Worlds Within Words: Translating Culture in Ancient Chinese Texts with Multi-Agent Coordination

• URL: http://arxiv.org/abs/2606.01276v1
• Date: Sun, 31 May 2026 14:58:03 GMT
• Title: Worlds Within Words: Translating Culture in Ancient Chinese Texts with Multi-Agent Coordination
• Abstract summary: 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.<n>We propose textbfMACAT, a framework that identifies culturally salient phrases and injects concise explanatory knowledge when necessary.<n>MACAT consistently outperforms both the backbone model and general-purpose MT baselines on 100 TCM documents.
• Score: 42.36051310803725
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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