Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction
Manuel Mager, Rajat Bhatnagar, Graham Neubig, Ngoc Thang Vu, Katharina Kann
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.- Anthology ID:
- 2023.americasnlp-1.13
- Volume:
- Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- AmericasNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 109–133
- Language:
- URL:
- https://aclanthology.org/2023.americasnlp-1.13
- DOI:
- 10.18653/v1/2023.americasnlp-1.13
- Cite (ACL):
- Manuel Mager, Rajat Bhatnagar, Graham Neubig, Ngoc Thang Vu, and Katharina Kann. 2023. Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction. In Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP), pages 109–133, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction (Mager et al., AmericasNLP 2023)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2023.americasnlp-1.13.pdf