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.
endingpage:
25
format.extent:
25
identifier.citation:
Shin, D., & Chon, Y. V. (2023). Second language learners’ post-editing strategies for machine translation errors. Language Learning & Technology, 27(1), 1–25. https://hdl.handle.net/10125/73523
identifier.issn:
1094-3501
identifier.uri:
https://hdl.handle.net/10125/73523
language:
eng
number:
1
publicationname:
Language Learning & Technology
publisher:
University of Hawaii National Foreign Language Resource Center Center for Language & Technology
rights.license:
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License