Machine Translation Between High-resource Languages in a Language Documentation Setting
Katharina Kann, Abteen Ebrahimi, Kristine Stenzel, Alexis Palmer
Abstract
Language documentation encompasses translation, typically into the dominant high-resource language in the region where the target language is spoken. To make data accessible to a broader audience, additional translation into other high-resource languages might be needed. Working within a project documenting Kotiria, we explore the extent to which state-of-the-art machine translation (MT) systems can support this second translation – in our case from Portuguese to English. This translation task is challenging for multiple reasons: (1) the data is out-of-domain with respect to the MT system’s training data, (2) much of the data is conversational, (3) existing translations include non-standard and uncommon expressions, often reflecting properties of the documented language, and (4) the data includes borrowings from other regional languages. Despite these challenges, existing MT systems perform at a usable level, though there is still room for improvement. We then conduct a qualitative analysis and suggest ways to improve MT between high-resource languages in a language documentation setting.- Anthology ID:
- 2022.fieldmatters-1.3
- Volume:
- Proceedings of the first workshop on NLP applications to field linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Venue:
- FieldMatters
- SIG:
- Publisher:
- International Conference on Computational Linguistics
- Note:
- Pages:
- 26–33
- Language:
- URL:
- https://aclanthology.org/2022.fieldmatters-1.3
- DOI:
- Cite (ACL):
- Katharina Kann, Abteen Ebrahimi, Kristine Stenzel, and Alexis Palmer. 2022. Machine Translation Between High-resource Languages in a Language Documentation Setting. In Proceedings of the first workshop on NLP applications to field linguistics, pages 26–33, Gyeongju, Republic of Korea. International Conference on Computational Linguistics.
- Cite (Informal):
- Machine Translation Between High-resource Languages in a Language Documentation Setting (Kann et al., FieldMatters 2022)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2022.fieldmatters-1.3.pdf