Second language learners' post-editing strategies for machine translation errors

Abstract

Considering noticeable improvements in the accuracy of Google Translate recently, the aim of this study was to examine second language (L2) learners' ability to use post-editing (PE) strategies when applying AI tools such as the neural machine translator (MT) to solve their lexical and grammatical problems during L2 writing. This study examined 57 students' MT output and post-edited (PEd) texts to analyze MT errors and the PE strategies that L2 learners employed to express target meaning. The MT errors occurred from mistranslation, missing words, ungrammaticality, and extra words. To modify the MT sentences, the learners employed PE strategies such as deletion, paraphrase, and grammar correction. Successfulness of PE was gauged by comparing sentence adequacy scores of the MT output and PEd texts. The results of the study highlight that L2 proficiency influences the learners' ability to deploy appropriate PE strategies. The taxonomy of MT errors and PE strategies provides a model for understanding the competence required as part of the new writing ability in the AI era. Implications are discussed as to how L2 learners are required to be trained in using MT by detecting MT errors and deploying appropriate PE strategies.