对文学作品翻译质量的评价一直是学界关注的难题｡人工评估和传统的单一指标自动评估均有一定的局限性｡本文利用 BERT 深度学习模型,调用公开的大型语料库预训练集,从文本相似度､情感评分以及意象和主题一致性三个角度对英文小说《刀锋》及其中译本进行评价分析,用量化的指标表征翻译的质量｡研究表明,所选的两个中译本较好地还原了原文的内容,具有较高的翻译水准｡同时也证明,利用BER词向量技术计量分析译文的质量具备可行性｡

The evaluation of the translation quality of literary works has always been a challenging issue in translation studies. Both manual evaluation and traditional single index automatic evaluation have certain limitations. This paper uses the BERT deep learning model and a publicly available large scale corpus pre trained corpus to evaluate and analyze the English novel The Razor’s Edge and its Chinese translations from the perspectives of text similarity, sentiment scores, and the consistency of imagery and themes. Quantitative indicators are used to represent the translation quality. The research shows that the selected two Chinese translations faithfully reproduce the content of the original text and demonstrate a high level of translation quality. The study also confirms the feasibility of using BERT based word vector technology to quantitatively evaluate and analyze the quality of literary translation.