Data-adaptive Transfer Learning for Translation: A Case Study in Haitian and Jamaican
Nathaniel Robinson, Cameron Hogan, Nancy Fulda, David R. Mortensen
Abstract
Multilingual transfer techniques often improve low-resource machine translation (MT). Many of these techniques are applied without considering data characteristics. We show in the context of Haitian-to-English translation that transfer effectiveness is correlated with amount of training data and relationships between knowledge-sharing languages. Our experiments suggest that for some languages beyond a threshold of authentic data, back-translation augmentation methods are counterproductive, while cross-lingual transfer from a sufficiently related language is preferred. We complement this finding by contributing a rule-based French-Haitian orthographic and syntactic engine and a novel method for phonological embedding. When used with multilingual techniques, orthographic transformation makes statistically significant improvements over conventional methods. And in very low-resource Jamaican MT, code-switching with a transfer language for orthographic resemblance yields a 6.63 BLEU point advantage.- Anthology ID:
- 2022.loresmt-1.5
- 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:
- 35–42
- Language:
- URL:
- https://aclanthology.org/2022.loresmt-1.5
- DOI:
- Cite (ACL):
- Nathaniel Robinson, Cameron Hogan, Nancy Fulda, and David R. Mortensen. 2022. Data-adaptive Transfer Learning for Translation: A Case Study in Haitian and Jamaican. In Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022), pages 35–42, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- Data-adaptive Transfer Learning for Translation: A Case Study in Haitian and Jamaican (Robinson et al., LoResMT 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.loresmt-1.5.pdf