@inproceedings{ebrahimi-etal-2024-findings,
title = "Findings of the {A}mericas{NLP} 2024 Shared Task on Machine Translation into Indigenous Languages",
author = "Ebrahimi, Abteen and
de Gibert, Ona and
Vazquez, Raul and
Coto-Solano, Rolando and
Denisov, Pavel and
Pugh, Robert and
Mager, Manuel and
Oncevay, Arturo and
Chiruzzo, Luis and
von der Wense, Katharina and
Rijhwani, Shruti",
editor = "Mager, Manuel and
Ebrahimi, Abteen and
Rijhwani, Shruti and
Oncevay, Arturo and
Chiruzzo, Luis and
Pugh, Robert and
von der Wense, Katharina",
booktitle = "Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.americasnlp-1.28",
doi = "10.18653/v1/2024.americasnlp-1.28",
pages = "236--246",
abstract = "This paper presents the findings of the third iteration of the AmericasNLP Shared Task on Machine Translation. This year{'}s competition features eleven Indigenous languages found across North, Central, and South America. A total of six teams participate with a total of 157 submissions across all languages and models. Two baselines {--} the Sheffield and Helsinki systems from 2023 {--} are provided and represent hard-to-beat starting points for the competition. In addition to the baselines, teams are given access to a new repository of training data which consists of data collected by teams in prior shared tasks. Using ChrF++ as the main competition metric, we see improvements over the baseline for 4 languages: Chatino, Guarani, Quechua, and Rar{\'a}muri, with performance increases over the best baseline of 4.2 ChrF++. In this work, we present a summary of the submitted systems, results, and a human evaluation of system outputs for Bribri, which consists of both (1) a rating of meaning and fluency and (2) a qualitative error analysis of outputs from the best submitted system.",
}
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<abstract>This paper presents the findings of the third iteration of the AmericasNLP Shared Task on Machine Translation. This year’s competition features eleven Indigenous languages found across North, Central, and South America. A total of six teams participate with a total of 157 submissions across all languages and models. Two baselines – the Sheffield and Helsinki systems from 2023 – are provided and represent hard-to-beat starting points for the competition. In addition to the baselines, teams are given access to a new repository of training data which consists of data collected by teams in prior shared tasks. Using ChrF++ as the main competition metric, we see improvements over the baseline for 4 languages: Chatino, Guarani, Quechua, and Rarámuri, with performance increases over the best baseline of 4.2 ChrF++. In this work, we present a summary of the submitted systems, results, and a human evaluation of system outputs for Bribri, which consists of both (1) a rating of meaning and fluency and (2) a qualitative error analysis of outputs from the best submitted system.</abstract>
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%0 Conference Proceedings
%T Findings of the AmericasNLP 2024 Shared Task on Machine Translation into Indigenous Languages
%A Ebrahimi, Abteen
%A de Gibert, Ona
%A Vazquez, Raul
%A Coto-Solano, Rolando
%A Denisov, Pavel
%A Pugh, Robert
%A Mager, Manuel
%A Oncevay, Arturo
%A Chiruzzo, Luis
%A von der Wense, Katharina
%A Rijhwani, Shruti
%Y Mager, Manuel
%Y Ebrahimi, Abteen
%Y Rijhwani, Shruti
%Y Oncevay, Arturo
%Y Chiruzzo, Luis
%Y Pugh, Robert
%Y von der Wense, Katharina
%S Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F ebrahimi-etal-2024-findings
%X This paper presents the findings of the third iteration of the AmericasNLP Shared Task on Machine Translation. This year’s competition features eleven Indigenous languages found across North, Central, and South America. A total of six teams participate with a total of 157 submissions across all languages and models. Two baselines – the Sheffield and Helsinki systems from 2023 – are provided and represent hard-to-beat starting points for the competition. In addition to the baselines, teams are given access to a new repository of training data which consists of data collected by teams in prior shared tasks. Using ChrF++ as the main competition metric, we see improvements over the baseline for 4 languages: Chatino, Guarani, Quechua, and Rarámuri, with performance increases over the best baseline of 4.2 ChrF++. In this work, we present a summary of the submitted systems, results, and a human evaluation of system outputs for Bribri, which consists of both (1) a rating of meaning and fluency and (2) a qualitative error analysis of outputs from the best submitted system.
%R 10.18653/v1/2024.americasnlp-1.28
%U https://aclanthology.org/2024.americasnlp-1.28
%U https://doi.org/10.18653/v1/2024.americasnlp-1.28
%P 236-246
Markdown (Informal)
[Findings of the AmericasNLP 2024 Shared Task on Machine Translation into Indigenous Languages](https://aclanthology.org/2024.americasnlp-1.28) (Ebrahimi et al., AmericasNLP-WS 2024)
ACL
- Abteen Ebrahimi, Ona de Gibert, Raul Vazquez, Rolando Coto-Solano, Pavel Denisov, Robert Pugh, Manuel Mager, Arturo Oncevay, Luis Chiruzzo, Katharina von der Wense, and Shruti Rijhwani. 2024. Findings of the AmericasNLP 2024 Shared Task on Machine Translation into Indigenous Languages. In Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024), pages 236–246, Mexico City, Mexico. Association for Computational Linguistics.