DEVELOPMENT OF HYBRID AND GENERATIVE LEARNING MODEL FOR GENERATION OF CROSS-LINGUAL WORD VECTORS FOR LOW RESOURCED LANGUAGES FOR AN EFFICIENT EDUCATIONAL SYSTEM

Abstract

Low-resourced languages face significant barriers in accessing modern educational technologies due to limited digital content and inadequate natural language processing tools. This research addresses these challenges by developing a hybrid and generative learning model for generating cross-lingual word vectors that bridge high-resource and low-resource languages in educational contexts. We investigate how combining traditional alignment-based methods with generative adversarial approaches can create robust multilingual embeddings despite training data scarcity. The study examines multiple low-resource languages including regional Indian languages, African languages, and Southeast Asian languages, analyzing how cross-lingual vectors enable knowledge transfer from resource-rich languages like English to underserved linguistic communities. Our hybrid model integrates supervised alignment techniques with unsupervised generative learning, creating word embeddings that preserve semantic relationships across language boundaries. Evaluation demonstrates that the proposed approach achieves significant improvements in cross-lingual similarity tasks, machine translation quality, and educational content adaptation compared to baseline methods. The research contributes both methodological innovations in multilingual representation learning and practical frameworks for deploying these technologies in educational systems serving linguistically diverse populations. Findings indicate that hybrid generative models can effectively democratize educational access by enabling automatic content translation, personalized learning in native languages, and cross-lingual knowledge retrieval.