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The study aims to investigate how strategically informed post-editing practices benefit intermediate Chinese EFL learners in translation tasks.

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

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The study aims to investigate how strategically informed post-editing practices benefit intermediate Chinese EFL learners in translation tasks.
Participants completed human translation and machine translation post-editing tasks.
Text complexity analysis using Eng-Editor assessed revision quality and complexity of post-edited texts.
Sentiment analysis with SnowNLP evaluated learner attitudes toward post-editing.
Post-edited texts showed higher textual, lexical, and syntactic complexity than human translations.
Correct revisions slightly outnumbered errors, indicating challenges with error detection among learners.
Learner reflections revealed a neutral-to-negative sentiment towards post-editing practices.

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Empirical study explores machine translation post-editing's impact on language skills in learners, suggesting improvements are needed.

Z Li (2026) studied this question.

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