A method for improving the accuracy of English professional term translation assisted by deep learning

Accurate translation of English medical terminology is crucial in healthcare, where errors can lead to significant semantic distortion. Traditional rule-based and statistical machine translation methods often fail to handle domain-specific language due to limited contextual understanding. This paper proposes BBNMTPET (BioBERT-Neural Machine Translation Professional English Term), a deep learning framework that integrates BioBidirectional Encoder Representations from Transformers with NMT to improve medical term translation accuracy. The BBNMTPET framework aims to utilize a BioBERT-enhanced NMT model to effectively capture the contextual semantic features of medical language. To ensure consistent bilingual term mapping, an attention-guided terminology alignment mechanism is incorporated. Training utilizes adaptive learning rate scheduling, combined with domain-sensitive loss functions, to prioritize accurate medical term translation while enhancing convergence and generalization. The model is trained and evaluated on the Kaggle Accurate Medical Translation Data, a large-scale bilingual medical corpus. Experiments demonstrate that BBNMTPET achieves a 7.6% improvement in BLEU scores and a 9.2% increase in medical term translation accuracy compared to baseline NMT models, significantly reducing the mistranslation of rare and ambiguous terms. The integration of domain-specific BERT, terminology alignment, and optimized training strategies significantly advances the quality of professional medical term translation.