Abstract
Minimum Bayes Risk (MBR) decoding can significantly improve translation performance of Multilingual Large Language Models (MLLMs). However, MBR decoding is computationally expensive. We show how the recently developed Reinforcement Learning technique, Direct Preference Optimization (DPO), can fine-tune MLLMs to get the gains of MBR without any additional computation in inference. Our method uses only a small monolingual fine-tuning set and yields significantly improved performance on multiple NMT test sets compared to MLLMs without DPO.- Anthology ID:
- 2024.naacl-short.34
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 391–398
- Language:
- URL:
- https://aclanthology.org/2024.naacl-short.34
- DOI:
- Cite (ACL):
- Guangyu Yang, Jinghong Chen, Weizhe Lin, and Bill Byrne. 2024. Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decoding. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 391–398, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decoding (Yang et al., NAACL 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.naacl-short.34.pdf