Optimization of English Machine Translation based on Bidirectional Encoder Representations from Transformers-Integrated Neural Architectures

The quick pace of artificial intelligence (AI) growth has accelerated English machine translation (MT) enormously, but classical systems have inherent problems such as narrow understanding of context, inefficient processing of unclear phrases, and weak semantic representation. The research in this work focuses on optimizing English machine translation using a hybrid approach through Neural Machine Translation (NMT) and Bidirectional Encoder Representations from Transformers (BERT). Conventional MT models, such as rule-based and statistical models, tend to overlook contextual dependencies and long-distance semantics. Even typical NMT models are poor in handling out-of-vocabulary tokens and syntactic subtlety. To resolve these shortcomings, the NMT+BERT architecture incorporates the strength of contextual representation of BERT into the translation process, enhancing semantic understanding and fluency accordingly. Experiments on benchmarking datasets demonstrate that the proposed model performs better with accuracy and F1-score of 94.6% and 92.3%, respectively, compared to conventional NMT models that had accuracy and F1-score of 88.1% and 85.7%, respectively. Comparative testing verifies the strength and efficacy of the model in generating context-sensitive, grammatically precise translations.