Context-Aware LLM Translation System Using Conversation Summarization and Dialogue History

Autor:	Sung, Mingi, Lee, Seungmin, Kim, Jiwon, Kim, Sejoon
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to WMT 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.16775 Zobrazit plný text záznamu View this record from Arxiv