The Only Chance to Understand: Machine Translation of the Severely Endangered Low-resource Languages of Eurasia

Anna Mosolova, Kamel Smaili


Abstract
Numerous machine translation systems have been proposed since the appearance of this task. Nowadays, new large language model-based algorithms show results that sometimes overcome human ones on the rich-resource languages. Nevertheless, it is still not the case for the low-resource languages, for which all these algorithms did not show equally impressive results. In this work, we want to compare 3 generations of machine translation models on 7 low-resource languages and make a step further by proposing a new way of automatic parallel data augmentation using the state-of-the-art generative model.
Anthology ID:
2022.loresmt-1.4
Volume:
Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
LoResMT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
23–34
Language:
URL:
https://aclanthology.org/2022.loresmt-1.4
DOI:
Bibkey:
Cite (ACL):
Anna Mosolova and Kamel Smaili. 2022. The Only Chance to Understand: Machine Translation of the Severely Endangered Low-resource Languages of Eurasia. In Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022), pages 23–34, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
The Only Chance to Understand: Machine Translation of the Severely Endangered Low-resource Languages of Eurasia (Mosolova & Smaili, LoResMT 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.loresmt-1.4.pdf