Abstract
This paper describes the submission of DUTNLP Lab submission to WMT23 Discourse-Level Literary Translation in the Chinese to English translation direction under unconstrained conditions. Our primary system aims to leverage a large language model with various prompt strategies, which can fully investigate the potential capabilities of large language models for discourse-level neural machine translation. Moreover, we test a widely used discourse-level machine translation model, G-transformer, with different training strategies. In our experimental results, the method with large language models achieves a BLEU score of 28.16, while the fine-tuned method scores 25.26. These findings indicate that selecting appropriate prompt strategies based on large language models can significantly improve translation performance compared to traditional model training methods.- Anthology ID:
- 2023.wmt-1.31
- Volume:
- Proceedings of the Eighth Conference on Machine Translation
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Philipp Koehn, Barry Haddow, Tom Kocmi, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 296–301
- Language:
- URL:
- https://aclanthology.org/2023.wmt-1.31
- DOI:
- 10.18653/v1/2023.wmt-1.31
- Cite (ACL):
- Anqi Zhao, Kaiyu Huang, Hao Yu, and Degen Huang. 2023. DUTNLP System for the WMT2023 Discourse-Level Literary Translation. In Proceedings of the Eighth Conference on Machine Translation, pages 296–301, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- DUTNLP System for the WMT2023 Discourse-Level Literary Translation (Zhao et al., WMT 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.wmt-1.31.pdf