Comparing Human and Machine Translation in Facilitating Comprehension of English Political News Articles in Arabic

Abstract

This study addresses the critical gap in understanding how machine translation (specifically Google Translate), affects the comprehension of political discourse compared to human translation (HT). As political news is usually full of historical metaphors and cultural nuances, the study aims to evaluate the effectiveness of Google translate (GT) in facilitating the comprehension of English political news articles for translation students. The significance of this work is centred in examining the machine’s ability to achieve communicative success, not just surface analysis of grammatical errors. The study was conducted at the Libyan Academy of Postgraduate Students- Al Baida, in which a mixed-method approach was employed to capture a comprehensive data set. Quantitatively, the study used web-based survey design via Google Forms featuring Likers-scale questions across three selected political articles. Qualitatively, a manual linguistic analysis of 20 political terms was conducted to identify the literalism in GT’s output. The study concludes that while GT demonstrates a high level of proficiency in translating context, it consistently fails in translating individual terms and idiomatic expressions.