SEMANTIC INTERPRETATION OF PHRASEOLOGICAL UNITS IN ARTIFICIAL INTELLIGENCE–BASED TRANSLATION SYSTEMS: CHALLENGES AND ENHANCEMENT STRATEGIES

The rapid development of artificial intelligence–based machine translation systems has significantly improved cross-linguistic communication; however, linguistic errors remain a persistent challenge, particularly for morphologically rich and low-resource languages such as Uzbek. This study examines the theoretical foundations of identifying and correcting linguistic errors in neural machine translation (NMT) systems, with specific attention to morphological, lexical, semantic, syntactic, stylistic, and idiomatic inaccuracies. The agglutinative structure of Uzbek, combined with its rich phraseological inventory and flexible syntax, increases the likelihood of structural misinterpretation and semantic distortion in AI-generated translations.

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