MACHINE TRANSLATION AND HUMAN TRANSLATION: A LINGUISTIC ANALYSIS
Keywords:
machine translation, human translation, syntax, semantics, pragmatics, linguistic analysis, translation studiesAbstract
The rapid evolution of artificial intelligence and neural networks has introduced a new era in translation studies, transforming traditional linguistic approaches into technology-based systems. Machine translation (MT) tools—such as Google Translate, DeepL, and Microsoft Translator—are now capable of processing large volumes of text in seconds, offering quick access to multilingual communication. However, this efficiency often comes at the expense of linguistic depth, contextual awareness, and cultural sensitivity. This study explores the linguistic differences between machine translation (MT) and human translation (HT) by analyzing English–Uzbek translation pairs. The research investigates structural (syntactic), semantic, and pragmatic aspects to determine how each translation type represents meaning. Results show that MT performs accurately at the lexical and grammatical levels but fails to grasp idiomatic expressions, emotional nuances, and contextual subtleties that are essential for authentic communication. In contrast, HT relies on cognitive understanding and creative adaptation, maintaining naturalness and cultural appropriateness.
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