The Machine Translation Post-Editing Annotation System (MTPEAS): A standardized and user-friendly taxonomy for student post-editing quality assessment
(2024) Translation Spaces — Vol. Online First, n° Online First, p. 1-28 (2024)
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- 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.
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Citations
Bodart, R., Piette, J., & Lefer, M.-A. (2024). The Machine Translation Post-Editing Annotation System (MTPEAS): A standardized and user-friendly taxonomy for student post-editing quality assessment. Translation Spaces, Online First(Online First), 1-28. https://doi.org/10.1075/ts.24002.bod (Original work published 2024)