试析医学论文英译中审校策略——以机器学习为例

Research on the Strategy of Chinese Translation Proofreading of English Medical Papers—A Case Study of Machine Learning

在实际翻译中,翻译质量评估是一项十分重要的工作。但是,二十世纪七十年代以后,翻译质量评估一直没有被作为一个单独的研究领域来进行。本文基于Python的自带模块Spacy,将文本分类算法SVC应用于医学论文英译中的审校过程中,利用SHAP模块对模型进行解释,对普通译文与专业译文的用词进行可视化,以便能清晰地反映出普通译文与专业译文的差距,从而进一步提升译文的质量。

In practical translation, translation quality assessment is a very important work. However, after the 1970s, translation quality assessment has not been carried out as a separate research field. In this paper, based on built-in module Spacy of Python, the text classification algorithm SVC is applied to the proofreading process of the English translation of medical papers. SHAP module is used to explain the model and visualize the terms of ordinary translation and professional translation, clearly reflecting the gap between the two translations, so as to further improve the quality of translation.