Strategically Informed Machine Translation Post-Editing: Enhancing Translation Performance among Intermediate Chinese EFL Learners

Abstract

Machine translation (MT) errors may be seen as a limitation, but it is precisely these imperfections that provide learners with valuable opportunities for error-driven practice through post-editing (PE), thus developing their language proficiency and, in turn, supporting translation performance. However, little is known about how machine translation post-editing (MTPE) benefits intermediate learners of English as a foreign language (EFL). This empirical study explores the role of strategically informed MTPE practices in supporting intermediate Chinese EFL learners’ linguistic and translation development. Participants completed human translation and MTPE tasks guided by PE strategies, followed by reflection on their edits. Text complexity analysis using Eng-Editor revealed that post-edited texts exhibited higher overall textual, lexical, and syntactic complexity than human translations, with correct revisions marginally exceeding errors, suggesting that intermediate learners still struggle with error detection and correction. These findings underscore the need for clearer strategic support in PE tasks. Sentiment analysis of learner reflections based on SnowNLP with contextual adjustments showed a neutral-to-negative attitude toward PE, highlighting challenges related to syntactic proficiency and technical support. This study contributes empirical evidence on pedagogical MTPE use with intermediate learners, leading to refined PE guidelines and a multi-task design that emphasizes both selection competence and linguistic development to enhance translation performance.