A Case Study on Post-editing Machine Translation: Tasks, Challenges, and Attitudes
Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ăślikool
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.
Description
Keywords
neural machine translation, post-editing, human-computer interaction, socio-technical systems