A comparative analysis of Google Translate and ChatGPT in translating Borneo Bulletin News into standard Malay - Syafiee, Amieziezaitul Syazlien Ezzeq Ezrynah Amirul; Yaqin, Lalu Nurul (Journal of Research on English and Language Learning (J-REaLL), 2025)
In the digital era, machine translation tools like Google Translate and ChatGPT have become essential for cross-linguistic communication, particularly in translating formal texts such as news articles. This study aims to evaluate the effectiveness of these tools in translating English news articles from Borneo Bulletin into Standard Malay, focusing on lexical accuracy, syntactic structure, and contextual appropriateness. A qualitative comparative analysis was conducted by translating selected articles using both Google Translate and ChatGPT-4. The outputs were manually analyzed using a qualitative rubric guided by meaning-based translation theory, focusing on word choice, sentence structure, the original tone, and cultural nuances. The results indicate that ChatGPT-4 outperforms Google Translate in maintaining lexical precision, grammatical coherence, and contextual relevance. ChatGPT-4 demonstrated superior handling of idiomatic expressions and complex sentence constructions, producing translations that adhered closely to journalistic norms in Standard Malay. However, both tools exhibited limitations in accurately translating culturally specific references and specialized terminology. Google Translate, despite improvements through neural machine translation, tended to produce overly literal translations, leading to a loss of tone and clarity in formal contexts. These findings highlight the potential and limitations of machine translation in journalism, where accuracy and tone are crucial for public trust and information dissemination. The study emphasizes the necessity for enhanced machine translation algorithms and the integration of human feedback to elevate translation quality in low-resource languages such as Malay.