Some Tradeoffs in Continual Learning for Parliamentary Neural Machine Translation Systems
Rebecca Knowles, Samuel Larkin, Michel Simard, Marc A Tessier, Gabriel Bernier-Colborne, Cyril Goutte, Chi-kiu Lo
Abstract
In long-term translation projects, like Parliamentary text, there is a desire to build machine translation systems that can adapt to changes over time. We implement and examine a simple approach to continual learning for neural machine translation, exploring tradeoffs between consistency, the model’s ability to learn from incoming data, and the time a client would need to wait to obtain a newly trained translation system.- Anthology ID:
- 2024.amta-research.10
- Volume:
- Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)
- Month:
- September
- Year:
- 2024
- Address:
- Chicago, USA
- Editors:
- Rebecca Knowles, Akiko Eriguchi, Shivali Goel
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 102–118
- Language:
- URL:
- https://aclanthology.org/2024.amta-research.10
- DOI:
- Cite (ACL):
- Rebecca Knowles, Samuel Larkin, Michel Simard, Marc A Tessier, Gabriel Bernier-Colborne, Cyril Goutte, and Chi-kiu Lo. 2024. Some Tradeoffs in Continual Learning for Parliamentary Neural Machine Translation Systems. In Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), pages 102–118, Chicago, USA. Association for Machine Translation in the Americas.
- Cite (Informal):
- Some Tradeoffs in Continual Learning for Parliamentary Neural Machine Translation Systems (Knowles et al., AMTA 2024)
- PDF:
- https://preview.aclanthology.org/landing_page/2024.amta-research.10.pdf