@inproceedings{ebrahimi-etal-2023-findings,
title = "Findings of the {A}mericas{NLP} 2023 Shared Task on Machine Translation into Indigenous Languages",
author = "Ebrahimi, Abteen and
Mager, Manuel and
Rijhwani, Shruti and
Rice, Enora and
Oncevay, Arturo and
Baltazar, Claudia and
Cort{\'e}s, Mar{\'\i}a and
Monta{\~n}o, Cynthia and
Ortega, John E. and
Coto-solano, Rolando and
Cruz, Hilaria and
Palmer, Alexis and
Kann, Katharina",
editor = "Mager, Manuel and
Ebrahimi, Abteen and
Oncevay, Arturo and
Rice, Enora and
Rijhwani, Shruti and
Palmer, Alexis and
Kann, Katharina",
booktitle = "Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.americasnlp-1.23",
doi = "10.18653/v1/2023.americasnlp-1.23",
pages = "206--219",
abstract = "In this work, we present the results of the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages of the Americas. This edition of the shared task featured eleven language pairs, one of which {--} Chatino-Spanish {--} uses a newly collected evaluation dataset, consisting of professionally translated text from the legal domain. Seven teams participated in the shared task, with a total of 181 submissions. Additionally, we conduct a human evaluation of the best system outputs, and compare them to the best submissions from the prior shared task. We find that this analysis agrees with the quantitative measures used to rank submissions, which shows further improvements of 9.64 ChrF on average across all languages, when compared to the prior winning system.",
}
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<abstract>In this work, we present the results of the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages of the Americas. This edition of the shared task featured eleven language pairs, one of which – Chatino-Spanish – uses a newly collected evaluation dataset, consisting of professionally translated text from the legal domain. Seven teams participated in the shared task, with a total of 181 submissions. Additionally, we conduct a human evaluation of the best system outputs, and compare them to the best submissions from the prior shared task. We find that this analysis agrees with the quantitative measures used to rank submissions, which shows further improvements of 9.64 ChrF on average across all languages, when compared to the prior winning system.</abstract>
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%0 Conference Proceedings
%T Findings of the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages
%A Ebrahimi, Abteen
%A Mager, Manuel
%A Rijhwani, Shruti
%A Rice, Enora
%A Oncevay, Arturo
%A Baltazar, Claudia
%A Cortés, María
%A Montaño, Cynthia
%A Ortega, John E.
%A Coto-solano, Rolando
%A Cruz, Hilaria
%A Palmer, Alexis
%A Kann, Katharina
%Y Mager, Manuel
%Y Ebrahimi, Abteen
%Y Oncevay, Arturo
%Y Rice, Enora
%Y Rijhwani, Shruti
%Y Palmer, Alexis
%Y Kann, Katharina
%S Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F ebrahimi-etal-2023-findings
%X In this work, we present the results of the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages of the Americas. This edition of the shared task featured eleven language pairs, one of which – Chatino-Spanish – uses a newly collected evaluation dataset, consisting of professionally translated text from the legal domain. Seven teams participated in the shared task, with a total of 181 submissions. Additionally, we conduct a human evaluation of the best system outputs, and compare them to the best submissions from the prior shared task. We find that this analysis agrees with the quantitative measures used to rank submissions, which shows further improvements of 9.64 ChrF on average across all languages, when compared to the prior winning system.
%R 10.18653/v1/2023.americasnlp-1.23
%U https://aclanthology.org/2023.americasnlp-1.23
%U https://doi.org/10.18653/v1/2023.americasnlp-1.23
%P 206-219
Markdown (Informal)
[Findings of the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages](https://aclanthology.org/2023.americasnlp-1.23) (Ebrahimi et al., AmericasNLP 2023)
ACL
- Abteen Ebrahimi, Manuel Mager, Shruti Rijhwani, Enora Rice, Arturo Oncevay, Claudia Baltazar, María Cortés, Cynthia Montaño, John E. Ortega, Rolando Coto-solano, Hilaria Cruz, Alexis Palmer, and Katharina Kann. 2023. Findings of the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages. In Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP), pages 206–219, Toronto, Canada. Association for Computational Linguistics.