{"ID":2921760,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-03T05:56:00.181519634Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.01276","arxiv_id":"2606.01276","title":"Worlds Within Words: Translating Culture in Ancient Chinese Texts with Multi-Agent Coordination","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}.","short_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 r...","url_abs":"https://arxiv.org/abs/2606.01276","url_pdf":"https://arxiv.org/pdf/2606.01276v1","authors":"[\"Xiaoqi He\",\"Kaixin Lan\",\"Mu You\",\"Tao Fang\",\"Lidia S. Chao\",\"Derek F. Wong\"]","published":"2026-05-31T14:58:03Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}