ENHANCING UZBEK-RUSSIAN TRANSLATION THROUGH PARALLEL CORPUS UTILIZATION
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Abstract
This study examines the impact of integrating Uzbek-Russian parallel corpora into machine translation (MT) systems. Parallel corpora, comprising collections of texts in one language aligned with their translations in another, serve as foundational datasets for training and refining MT algorithms. The research focuses on addressing lexical ambiguities and syntactic divergences between Uzbek and Russian. Findings indicate that the utilization of high-quality parallel corpora significantly enhances translation accuracy and fluency, as evidenced by increased BLEU scores. Methodological approaches to corpus construction, their role in translation studies, and prospects for future research in computational linguistics are discussed.
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