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
- Editors:
- Atul Kr. Ojha, Chao-Hong Liu, Ekaterina Vylomova, Jade Abbott, Jonathan Washington, Nathaniel Oco, Tommi A Pirinen, Valentin Malykh, Varvara Logacheva, Xiaobing Zhao
- Venue:
- LoResMT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 23–34
- Language:
- URL:
- https://aclanthology.org/2022.loresmt-1.4
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/aacl-23-doi-ingestion/2022.loresmt-1.4.pdf