基于机器学习方法的《德伯家的苔丝》中文译本翻译风格考察

An Investigation of Literary Translation Style Through ML Method:A Case Study of Tess of D'Urberville

研究使用机器学习中的分类和聚类方法,基于自建平行语料库,考察哈代名作《德伯家的苔丝》中文三译本的翻译风格。从68个全部特征中筛选出15个显著特征,并结合实例进行量性融合的阐释和总结。结果表明,显著特征能够有效区分三译本风格差异,分类、聚类实验的平均准确率均达到97%左右,提示出各译本在词汇、句法、语篇上的不同风格特征和译者的个人偏好。研究在为既往质性研究提供数据支持和细粒度分析的同时,也提出了一些纠正性结论,如张谷若译本词汇密度更大、被字句比例极少、成语比例差别不大等,并为翻译风格和译者风格研究方法提供了一定改进和补充。

Tthis paper applies classification and clustering methods in machine learning studies,builds a parallel corpus,and examines the translation styles of the three versions of Hardy's masterpiece Tess of the D'Urberville.From a total of 68 features,15 significant ones are selected and quantitatively synthesized with examples for detailed explanation.The results show that these salient features can effectively distinguish the stylistic differences among the three translations,with both classifying and clustering experiments achieving an average accuracy rate of about 97%.The study found that at the document-level,each translation shows different style features at the vocabulary,syntax,and discourse aspects;in terms of the keyword level,the frequency differences of certain keywords also present the translator's personal preferences.The article provides data support and fine-grained analysis for previous qualitative research,while also proposing some corrective conclusions,such as the higher lexical density,extremely lower proportion of passive bei sentence,and similar number of idioms in Zhang's translation compared to the others.Eventually we attempt to provide some improvements and supplements to the research methodology in translation style and translator's style studies.