Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara

Allahsera Auguste Tapo, Bakary Coulibaly, Sébastien Diarra, Christopher Homan, Julia Kreutzer, Sarah Luger, Arthur Nagashima, Marcos Zampieri, Michael Leventhal


Abstract
Low-resource languages present unique challenges to (neural) machine translation. We discuss the case of Bambara, a Mande language for which training data is scarce and requires significant amounts of pre-processing. More than the linguistic situation of Bambara itself, the socio-cultural context within which Bambara speakers live poses challenges for automated processing of this language. In this paper, we present the first parallel data set for machine translation of Bambara into and from English and French and the first benchmark results on machine translation to and from Bambara. We discuss challenges in working with low-resource languages and propose strategies to cope with data scarcity in low-resource machine translation (MT).
Anthology ID:
2020.loresmt-1.3
Volume:
Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages
Month:
December
Year:
2020
Address:
Suzhou, China
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AACL | loresmt
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Publisher:
Association for Computational Linguistics
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Pages:
23–32
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URL:
https://aclanthology.org/2020.loresmt-1.3
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Cite (ACL):
Allahsera Auguste Tapo, Bakary Coulibaly, Sébastien Diarra, Christopher Homan, Julia Kreutzer, Sarah Luger, Arthur Nagashima, Marcos Zampieri, and Michael Leventhal. 2020. Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara. In Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages, pages 23–32, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara (Tapo et al., loresmt 2020)
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https://aclanthology.org/2020.loresmt-1.3.pdf