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.

翻译：在客户支持等场景中，对话文本因其非正式与非结构化的特性，给翻译工作带来了独特的挑战。本文提出一种上下文感知的大语言模型翻译系统，该系统利用对话摘要与历史记录来提升英语-韩语语言对的翻译质量。我们的方法通过引入最近两轮对话的原始数据及早期对话的摘要，以有效管理上下文长度。实验表明，该方法显著提高了翻译准确性，保持了对话间的连贯性与一致性。本系统为客服翻译任务提供了一种实用解决方案，有效应对了对话文本的复杂性。