Improvement of English-Chinese bilingual learning by integrating semantic analysis and neural machine translation.

ABSTRACT: To improve the effectiveness of English-Chinese bilingual learning, this study introduces an optimization model that combines semantic analysis with neural machine translation (NMT). A series of comprehensive experiments were conducted to evaluate its performance. The results demonstrated that the proposed model outperformed existing approaches across multiple key metrics, including reading comprehension, translation accuracy, and learning efficiency. Specifically, the optimized model achieved a reading comprehension score of 85.0, a grammar accuracy rate of 82.5%, and a vocabulary score of 80.1. For translation performance, it attained a sentence-level accuracy of 84.3%, paragraph coherence of 81.5%, and a cultural understanding score of 78.9. In terms of learning efficiency, the model re

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC12301469 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature