Abstract
Translating conversational text, particularly in customer support contexts, presents unique challenges due to its informal and unstructured nature. We propose a context-aware LLM translation system that leverages conversation summarization and dialogue history to enhance translation quality for the English-Korean language pair. Our approach incorporates the two most recent dialogues as raw data and a summary of earlier conversations to manage context length effectively. We demonstrate that this method significantly improves translation accuracy, maintaining coherence and consistency across conversations. This system offers a practical solution for customer support translation tasks, addressing the complexities of conversational text.- Anthology ID:
- 2024.wmt-1.102
- Volume:
- Proceedings of the Ninth Conference on Machine Translation
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Barry Haddow, Tom Kocmi, Philipp Koehn, Christof Monz
- Venue:
- WMT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1011–1015
- Language:
- URL:
- https://aclanthology.org/2024.wmt-1.102
- DOI:
- 10.18653/v1/2024.wmt-1.102
- Cite (ACL):
- Mingi Sung, Seungmin Lee, Jiwon Kim, and Sejoon Kim. 2024. Context-Aware LLM Translation System Using Conversation Summarization and Dialogue History. In Proceedings of the Ninth Conference on Machine Translation, pages 1011–1015, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Context-Aware LLM Translation System Using Conversation Summarization and Dialogue History (Sung et al., WMT 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.wmt-1.102.pdf