Quality estimation for machine translation: some lessons learned
Machine Translation
Cost-sensitive active learning for computer-assisted translation
Pattern Recognition Letters
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Post-editing performed by translators is an increasingly common use of machine translated texts. While high quality MT may increase productivity, post-editing poor translations can be a frustrating task which requires more effort than translating from scratch. For this reason, estimating whether machine translations are of sufficient quality to be used for post-editing and finding means to reduce post-editing effort are an important field of study. Post-editing effort consists of different aspects, of which temporal effort, or the time spent on post-editing, is the most visible and involves not only the technical effort needed to perform the editing, but also the cognitive effort required to detect and plan necessary corrections. Cognitive effort is difficult to examine directly, but ways to reduce the cognitive effort in particular may prove valuable in reducing the frustration associated with post-editing work. In this paper, we describe an experiment aimed at studying the relationship between technical post-editing effort and cognitive post-editing effort by comparing cases where the edit distance and a manual score reflecting perceived effort differ. We present results of an error analysis performed on such sentences and discuss the clues they may provide about edits requiring great cognitive effort compared to the technical effort, on one hand, or little cognitive effort, on the other.