Abstract
This paper describes Lan-Bridge Translation systems for the WMT 2023 General Translation shared task. We participate in 2 directions: English to and from Chinese. With the emergence of large-scale models, various industries have undergone significant transformations, particularly in the realm of document-level machine translation. This has introduced a novel research paradigm that we have embraced in our participation in the WMT23 competition. Focusing on advancements in models such as GPT-3.5 and GPT-4, we have undertaken numerous prompt-based experiments. Our objective is to achieve optimal human evaluation results for document-level machine translation, resulting in our submission of the final outcomes in the general track.- Anthology ID:
- 2023.wmt-1.15
- 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:
- 166–169
- Language:
- URL:
- https://aclanthology.org/2023.wmt-1.15
- DOI:
- 10.18653/v1/2023.wmt-1.15
- Cite (ACL):
- Yangjian Wu and Gang Hu. 2023. Exploring Prompt Engineering with GPT Language Models for Document-Level Machine Translation: Insights and Findings. In Proceedings of the Eighth Conference on Machine Translation, pages 166–169, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Exploring Prompt Engineering with GPT Language Models for Document-Level Machine Translation: Insights and Findings (Wu & Hu, WMT 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.wmt-1.15.pdf