Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

Manuel Mager, Arturo Oncevay, Annette Rios, Ivan Vladimir Meza Ruiz, Alexis Palmer, Graham Neubig, Katharina Kann (Editors)

Anthology ID:
AmericasNLP | NAACL
Association for Computational Linguistics
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Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas
Manuel Mager | Arturo Oncevay | Annette Rios | Ivan Vladimir Meza Ruiz | Alexis Palmer | Graham Neubig | Katharina Kann

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Investigating variation in written forms of Nahuatl using character-based language modelsNahuatl using character-based language models
Robert Pugh | Francis Tyers

We describe experiments with character-based language modeling for written variants of Nahuatl. Using a standard LSTM model and publicly available Bible translations, we explore how character language models can be applied to the tasks of estimating mutual intelligibility, identifying genetic similarity, and distinguishing written variants. We demonstrate that these simple language models are able to capture similarities and differences that have been described in the linguistic literature.

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Morphological Segmentation for SenecaSeneca
Zoey Liu | Robert Jimerson | Emily Prud’hommeaux

This study takes up the task of low-resource morphological segmentation for Seneca, a critically endangered and morphologically complex Native American language primarily spoken in what is now New York State and Ontario. The labeled data in our experiments comes from two sources : one digitized from a publicly available grammar book and the other collected from informal sources. We treat these two sources as distinct domains and investigate different evaluation designs for model selection. The first design abides by standard practices and evaluate models with the in-domain development set, while the second one carries out evaluation using a development domain, or the out-of-domain development set. Across a series of monolingual and crosslinguistic training settings, our results demonstrate the utility of neural encoder-decoder architecture when coupled with multi-task learning.

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Representation of Yine [ Arawak ] Morphology by Finite State Transducer FormalismYine [Arawak] Morphology by Finite State Transducer Formalism
Adriano Ingunza Torres | John Miller | Arturo Oncevay | Roberto Zariquiey Biondi

We represent the complexity of Yine (Arawak) morphology with a finite state transducer (FST) based morphological analyzer. Yine is a low-resource indigenous polysynthetic Peruvian language spoken by approximately 3,000 people and is classified as ‘definitely endangered’ by UNESCO. We review Yine morphology focusing on morphophonology, possessive constructions and verbal predicates. Then we develop FSTs to model these components proposing techniques to solve challenging problems such as complex patterns of incorporating open and closed category arguments. This is a work in progress and we still have more to do in the development and verification of our analyzer. Our analyzer will serve both as a tool to better document the Yine language and as a component of natural language processing (NLP) applications such as spell checking and correction.

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Expanding Universal Dependencies for Polysynthetic Languages : A Case of St. Lawrence Island YupikUniversal Dependencies for Polysynthetic Languages: A Case of St. Lawrence Island Yupik
Hyunji Hayley Park | Lane Schwartz | Francis Tyers

This paper describes the development of the first Universal Dependencies (UD) treebank for St. Lawrence Island Yupik, an endangered language spoken in the Bering Strait region. While the UD guidelines provided a general framework for our annotations, language-specific decisions were made necessary by the rich morphology of the polysynthetic language. Most notably, we annotated a corpus at the morpheme level as well as the word level. The morpheme level annotation was conducted using an existing morphological analyzer and manual disambiguation. By comparing the two resulting annotation schemes, we argue that morpheme-level annotation is essential for polysynthetic languages like St. Lawrence Island Yupik. Word-level annotation results in degenerate trees for some Yupik sentences and often fails to capture syntactic relations that can be manifested at the morpheme level. Dependency parsing experiments provide further support for morpheme-level annotation. Implications for UD annotation of other polysynthetic languages are discussed.

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Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the AmericasAmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas
Manuel Mager | Arturo Oncevay | Abteen Ebrahimi | John Ortega | Annette Rios | Angela Fan | Ximena Gutierrez-Vasques | Luis Chiruzzo | Gustavo Giménez-Lugo | Ricardo Ramos | Ivan Vladimir Meza Ruiz | Rolando Coto-Solano | Alexis Palmer | Elisabeth Mager-Hois | Vishrav Chaudhary | Graham Neubig | Ngoc Thang Vu | Katharina Kann

This paper presents the results of the 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas. The shared task featured two independent tracks, and participants submitted machine translation systems for up to 10 indigenous languages. Overall, 8 teams participated with a total of 214 submissions. We provided training sets consisting of data collected from various sources, as well as manually translated sentences for the development and test sets. An official baseline trained on this data was also provided. Team submissions featured a variety of architectures, including both statistical and neural models, and for the majority of languages, many teams were able to considerably improve over the baseline. The best performing systems achieved 12.97 ChrF higher than baseline, when averaged across languages.

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The REPU CS’ SpanishQuechua Submission to the AmericasNLP 2021 Shared Task on Open Machine TranslationREPU CSSpanish–Quechua Submission to the AmericasNLP 2021 Shared Task on Open Machine Translation
Oscar Moreno

We present the submission of REPUcs to the AmericasNLP machine translation shared task for the low resource language pair SpanishQuechua. Our neural machine translation system ranked first in Track two (development set not used for training) and third in Track one (training includes development data). Our contribution is focused on : (i) the collection of new parallel data from different web sources (poems, lyrics, lexicons, handbooks), and (ii) using large SpanishEnglish data for pre-training and then fine-tuning the SpanishQuechua system. This paper describes the new parallel corpora and our approach in detail.

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The Helsinki submission to the AmericasNLP shared taskHelsinki submission to the AmericasNLP shared task
Raúl Vázquez | Yves Scherrer | Sami Virpioja | Jörg Tiedemann

The University of Helsinki participated in the AmericasNLP shared task for all ten language pairs. Our multilingual NMT models reached the first rank on all language pairs in track 1, and first rank on nine out of ten language pairs in track 2. We focused our efforts on three aspects : (1) the collection of additional data from various sources such as Bibles and political constitutions, (2) the cleaning and filtering of training data with the OpusFilter toolkit, and (3) different multilingual training techniques enabled by the latest version of the OpenNMT-py toolkit to make the most efficient use of the scarce data. This paper describes our efforts in detail.