与神经机器翻译和跨类型的人类翻译相比,ChatGPT翻译中的简化基于的研究

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Guangyuan Yao¹, Lingxi Fan²

¹School of Foreign Languages, Central South University, Changsha, Hunan, China.

|December 31, 2025

概括

翻译中的简化因模式和类型而异. 虽然政治和学术文本在词法上简化,但所有类型在语法上都简化,ChatGPT显示了比NMT更复杂的词法.

科学领域:

计算语言学计算语言学
翻译研究翻译研究
自然语言处理自然语言处理.

背景情况:

翻译研究往往假定简化是一种普遍现象.
现有的研究缺乏对人类,NMT和LLM等不同翻译模式的比较分析.

研究的目的:

通过人类翻译 (HT),神经机器翻译 (NMT) 和基于大型语言模型 (LLM) 的翻译 (ChatGPT) 来研究中英翻译的简化.
通过基于的指标来评估词汇和语法复杂性.
分析类型 (政治,小说,学术) 对翻译复杂性的影响.

主要方法:

对中英翻译的比较分析.
使用基于的指标:单格式用于词汇复杂性和语法部分 (POS) 用于语法复杂性.
审查了跨政治,小说和学术类型的翻译.

主要成果:

在政治和学术文本中观察到的词法简化;所有类型的语法简化趋势.
类型对词汇复杂性的影响最小,但对语法复杂性的影响很大 (学术最低,小说最高).
聊天GPT翻译显示出比NMT翻译更高的词汇复杂性.

结论:

简化不是通用的翻译特征,而是由翻译模式和类型所影响的概率现象.
基于的测量有效地捕捉了翻译复杂性的细微差异.
倡导在翻译研究中采用模态方法,承认不同翻译方法的特点.

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