ENHANCING UZBEK-ENGLISH NEURAL MACHINE TRANSLATION WITH DOMAIN-SPECIFIC BERT PRETRAINING

Аннотация

This article investigates the enhancement of Uzbek-English neural machine translation (NMT) by leveraging domain-specific BERT pretraining. Due to the low-resource nature and morphological complexity of Uzbek, standard NMT models often struggle with domain-specific terminology and contextual nuances. By pretraining BERT models on monolingual corpora tailored to general, medical, and legal domains, and integrating them into a transformer-based NMT framework, the study achieves significant improvements in translation quality. Results demonstrate that domain-specific pretraining notably outperforms general pretraining and baseline models, highlighting its effectiveness for specialized translations in low-resource language pairs.