Translating between languages with drastically different grammatical conventions poses challenges, not just for human interpreters but also for machine translation systems. In this work, we specifically target the translation challenges posed by attributive nouns in Chinese, which frequently cause ambiguities in English translation. By manually inserting the omitted particle X ('DE'). In news article titles from the Penn Chinese Discourse Treebank, we developed a targeted dataset to fine-tune Hugging Face Chinese to English translation models, specifically improving how this critical function word is handled. This focused approach not only complements the broader strategies suggested by previous studies but also offers a practical enhancement by specifically addressing a common error type in Chinese-English translation.

翻译：在语法规范迥异的语言之间进行翻译，不仅对人类译员构成挑战，对机器翻译系统亦是如此。本研究专门针对汉语中定语性名词引发的翻译难题展开探讨，这类结构常导致英译时产生歧义。我们通过人工补全宾州中文篇章树库新闻标题中省略的助词“的”，构建了一个针对性数据集，用以微调Hugging Face平台的汉英翻译模型，重点优化对这一关键功能词的处理方式。这一聚焦性方法不仅对先前研究提出的宏观策略形成了补充，而且通过专门解决汉英翻译中的一个常见错误类型，提供了切实可行的性能提升路径。