A Case Study of Preferential Changes in the Revision of English-to-Chinese Translation

Preferential changes in revision are a phenomenon commonly observed in other-revision contexts (one translator revises another translator’s work). Revisers tend to over-revise the translations rendered by others even though the translations are accurate and adequate enough. Despite the ongoing debate within translation studies on preferential changes for years, detailed case studies of that phenomenon in the scenario of English-to-Chinese translation remain scarce. The current study selects an English text excerpted from a think tank handbook originally published in the United States and collects its unrevised Chinese translation alongside 12 versions of revision conducted independently by 8 undergraduates with different academic backgrounds, 2 postgraduate translation students, 1 doctoral translation student, and their advisor. All the 12 revisers are native speakers of Mandarin Chinese. This study counts the number of changes made by revisers, analyzes and assesses which changes are necessary and which are preferential (unnecessary), and categorizes and quantifies those preferential changes following the classification proposed by Jean Nitzke and Anne-Kathrin Gros in their research on English-to-German translation. Though adopting this classification, this study adapts it slightly so that it can better suit the scenario of English-to-Chinese translation. The research result reveals that the rate of preferential changes declines notably with the revisers’ advancement in academic levels and improvement in specialized training of translation and revision. This study then explores the reasons behind the phenomenon of preferential changes based on the case study. Besides the linguistic reasons and the revisers’ translation competence, sociological reasons are also considered. Some revisers may prefer to actively look for mistakes in the target text in order to demonstrate that they’ve taken the task seriously and performed their duty well, even though the target text does not need so many changes. Regarding future research, a case study of preferential changes in the post-editing of machine translation, or MTPE, will be conducted as a follow-up. With the rapid development and wide application of artificial intelligence-empowered large language models, it is more and more common to post-edit a machine translation rather than translate a script manually from scratch. Post-editing, like revision, is still conducted by humans, at least in the present and for the several years to come.