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Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas


Mager, Manuel; Oncevay, Arturo; Ebrahimi, Abteen; Ortega, John; Rios, Annette; Fan, Angela; Gutierrez-Vasques, Ximena; Chiruzzo, Luis; Giménez-Lugo, Gustavo; Ramos, Ricardo; Meza Ruiz, Ivan Vladimir; Coto-Solano, Rolando; Palmer, Alexis; Mager-Hois, Elisabeth; Chaudhary, Vishrav; Neubig, Graham; Vu, Ngoc Thang; Kann, Katharina (2021). Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas. In: Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas, Online, 11 June 2021. Association for Computational Linguistics, 202-217.

Abstract

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.

Abstract

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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Item Type:Conference or Workshop Item (Other), further contribution
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Language:English
Event End Date:11 June 2021
Deposited On:25 May 2021 14:49
Last Modified:25 Oct 2021 16:18
Publisher:Association for Computational Linguistics
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:https://www.aclweb.org/anthology/2021.americasnlp-1.23

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