Faithfulness or Fluency? A Comparative Analysis of Neural Machine and Human Translations in Asian News Reporting - Tenis, Amon Bernabas; Sutrisno, Adi; Roman, Sajarwa (Makassar State University, 2026)
This study investigates the comparative performance of human and machine translations in Asia-Pacific news reporting, focusing on faithfulness, fluency, and cultural appropriateness. Although Neural Machine Translation (NMT) systems, particularly Google Translate and ChatGPT, have advanced significantly in fluency, concerns remain regarding their ability to preserve semantic accuracy and convey culturally embedded expressions. ChatGPT, with its transformer-based architecture and self-attention mechanisms, shows progress in resolving inter-sentential dependencies and producing coherent outputs. Nonetheless, the findings reveal that human translators consistently outperform machine systems in maintaining message fidelity, adapting cultural nuances, and preserving journalistic tone. A qualitative comparison of 12 English to Indonesia news texts translated into Bahasa Indonesia highlights key divergences in handling ideological framing and sociolinguistic specificity. These results emphasize the continued importance of human oversight in high-stakes domains such as journalism. While AI-driven systems provide promising fluency, they require contextual and cultural calibration to achieve faithful and responsible translation in global media flows. This study also has implications fro the newsrooms adopting AI in multilingual reporting, as careful editorial intervention remains necessary to ensure accuracy, cultural sensitivity, and credibility