Articles
| Open Access | QUALITY ANALYSIS OF ENGLISH–UZBEK TRANSLATIONS IN MACHINE TRANSLATION SYSTEMS: A LINGUISTIC ASSESSMENT OF AI-BASED TRANSLATION PROCESSES
Narkabilova Rayhonoy Alisherovna ,Abstract
This article examines the quality of English-to-Uzbek translations in artificial intelligence-based machine translation systems — specifically Google Translate, ChatGPT, and other modern neural translation models. The study evaluates translation quality using linguistic criteria, including lexical equivalence, grammatical correctness, syntactic consistency, contextual adequacy, stylistic appropriateness, and pragmatic relevance, across multiple text types (scientific, literary, official documents, and everyday speech). The results identify the most frequent errors in machine translation: incorrect rendering of polysemantic units, grammatical mismatches between nouns and verbs, disrupted consistency in complex sentences, literal translation of idioms, and loss of stylistic features. The article also highlights AI models’ advantages in context analysis and the positive trends of neural translation systems in adapting to the agglutinative features of the Uzbek language. Finally, the study proposes linguistic and technological recommendations to improve the efficiency of machine translation systems.
Keywords
machine translation, artificial intelligence, neural translation systems, English–Uzbek translation, linguistic analysis, translation quality, equivalence, contextual adequacy, lexical-semantic errors, ChatGPT translation, Google Translate evaluation.
References
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