Evaluating the Accuracy of Machine Translation in Rendering Arabic Idioms into English: A Case Study on Google Translate and ChatGPT

Abstract

This study investigates the translation of 145 Arabic idioms by two prominent machine translation systems: Google Translate and ChatGPT. The research employs both quantitative and qualitative methodologies to analyze translation approaches and assess accuracy in conveying idiomatic meanings from Arabic to English. Data collection involved Arabic idiomatic expressions from literary sources, cultural texts, and linguistic databases. The analysis framework builds upon Baker's (1992) taxonomy of translation strategies. Quantitative findings reveal that Google Translate employed literal translation in 74% of cases, while ChatGPT demonstrated more varied approaches with 48% literal translations. For sense-based translations using non-figurative language, ChatGPT led with 41%, compared to Google Translate's 15%. When examining figurative language translations, ChatGPT achieved 11% compared to Google Translate's 11%. The qualitative analysis highlights persistent challenges in both systems regarding cultural context preservation and semantic accuracy. The study concludes that while technological advances have improved machine translation capabilities, rendering Arabic idioms into English remains problematic due to cultural-linguistic gaps and contextual complexities inherent in idiomatic expressions.