@inproceedings{xing-etal-2023-mian,
    title = "面向机器翻译的汉英小句复合体转换生成能力调查(Investigation of the Clause Complexes Transfer and Generation Capability from {C}hinese to {E}nglish for Machine Translation)",
    author = "Xing, Fukun  and
      Xu, Jianing",
    editor = "Sun, Maosong  and
      Qin, Bing  and
      Qiu, Xipeng  and
      Jiang, Jing  and
      Han, Xianpei",
    booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics",
    month = aug,
    year = "2023",
    address = "Harbin, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.ccl-1.9/",
    pages = "102--112",
    language = "zho",
    abstract = "``小句复合体由小句组合而成,不同语言在小句的组合模式上存在差异,该差异对机器翻译有何影响尚不清楚。本文以汉英机器翻译为例,选取多语体的汉语小句复合体及专家译文,从话头共享关系和共享类型两方面对主流机器翻译系统以及ChatGPT开展调查。结果显示,与专家译文相比,机器翻译的小句复合体转换生成能力存在较大不足,表现为机器翻译在话头补足、转换、提炼等方面的能力较弱,小句组合模式单一且带有明显的汉语原文痕迹,译文的地道性受到较大影响。''"
}