Worlds Within Words: Translating Culture in Ancient Chinese Texts with Multi-Agent Coordination
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866916071935574016 |
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| author | He, Xiaoqi Lan, Kaixin You, Mu Fang, Tao Chao, Lidia S. Wong, Derek F. |
| author_facet | He, Xiaoqi Lan, Kaixin You, Mu Fang, Tao Chao, Lidia S. Wong, Derek F. |
| contents | 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}. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2606_01276 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Worlds Within Words: Translating Culture in Ancient Chinese Texts with Multi-Agent Coordination He, Xiaoqi Lan, Kaixin You, Mu Fang, Tao Chao, Lidia S. Wong, Derek F. Computation and Language 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}. |
| title | Worlds Within Words: Translating Culture in Ancient Chinese Texts with Multi-Agent Coordination |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2606.01276 |