Human vs Machine Translation: A Comparative Study of Contextual Accuracy

Abstract

Translation has a significant impact on the process of intercultural communication, international business, and transfer of knowledge. In recent decades, major advancements in machine translations (MT) have been experienced especially with the emergence of neural machine translations (NMT), which has significantly enhanced significantly the levels of fluency, grammar, and accessibility. Whether or not MT can maintain contextual accuracy, particularly in areas where subtlety and idiomatic phrasing, emotional appeal and cultural sensitivity are more important, still hangs. In this paper, a comparative analysis of human and machine translation is provided mainly on the contextual fidelity. A combination of the mixed-methods development process included a corpus of legal texts, literary texts, medical texts and marketing texts in Arabic, French and Japanese, which were translated by professional human translators, and the most popular systems of machine translators (Google Translate, DeepL, Microsoft Translator). A five-criterion evaluation system, that is, semantic fidelity, cultural appropriateness, idiomatic accuracy, emotional tone, and grammatical correctness, was used to evaluate translations. Objective data also reveal that, although MT scores close to human functional levels of grammar and fundamental semantics faithfulness, it perpetually performs poorly when it comes to the expression of idioms, tonality, and cultural subtext. Qualitative results also support the fact that MT cannot process light contextual clues, including irony, formality hierarchies, and culturally associated metaphors. In comparison, human translators are better at cultural adaptation as well as cost-effective and scalable. The research finds that hybrid systems in which MT delivers initial translations that are further processed with the assistance of human post-editing are the way to go. The implications of these findings for the research of translation, AI ethics, and professional training are immense, as they can confirm that there is ongoing relevance of human judgment in a translation field that is becoming more automated.