@inproceedings{mager-etal-2023-neural,
title = "Neural Machine Translation for the Indigenous Languages of the {A}mericas: An Introduction",
author = "Mager, Manuel and
Bhatnagar, Rajat and
Neubig, Graham and
Vu, Ngoc Thang 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-1.13",
doi = "10.18653/v1/2023.americasnlp-1.13",
pages = "109--133",
abstract = "Neural models have drastically advanced state of the art for machine translation (MT) between high-resource languages. Traditionally, these models rely on large amounts of training data, but many language pairs lack these resources. However, an important part of the languages in the world do not have this amount of data. Most languages from the Americas are among them, having a limited amount of parallel and monolingual data, if any. Here, we present an introduction to the interested reader to the basic challenges, concepts, and techniques that involve the creation of MT systems for these languages. Finally, we discuss the recent advances and findings and open questions, product of an increased interest of the NLP community in these languages.",
}
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%0 Conference Proceedings
%T Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction
%A Mager, Manuel
%A Bhatnagar, Rajat
%A Neubig, Graham
%A Vu, Ngoc Thang
%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 mager-etal-2023-neural
%X Neural models have drastically advanced state of the art for machine translation (MT) between high-resource languages. Traditionally, these models rely on large amounts of training data, but many language pairs lack these resources. However, an important part of the languages in the world do not have this amount of data. Most languages from the Americas are among them, having a limited amount of parallel and monolingual data, if any. Here, we present an introduction to the interested reader to the basic challenges, concepts, and techniques that involve the creation of MT systems for these languages. Finally, we discuss the recent advances and findings and open questions, product of an increased interest of the NLP community in these languages.
%R 10.18653/v1/2023.americasnlp-1.13
%U https://aclanthology.org/2023.americasnlp-1.13
%U https://doi.org/10.18653/v1/2023.americasnlp-1.13
%P 109-133
Markdown (Informal)
[Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction](https://aclanthology.org/2023.americasnlp-1.13) (Mager et al., AmericasNLP 2023)
ACL