用多智能体协作精准翻译古汉语文化词，既不漏意又不啰嗦。
Worlds Within Words: Translating Culture in Ancient Chinese Texts with Multi-Agent Coordination
- 设计多智能体框架，动态识别需解释的文化关键词。
- 在100份中医典籍和20章《论语》上超越基线模型。
- 适合研究古籍、跨文化传播的学者和译者使用。
基于大语言模型的机器翻译虽推进了跨文化交流，但在翻译古汉语中的文化负载词（CLWs）时仍面临挑战。问题不仅在于词汇对齐，更在于判断何时以及如何为缺乏背景知识的读者阐明文化内涵。直译常保留表面形式却丢失深层概念，过度解释则影响简洁性与可读性。为此，本文将文化词翻译定义为选择性显化任务，提出多智能体文化感知翻译框架MACAT，能动态识别文化显著短语，并在必要时注入简洁解释。MACAT还包含质量感知重排序模块和多轮评估智能体，从术语精度、可读性、忠实度、文化保留与文化显化五个维度评估翻译质量。在传统中医经典和《论语》20章子集上的实验表明，在统一GPT-5.4评估设置下，MACAT在100份中医文献和20章《论语》上持续优于基线模型与通用机器翻译方案。
原文摘要 · Abstract (English)
Large language model (LLM)-based machine translation has advanced cross-cultural communication, yet it still struggles with culture-loaded words (CLWs) in ancient Chinese texts. The challenge extends beyond lexical alignment to deciding when and how culture-dependent knowledge should be explicated for readers lacking relevant background. Literal translation often preserves surface forms while missing underlying concepts, whereas over-explicitation harms conciseness and readability. To address this problem, we formulate CLW translation as a selective explicitation task and propose \textbf{MACAT}, a \textbf{M}ulti-\textbf{A}gent \textbf{C}ulture-\textbf{A}ware \textbf{T}ranslation framework that dynamically identifies culturally salient phrases and injects concise explanatory knowledge when necessary. MACAT further incorporates a quality-aware reranking module for candidate selection and a multi-round evaluation agent that assesses translations across terminological precision, readability, fidelity, cultural preservation, and cultural explicitation. Experiments on traditional Chinese medicine (TCM) classics and the \textit{Analects} show that, under a unified GPT-5.4 evaluation setting, MACAT consistently outperforms both the backbone model and general-purpose MT baselines on 100 TCM documents and a 20-chapter subset of the \textit{Analects}.
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