用对话历史和摘要提升客服翻译的连贯性
Context-Aware LLM Translation System Using Conversation Summarization and Dialogue History
- 结合最新对话与早期摘要，管理上下文长度
- 英文→韩文翻译准确率显著提升，保持对话一致性
- 适合需要连贯客服翻译的场景，如跨国支持
客服场景中的对话翻译因非正式、无结构化而面临挑战。我们提出一种基于大语言模型的上下文感知翻译系统，利用对话摘要与对话历史增强英韩翻译质量。方法采用最近两条对话作为原始数据，并引入早期对话摘要以有效控制上下文长度。实验表明，该方法显著提升翻译准确性，维持跨轮次的语义连贯性与一致性。该系统为客服翻译任务提供了实用解决方案，有效应对对话文本的复杂性。
原文摘要 · Abstract (English)
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.
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