Translating the Untranslatable: DeepL and ChatGPT on Academic Idioms

Abstract

This research explores the efficacy of two prominent machine translation platforms, DeepL and ChatGPT, in translating academic idioms from English to Indonesian. Academic idioms, situated between discipline-specific jargon and universally understood expressions, pose a challenge for existing translation systems, particularly those rooted in Neural Machine Translation (NMT). The study employs a qualitative descriptive methodology, focusing on translation precision and naturalness, with bilingual experts evaluating translations through a questionnaire, focusing on translation precision and naturalness. The comprehensive analysis involved 50 participants who assessed translations on a scale of accuracy and fluency using Fiederer and O'Brian's (2009) rating scale.  The results indicate that both platforms exhibit strengths and weaknesses in terms of accuracy and fluency. While DeepL demonstrates trust in its translation proficiency, ChatGPT receives a more favorable response, especially regarding fluency. Participants preferred ChatGPT for fluency in handling academic expressions, indicating its adaptability. The study also revealed a general agreement among participants regarding the difficulties both platforms encounter in accurately translating academic idioms, emphasizing continuous requirements for improved machine translation. These insights enhance understanding of machine translation's strengths and limitations in academic setting, with implications for future technology development.