The Machine Translation Post-Editing Annotation System (MTPEAS)

Abstract

Abstract Machine translation post-editing quality evaluation has received relatively little attention in translation pedagogy to date. It is a time-consuming process that involves the comparison of three texts (source text, machine translation and student post-edited text) and the systematic identification and correction of students’ edits (or absence thereof) of machine translation (MT) output. There are as yet no widely available, standardized, user-friendly annotation systems for use in translator education. In this article, we address this gap by describing the Machine Translation Post-Editing Annotation System (MTPEAS). MTPEAS includes a taxonomy of seven categories that are presented in easy-to-understand terms: Value-adding edits, Successful edits, Unnecessary edits, Incomplete edits, Error-introducing edits, Unsuccessful edits, and Missing edits. We then assess the robustness of the MTPEAS taxonomy in a pilot study of 30 students’ post-edited texts and offer some preliminary findings on students’ MT error identification and correction skills.