Segmentation
Khuyagbaatar Batsuren | Gábor Bella | Aryaman Arora | Viktor Martinovic | Kyle Gorman | Zdeněk Žabokrtský | Amarsanaa Ganbold | Šárka Dohnalová | Magda Ševčíková | Kateřina Pelegrinová | Fausto Giunchiglia | Ryan Cotterell | Ekaterina Vylomova
Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, derivations, and inflections. Subtask 1, word-level morpheme segmentation, covered 5 million words in 9 languages (Czech, English, Spanish, Hungarian, French, Italian, Russian, Latin, Mongolian) and received 13 system submissions from 7 teams and the best system averaged 97.29% F1 score across all languages, ranging English (93.84%) to Latin (99.38%). Subtask 2, sentence-level morpheme segmentation, covered 18,735 sentences in 3 languages (Czech, English, Mongolian), received 10 system submissions from 3 teams, and the best systems outperformed all three state-of-the-art subword tokenization methods (BPE, ULM, Morfessor2) by 30.71% absolute. To facilitate error analysis and support any type of future studies, we released all system predictions, the evaluation script, and all gold standard datasets.

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SIGMORPHONUniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection
Jordan Kodner | Salam Khalifa | Khuyagbaatar Batsuren | Hossep Dolatian | Ryan Cotterell | Faruk Akkus | Antonios Anastasopoulos | Taras Andrushko | Aryaman Arora | Nona Atanalov | Gábor Bella | Elena Budianskaya | Yustinus Ghanggo Ate | Omer Goldman | David Guriel | Simon Guriel | Silvia Guriel-Agiashvili | Witold Kieraś | Andrew Krizhanovsky | Natalia Krizhanovsky | Igor Marchenko | Magdalena Markowska | Polina Mashkovtseva | Maria Nepomniashchaya | Daria Rodionova | Karina Scheifer | Alexandra Sorova | Anastasia Yemelina | Jeremiah Young | Ekaterina Vylomova
Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

The 2022 SIGMORPHON–UniMorph shared task on large scale morphological inflection generation included a wide range of typologically diverse languages: 33 languages from 11 top-level language families: Arabic (Modern Standard), Assamese, Braj, Chukchi, Eastern Armenian, Evenki, Georgian, Gothic, Gujarati, Hebrew, Hungarian, Itelmen, Karelian, Kazakh, Ket, Khalkha Mongolian, Kholosi, Korean, Lamahalot, Low German, Ludic, Magahi, Middle Low German, Old English, Old High German, Old Norse, Polish, Pomak, Slovak, Turkish, Upper Sorbian, Veps, and Xibe. We emphasize generalization along different dimensions this year by evaluating test items with unseen lemmas and unseen features separately under small and large training conditions. Across the five submitted systems and two baselines, the prediction of inflections with unseen features proved challenging, with average performance decreased substantially from last year. This was true even for languages for which the forms were in principle predictable, which suggests that further work is needed in designing systems that capture the various types of generalization required for the world’s languages.

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Morphology is not just a naive Bayes – UniMelb Submission to SIGMORPHON 2022 ST on Morphological Inflection
Andreas Sherbakov | Ekaterina Vylomova
Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

The paper describes the Flexica team’s submission to the SIGMORPHON 2022 Shared Task 1 Part 1: Typologically Diverse Morphological Inflection. Our team submitted a nonneural system that extracted transformation patterns from alignments between a lemma and inflected forms. For each inflection category, we chose a pattern based on its abstractness score. The system outperformed the non-neural baseline, the extracted patterns covered a substantial part of possible inflections. However, we discovered that such score that does not account for all possible combinations of string segments as well as morphosyntactic features is not sufficient for a certain proportion of inflection cases.

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UniMorph 4.0: Universal Morphology
Khuyagbaatar Batsuren | Omer Goldman | Salam Khalifa | Nizar Habash | Witold Kieraś | Gábor Bella | Brian Leonard | Garrett Nicolai | Kyle Gorman | Yustinus Ghanggo Ate | Maria Ryskina | Sabrina Mielke | Elena Budianskaya | Charbel El-Khaissi | Tiago Pimentel | Michael Gasser | William Abbott Lane | Mohit Raj | Matt Coler | Jaime Rafael Montoya Samame | Delio Siticonatzi Camaiteri | Esaú Zumaeta Rojas | Didier López Francis | Arturo Oncevay | Juan López Bautista | Gema Celeste Silva Villegas | Lucas Torroba Hennigen | Adam Ek | David Guriel | Peter Dirix | Jean-Philippe Bernardy | Andrey Scherbakov | Aziyana Bayyr-ool | Antonios Anastasopoulos | Roberto Zariquiey | Karina Sheifer | Sofya Ganieva | Hilaria Cruz | Ritván Karahóǧa | Stella Markantonatou | George Pavlidis | Matvey Plugaryov | Elena Klyachko | Ali Salehi | Candy Angulo | Jatayu Baxi | Andrew Krizhanovsky | Natalia Krizhanovskaya | Elizabeth Salesky | Clara Vania | Sardana Ivanova | Jennifer White | Rowan Hall Maudslay | Josef Valvoda | Ran Zmigrod | Paula Czarnowska | Irene Nikkarinen | Aelita Salchak | Brijesh Bhatt | Christopher Straughn | Zoey Liu | Jonathan North Washington | Yuval Pinter | Duygu Ataman | Marcin Wolinski | Totok Suhardijanto | Anna Yablonskaya | Niklas Stoehr | Hossep Dolatian | Zahroh Nuriah | Shyam Ratan | Francis M. Tyers | Edoardo M. Ponti | Grant Aiton | Aryaman Arora | Richard J. Hatcher | Ritesh Kumar | Jeremiah Young | Daria Rodionova | Anastasia Yemelina | Taras Andrushko | Igor Marchenko | Polina Mashkovtseva | Alexandra Serova | Emily Prud’hommeaux | Maria Nepomniashchaya | Fausto Giunchiglia | Eleanor Chodroff | Mans Hulden | Miikka Silfverberg | Arya D. McCarthy | David Yarowsky | Ryan Cotterell | Reut Tsarfaty | Ekaterina Vylomova
Proceedings of the Thirteenth Language Resources and Evaluation Conference

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet.

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Proceedings of the first workshop on NLP applications to field linguistics
Oleg Serikov | Ekaterina Voloshina | Anna Postnikova | Elena Klyachko | Ekaterina Neminova | Ekaterina Vylomova | Tatiana Shavrina | Eric Le Ferrand | Valentin Malykh | Francis Tyers | Timofey Arkhangelskiy | Vladislav Mikhailov | Alena Fenogenova
Proceedings of the first workshop on NLP applications to field linguistics

2021

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SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages
Tiago Pimentel | Maria Ryskina | Sabrina J. Mielke | Shijie Wu | Eleanor Chodroff | Brian Leonard | Garrett Nicolai | Yustinus Ghanggo Ate | Salam Khalifa | Nizar Habash | Charbel El-Khaissi | Omer Goldman | Michael Gasser | William Lane | Matt Coler | Arturo Oncevay | Jaime Rafael Montoya Samame | Gema Celeste Silva Villegas | Adam Ek | Jean-Philippe Bernardy | Andrey Shcherbakov | Aziyana Bayyr-ool | Karina Sheifer | Sofya Ganieva | Matvey Plugaryov | Elena Klyachko | Ali Salehi | Andrew Krizhanovsky | Natalia Krizhanovsky | Clara Vania | Sardana Ivanova | Aelita Salchak | Christopher Straughn | Zoey Liu | Jonathan North Washington | Duygu Ataman | Witold Kieraś | Marcin Woliński | Totok Suhardijanto | Niklas Stoehr | Zahroh Nuriah | Shyam Ratan | Francis M. Tyers | Edoardo M. Ponti | Grant Aiton | Richard J. Hatcher | Emily Prud’hommeaux | Ritesh Kumar | Mans Hulden | Botond Barta | Dorina Lakatos | Gábor Szolnok | Judit Ács | Mohit Raj | David Yarowsky | Ryan Cotterell | Ben Ambridge | Ekaterina Vylomova
Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

This year’s iteration of the SIGMORPHON Shared Task on morphological reinflection focuses on typological diversity and cross-lingual variation of morphosyntactic features. In terms of the task, we enrich UniMorph with new data for 32 languages from 13 language families, with most of them being under-resourced: Kunwinjku, Classical Syriac, Arabic (Modern Standard, Egyptian, Gulf), Hebrew, Amharic, Aymara, Magahi, Braj, Kurdish (Central, Northern, Southern), Polish, Karelian, Livvi, Ludic, Veps, Võro, Evenki, Xibe, Tuvan, Sakha, Turkish, Indonesian, Kodi, Seneca, Asháninka, Yanesha, Chukchi, Itelmen, Eibela. We evaluate six systems on the new data and conduct an extensive error analysis of the systems’ predictions. Transformer-based models generally demonstrate superior performance on the majority of languages, achieving >90% accuracy on 65% of them. The languages on which systems yielded low accuracy are mainly under-resourced, with a limited amount of data. Most errors made by the systems are due to allomorphy, honorificity, and form variation. In addition, we observe that systems especially struggle to inflect multiword lemmas. The systems also produce misspelled forms or end up in repetitive loops (e.g., RNN-based models). Finally, we report a large drop in systems’ performance on previously unseen lemmas.

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Proceedings of the Third Workshop on Computational Typology and Multilingual NLP
Ekaterina Vylomova | Elizabeth Salesky | Sabrina Mielke | Gabriella Lapesa | Ritesh Kumar | Harald Hammarström | Ivan Vulić | Anna Korhonen | Roi Reichart | Edoardo Maria Ponti | Ryan Cotterell
Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

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SIGTYP 2021 Shared Task: Robust Spoken Language Identification
Elizabeth Salesky | Badr M. Abdullah | Sabrina Mielke | Elena Klyachko | Oleg Serikov | Edoardo Maria Ponti | Ritesh Kumar | Ryan Cotterell | Ekaterina Vylomova
Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

While language identification is a fundamental speech and language processing task, for many languages and language families it remains a challenging task. For many low-resource and endangered languages this is in part due to resource availability: where larger datasets exist, they may be single-speaker or have different domains than desired application scenarios, demanding a need for domain and speaker-invariant language identification systems. This year’s shared task on robust spoken language identification sought to investigate just this scenario: systems were to be trained on largely single-speaker speech from one domain, but evaluated on data in other domains recorded from speakers under different recording circumstances, mimicking realistic low-resource scenarios. We see that domain and speaker mismatch proves very challenging for current methods which can perform above 95% accuracy in-domain, which domain adaptation can address to some degree, but that these conditions merit further investigation to make spoken language identification accessible in many scenarios.

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Anlirika: An LSTMCNN Flow Twister for Spoken Language Identification
Andreas Scherbakov | Liam Whittle | Ritesh Kumar | Siddharth Singh | Matthew Coleman | Ekaterina Vylomova
Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

The paper presents Anlirika’s submission to SIGTYP 2021 Shared Task on Robust Spoken Language Identification. The task aims at building a robust system that generalizes well across different domains and speakers. The training data is limited to a single domain only with predominantly single speaker per language while the validation and test data samples are derived from diverse dataset and multiple speakers. We experiment with a neural system comprising a combination of dense, convolutional, and recurrent layers that are designed to perform better generalization and obtain speaker-invariant representations. We demonstrate that the task in its constrained form (without making use of external data or augmentation the train set with samples from the validation set) is still challenging. Our best system trained on the data augmented with validation samples achieves 29.9% accuracy on the test data.

2020

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Proceedings of the Second Workshop on Computational Research in Linguistic Typology
Ekaterina Vylomova | Edoardo M. Ponti | Eitan Grossman | Arya D. McCarthy | Yevgeni Berzak | Haim Dubossarsky | Ivan Vulić | Roi Reichart | Anna Korhonen | Ryan Cotterell
Proceedings of the Second Workshop on Computational Research in Linguistic Typology

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SIGTYP 2020 Shared Task: Prediction of Typological Features
Johannes Bjerva | Elizabeth Salesky | Sabrina J. Mielke | Aditi Chaudhary | Giuseppe G. A. Celano | Edoardo Maria Ponti | Ekaterina Vylomova | Ryan Cotterell | Isabelle Augenstein
Proceedings of the Second Workshop on Computational Research in Linguistic Typology

Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world’s languages. They have been shown to be useful for downstream applications, including cross-lingual transfer learning and linguistic probing. A major drawback hampering broader adoption of typological KBs is that they are sparsely populated, in the sense that most languages only have annotations for some features, and skewed, in that few features have wide coverage. As typological features often correlate with one another, it is possible to predict them and thus automatically populate typological KBs, which is also the focus of this shared task. Overall, the task attracted 8 submissions from 5 teams, out of which the most successful methods make use of such feature correlations. However, our error analysis reveals that even the strongest submitted systems struggle with predicting feature values for languages where few features are known.

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SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
Ekaterina Vylomova | Jennifer White | Elizabeth Salesky | Sabrina J. Mielke | Shijie Wu | Edoardo Maria Ponti | Rowan Hall Maudslay | Ran Zmigrod | Josef Valvoda | Svetlana Toldova | Francis Tyers | Elena Klyachko | Ilya Yegorov | Natalia Krizhanovsky | Paula Czarnowska | Irene Nikkarinen | Andrew Krizhanovsky | Tiago Pimentel | Lucas Torroba Hennigen | Christo Kirov | Garrett Nicolai | Adina Williams | Antonios Anastasopoulos | Hilaria Cruz | Eleanor Chodroff | Ryan Cotterell | Miikka Silfverberg | Mans Hulden
Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from just one language such as English. The SIGMORPHON 2020 shared task on morphological reinflection aims to investigate systems’ ability to generalize across typologically distinct languages, many of which are low resource. Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages. A total of 22 systems (19 neural) from 10 teams were submitted to the task. All four winning systems were neural (two monolingual transformers and two massively multilingual RNN-based models with gated attention). Most teams demonstrate utility of data hallucination and augmentation, ensembles, and multilingual training for low-resource languages. Non-neural learners and manually designed grammars showed competitive and even superior performance on some languages (such as Ingrian, Tajik, Tagalog, Zarma, Lingala), especially with very limited data. Some language families (Afro-Asiatic, Niger-Congo, Turkic) were relatively easy for most systems and achieved over 90% mean accuracy while others were more challenging.

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UniMorph 3.0: Universal Morphology
Arya D. McCarthy | Christo Kirov | Matteo Grella | Amrit Nidhi | Patrick Xia | Kyle Gorman | Ekaterina Vylomova | Sabrina J. Mielke | Garrett Nicolai | Miikka Silfverberg | Timofey Arkhangelskiy | Nataly Krizhanovsky | Andrew Krizhanovsky | Elena Klyachko | Alexey Sorokin | John Mansfield | Valts Ernštreits | Yuval Pinter | Cassandra L. Jacobs | Ryan Cotterell | Mans Hulden | David Yarowsky
Proceedings of the Twelfth Language Resources and Evaluation Conference

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological paradigms for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. We have implemented several improvements to the extraction pipeline which creates most of our data, so that it is both more complete and more correct. We have added 66 new languages, as well as new parts of speech for 12 languages. We have also amended the schema in several ways. Finally, we present three new community tools: two to validate data for resource creators, and one to make morphological data available from the command line. UniMorph is based at the Center for Language and Speech Processing (CLSP) at Johns Hopkins University in Baltimore, Maryland. This paper details advances made to the schema, tooling, and dissemination of project resources since the UniMorph 2.0 release described at LREC 2018.

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Modelling Verbal Morphology in Nen
Saliha Muradoglu | Nicholas Evans | Ekaterina Vylomova
Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association

Nen verbal morphology is particularly complex; a transitive verb can take up to 1,740 unique forms. The combined effect of having a large combinatoric space and a low-resource setting amplifies the need for NLP tools. Nen morphology utilises distributed exponence - a non-trivial means of mapping form to meaning. In this paper, we attempt to model Nen verbal morphology using state-of-the-art machine learning models for morphological reinflection. We explore and categorise the types of errors these systems generate. Our results show sensitivity to training data composition; different distributions of verb type yield different accuracies (patterning with E-complexity). We also demonstrate the types of patterns that can be inferred from the training data, through the case study of sycretism.

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Exploring Looping Effects in RNN-based Architectures
Andrei Shcherbakov | Saliha Muradoglu | Ekaterina Vylomova
Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association

The paper investigates repetitive loops, a common problem in contemporary text generation (such as machine translation, language modelling, morphological inflection) systems. More specifically, we conduct a study on neural models with recurrent units by explicitly altering their decoder internal state. We use a task of morphological reinflection task as a proxy to study the effects of the changes. Our results show that the probability of the occurrence of repetitive loops is significantly reduced by introduction of an extra neural decoder output. The output should be specifically trained to produce gradually increasing value upon generation of each character of a given sequence. We also explored variations of the technique and found that feeding the extra output back to the decoder amplifies the positive effects.

2019

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Weird Inflects but OK: Making Sense of Morphological Generation Errors
Kyle Gorman | Arya D. McCarthy | Ryan Cotterell | Ekaterina Vylomova | Miikka Silfverberg | Magdalena Markowska
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

We conduct a manual error analysis of the CoNLL-SIGMORPHON Shared Task on Morphological Reinflection. This task involves natural language generation: systems are given a word in citation form (e.g., hug) and asked to produce the corresponding inflected form (e.g., the simple past hugged). We propose an error taxonomy and use it to annotate errors made by the top two systems across twelve languages. Many of the observed errors are related to inflectional patterns sensitive to inherent linguistic properties such as animacy or affect; many others are failures to predict truly unpredictable inflectional behaviors. We also find nearly one quarter of the residual “errors” reflect errors in the gold data.

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Modelling Tibetan Verbal Morphology
Qianji Di | Ekaterina Vylomova | Tim Baldwin
Proceedings of the 17th Annual Workshop of the Australasian Language Technology Association

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A string-to-graph constructive alignment algorithm for discrete and probabilistic language modeling
Andrey Shcherbakov | Ekaterina Vylomova
Proceedings of the 17th Annual Workshop of the Australasian Language Technology Association

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Contextualization of Morphological Inflection
Ekaterina Vylomova | Ryan Cotterell | Trevor Cohn | Timothy Baldwin | Jason Eisner
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Critical to natural language generation is the production of correctly inflected text. In this paper, we isolate the task of predicting a fully inflected sentence from its partially lemmatized version. Unlike traditional morphological inflection or surface realization, our task input does not provide “gold” tags that specify what morphological features to realize on each lemmatized word; rather, such features must be inferred from sentential context. We develop a neural hybrid graphical model that explicitly reconstructs morphological features before predicting the inflected forms, and compare this to a system that directly predicts the inflected forms without relying on any morphological annotation. We experiment on several typologically diverse languages from the Universal Dependencies treebanks, showing the utility of incorporating linguistically-motivated latent variables into NLP models.

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The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection
Arya D. McCarthy | Ekaterina Vylomova | Shijie Wu | Chaitanya Malaviya | Lawrence Wolf-Sonkin | Garrett Nicolai | Christo Kirov | Miikka Silfverberg | Sabrina J. Mielke | Jeffrey Heinz | Ryan Cotterell | Mans Hulden
Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology

The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in morphology examined transfer learning of inflection between 100 language pairs, as well as contextual lemmatization and morphosyntactic description in 66 languages. The first task evolves past years’ inflection tasks by examining transfer of morphological inflection knowledge from a high-resource language to a low-resource language. This year also presents a new second challenge on lemmatization and morphological feature analysis in context. All submissions featured a neural component and built on either this year’s strong baselines or highly ranked systems from previous years’ shared tasks. Every participating team improved in accuracy over the baselines for the inflection task (though not Levenshtein distance), and every team in the contextual analysis task improved on both state-of-the-art neural and non-neural baselines.

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Evaluation of Semantic Change of Harm-Related Concepts in Psychology
Ekaterina Vylomova | Sean Murphy | Nicholas Haslam
Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change

The paper focuses on diachronic evaluation of semantic changes of harm-related concepts in psychology. More specifically, we investigate a hypothesis that certain concepts such as “addiction”, “bullying”, “harassment”, “prejudice”, and “trauma” became broader during the last four decades. We evaluate semantic changes using two models: an LSA-based model from Sagi et al. (2009) and a diachronic adaptation of word2vec from Hamilton et al. (2016), that are trained on a large corpus of journal abstracts covering the period of 1980– 2019. Several concepts showed evidence of broadening. “Addiction” moved from physiological dependency on a substance to include psychological dependency on gaming and the Internet. Similarly, “harassment” and “trauma” shifted towards more psychological meanings. On the other hand, “bullying” has transformed into a more victim-related concept and expanded to new areas such as workplaces.

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Proceedings of TyP-NLP: The First Workshop on Typology for Polyglot NLP
Haim Dubossarsky | Arya D. McCarthy | Edoardo Maria Ponti | Ivan Vulić | Ekaterina Vylomova | Yevgeni Berzak | Ryan Cotterell | Manaal Faruqui | Anna Korhonen | Roi Reichart
Proceedings of TyP-NLP: The First Workshop on Typology for Polyglot NLP

2018

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The CoNLLSIGMORPHON 2018 Shared Task: Universal Morphological Reinflection
Ryan Cotterell | Christo Kirov | John Sylak-Glassman | Géraldine Walther | Ekaterina Vylomova | Arya D. McCarthy | Katharina Kann | Sabrina J. Mielke | Garrett Nicolai | Miikka Silfverberg | David Yarowsky | Jason Eisner | Mans Hulden
Proceedings of the CoNLL–SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection

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UniMorph 2.0: Universal Morphology
Christo Kirov | Ryan Cotterell | John Sylak-Glassman | Géraldine Walther | Ekaterina Vylomova | Patrick Xia | Manaal Faruqui | Sabrina J. Mielke | Arya McCarthy | Sandra Kübler | David Yarowsky | Jason Eisner | Mans Hulden
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages
Ryan Cotterell | Christo Kirov | John Sylak-Glassman | Géraldine Walther | Ekaterina Vylomova | Patrick Xia | Manaal Faruqui | Sandra Kübler | David Yarowsky | Jason Eisner | Mans Hulden
Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection

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Paradigm Completion for Derivational Morphology
Ryan Cotterell | Ekaterina Vylomova | Huda Khayrallah | Christo Kirov | David Yarowsky
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models adapted from the inflection task are able to learn the range of derivation patterns, and outperform a non-neural baseline by 16.4%. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems.

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Context-Aware Prediction of Derivational Word-forms
Ekaterina Vylomova | Ryan Cotterell | Timothy Baldwin | Trevor Cohn
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Derivational morphology is a fundamental and complex characteristic of language. In this paper we propose a new task of predicting the derivational form of a given base-form lemma that is appropriate for a given context. We present an encoder-decoder style neural network to produce a derived form character-by-character, based on its corresponding character-level representation of the base form and the context. We demonstrate that our model is able to generate valid context-sensitive derivations from known base forms, but is less accurate under lexicon agnostic setting.

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Word Representation Models for Morphologically Rich Languages in Neural Machine Translation
Ekaterina Vylomova | Trevor Cohn | Xuanli He | Gholamreza Haffari
Proceedings of the First Workshop on Subword and Character Level Models in NLP

Out-of-vocabulary words present a great challenge for Machine Translation. Recently various character-level compositional models were proposed to address this issue. In current research we incorporate two most popular neural architectures, namely LSTM and CNN, into hard- and soft-attentional models of translation for character-level representation of the source. We propose semantic and morphological intrinsic evaluation of encoder-level representations. Our analysis of the learned representations reveals that character-based LSTM seems to be better at capturing morphological aspects compared to character-based CNN. We also show that hard-attentional model provides better character-level representations compared to vanilla one.

2016

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VectorWeavers at SemEval-2016 Task 10: From Incremental Meaning to Semantic Unit (phrase by phrase)
Andreas Scherbakov | Ekaterina Vylomova | Fei Liu | Timothy Baldwin
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Phonotactic Modeling of Extremely Low Resource Languages
Andrei Shcherbakov | Ekaterina Vylomova | Nick Thieberger
Proceedings of the Australasian Language Technology Association Workshop 2016

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Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning
Ekaterina Vylomova | Laura Rimell | Trevor Cohn | Timothy Baldwin
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2014

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Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions
Jing Peng | Anna Feldman | Ekaterina Vylomova
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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