In the field of Japanese-Chinese translation linguistics, the issue of correctly translating attributive clauses has persistently proven to be challenging. Present-day machine translation tools often fail to accurately translate attributive clauses from Japanese to Chinese. In light of this, this paper investigates the linguistic problem underlying such difficulties, namely how does the semantic role of the modified noun affect the selection of translation patterns for attributive clauses, from a linguistic perspective. To ad-dress these difficulties, a pre-edit scheme is proposed, which aims to enhance the accuracy of translation. Furthermore, we propose a novel two-step prompt strategy, which combines this pre-edit scheme with ChatGPT, currently the most widely used large language model. This prompt strategy is capable of optimizing translation input in zero-shot scenarios and has been demonstrated to improve the average translation accuracy score by over 35%.

在日汉翻译语言学领域中，我们提出了一个基于语言学角度的研究问题：修饰名词的语义角色如何影响专有名词的属性翻译模板的选择，进而提出了一个增强翻译准确度的预编辑方案和一个结合ChatGPT的新颖的双阶段提示策略，在零-shot情况下优化了翻译输入，平均翻译准确度提高了35%以上。

基于语言学的ChatGPT提示对日中机器翻译的增强：以定语从句为例的案例研究