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}.
| Comments: | The preprint manuscript is 20 pages long and is currently under review |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.01276 [cs.CL] |
| (or arXiv:2606.01276v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.01276 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tao Fang [view email]
[v1]
Sun, 31 May 2026 14:58:03 UTC (8,951 KB)