Morphological Generators in Computer Linguodidactics: Analysis of Parallel Corpus Applications for Turkic Languages

This paper examines the application of parallel corpora in computer linguodidactics and addresses critical issues related to morphological generators in machine translation systems. The research analyzes machine translation systems developed on the Apertium platform, focusing on seven core modules: morphological analyzer, morphological disambiguator, lexical transfer, lexical selection, structural transfer, morphological generator, and post-generator. The study compares rule-based and statistical approaches while exploring their hybrid applications for enhanced translation quality. Machine translation systems for Turkic languages including Uzbek, Kazakh, Tatar, and Turkmen are comparatively analyzed to demonstrate the effectiveness of morphological generators in linguodidactic applications. Results indicate that morphological generators play a fundamental role in creating effective language learning tools based on parallel corpora, particularly for agglutinative languages. The findings contribute to the development of computer-assisted language learning systems and provide insights for future linguodidactic platform development. This research demonstrates the practical significance of integrating advanced morphological processing techniques with parallel corpus tools to enhance machine translation quality and educational effectiveness in computer linguodidactics.

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