Machine Translation Through Cultural Texts: Can Verses and Prose Help Low-Resource Indigenous Models?

Antoine Cadotte, Nathalie André, Fatiha Sadat

Abstract
We propose the first MT models for Innu-Aimun, an Indigenous language in Eastern Canada, in an effort to provide assistance tools for translation and language learning. This project is carried out in collaboration with an Innu community school and involves the participation of students in Innu-Aimun translation, within the framework of a meaningful consideration of Indigenous perspectives.Our contributions in this paper result from the three initial stages of this project. First, we aim to align bilingual Innu-Aimun/French texts with collaboration and participation of Innu-Aimun locutors. Second, we present the training and evaluation results of the MT models (both statistical and neural) based on these aligned corpora. And third, we collaboratively analyze some of the translations resulting from the MT models.We also see these developments for Innu-Aimun as a useful case study for answering a larger question: in a context where few aligned bilingual sentences are available for an Indigenous language, can cultural texts such as literature and poetry be used in the development of MT models?
Anthology ID:
2024.loresmt-1.12
Volume:
Proceedings of the Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Atul Kr. Ojha, Chao-hong Liu, Ekaterina Vylomova, Flammie Pirinen, Jade Abbott, Jonathan Washington, Nathaniel Oco, Valentin Malykh, Varvara Logacheva, Xiaobing Zhao
Venues:
LoResMT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
121–127
Language:
URL:
https://aclanthology.org/2024.loresmt-1.12
DOI:
10.18653/v1/2024.loresmt-1.12
Bibkey:
Cite (ACL):
Antoine Cadotte, Nathalie André, and Fatiha Sadat. 2024. Machine Translation Through Cultural Texts: Can Verses and Prose Help Low-Resource Indigenous Models?. In Proceedings of the Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024), pages 121–127, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Machine Translation Through Cultural Texts: Can Verses and Prose Help Low-Resource Indigenous Models? (Cadotte et al., LoResMT-WS 2024)
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