@inproceedings{zheng-etal-2021-low,
title = "Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining",
author = "Zheng, Francis and
Reid, Machel and
Marrese-Taylor, Edison and
Matsuo, Yutaka",
editor = "Mager, Manuel and
Oncevay, Arturo and
Rios, Annette and
Ruiz, Ivan Vladimir Meza and
Palmer, Alexis and
Neubig, Graham and
Kann, Katharina",
booktitle = "Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.americasnlp-1.26",
doi = "10.18653/v1/2021.americasnlp-1.26",
pages = "234--240",
abstract = {This paper describes UTokyo{'}s submission to the AmericasNLP 2021 Shared Task on machine translation systems for indigenous languages of the Americas. We present a low-resource machine translation system that improves translation accuracy using cross-lingual language model pretraining. Our system uses an mBART implementation of fairseq to pretrain on a large set of monolingual data from a diverse set of high-resource languages before finetuning on 10 low-resource indigenous American languages: Aymara, Bribri, Ash{\'a}ninka, Guaran{\'\i}, Wixarika, N{\'a}huatl, H{\~n}{\"a}h{\~n}u, Quechua, Shipibo-Konibo, and Rar{\'a}muri. On average, our system achieved BLEU scores that were 1.64 higher and chrF scores that were 0.0749 higher than the baseline.},
}
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<abstract>This paper describes UTokyo’s submission to the AmericasNLP 2021 Shared Task on machine translation systems for indigenous languages of the Americas. We present a low-resource machine translation system that improves translation accuracy using cross-lingual language model pretraining. Our system uses an mBART implementation of fairseq to pretrain on a large set of monolingual data from a diverse set of high-resource languages before finetuning on 10 low-resource indigenous American languages: Aymara, Bribri, Asháninka, Guaraní, Wixarika, Náhuatl, Hñähñu, Quechua, Shipibo-Konibo, and Rarámuri. On average, our system achieved BLEU scores that were 1.64 higher and chrF scores that were 0.0749 higher than the baseline.</abstract>
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%0 Conference Proceedings
%T Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining
%A Zheng, Francis
%A Reid, Machel
%A Marrese-Taylor, Edison
%A Matsuo, Yutaka
%Y Mager, Manuel
%Y Oncevay, Arturo
%Y Rios, Annette
%Y Ruiz, Ivan Vladimir Meza
%Y Palmer, Alexis
%Y Neubig, Graham
%Y Kann, Katharina
%S Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F zheng-etal-2021-low
%X This paper describes UTokyo’s submission to the AmericasNLP 2021 Shared Task on machine translation systems for indigenous languages of the Americas. We present a low-resource machine translation system that improves translation accuracy using cross-lingual language model pretraining. Our system uses an mBART implementation of fairseq to pretrain on a large set of monolingual data from a diverse set of high-resource languages before finetuning on 10 low-resource indigenous American languages: Aymara, Bribri, Asháninka, Guaraní, Wixarika, Náhuatl, Hñähñu, Quechua, Shipibo-Konibo, and Rarámuri. On average, our system achieved BLEU scores that were 1.64 higher and chrF scores that were 0.0749 higher than the baseline.
%R 10.18653/v1/2021.americasnlp-1.26
%U https://aclanthology.org/2021.americasnlp-1.26
%U https://doi.org/10.18653/v1/2021.americasnlp-1.26
%P 234-240
Markdown (Informal)
[Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining](https://aclanthology.org/2021.americasnlp-1.26) (Zheng et al., AmericasNLP 2021)
ACL