Investigating ChatGPT’s Genre-Sensitive Translation Behavior: A Theoretical Analysis Using Newmark’s Translation Method - Febriansyah, Gema; Wallidaeny, Alwin Firdaus (Journal of English Language and Education, 2025)
This study investigates ChatGPT’s genre-sensitive translation behavior using Newmark’s translation theory. Three English texts from distinct genres, that are academic (3519 words), literary (4153 words), and news (1242 words) were selected and translated using ChatGPT. The analysis focuses on identifying the dominant translation methods and procedures applied by the model across genres. Findings reveal that ChatGPT employs Semantic translation predominantly in academic texts to preserve terminological accuracy and logical clarity, while Communicative translation is favored in literary and news texts to enhance fluency and readability. The analysis also highlights genre-specific procedures such as modulation, transposition, equivalence, and cultural substitution. While ChatGPT demonstrates adaptive strategies in handling different genres, it struggles with idiomatic expressions and culturally embedded metaphors, particularly in literary texts. The study underscores the model’s surface-level functional awareness but also its limitations in interpreting deeper pragmatic and cultural nuances. These findings offer insights into AI-assisted translation and suggest pedagogical implications for training future translators.