A Case Study on Post-editing Machine Translation: Tasks, Challenges, and Attitudes

Abstract

While neural machine translation is gaining wider commercial traction as a useful tool for translating technical texts, the professional translator community is hesitant about post-editing machine translation. A qualitative case study guided by the socio-technical idea of the importance to achieve a balance between human and technical aspects of a system was conducted with a focus on post-editing machine translation. The eight translators who took part in this study were given an identical task of post-editing machine-translated technical text. Interviews were then conducted allowing translators to express their perspectives. The interview questions were designed to address how translators approached the study task, what caused them difficulties about post-editing, and their attitudes towards machine translation. The analysis of the data collected for this case showed that while there was variation to how the translators approached the post-editing task, there appeared to be a workflow shared by the majority of participants. Regarding the challenges translators faced in post-editing, the analysis suggests the key factors affecting the translators were decision making and knowledge of the translation tool. This research contributes to the knowledge about translators’ attitudes by showing a difference of opinion between professional and personal machine translation use.