Machine Translation Post-Editing Behavior of Undergraduate EFL Students Across Two Genres.

This article investigates how undergraduate Turkish learners of English as a foreign language (EFL) differ in their machine translation (MT) post-editing behavior across two genres: short stories and research articles. In a second-year online translation course, 25 students post-edited Google-translated texts from Turkish to English, with analyses focusing on the frequency, linguistic level (morphological, syntactic, semantic, pragmatic), success, and types of post-edits (addition, deletion, substitution, reorder, rewrite). Results showed significantly more post-editing activity and engagement in the short story genre, though the research article genre yielded a higher success rate in improvements. The study highlights genre as a key factor influencing post-editing behavior and suggests pedagogical implications for integrating MT post-editing into translation training, emphasizing the potential of literary genres to foster greater student engagement.