Research on Optimized BERT Model for Sentence Generation Algorithm in English-Chinese Translation

In recent years, neural machine translation methods based on pre-trained language models have made significant progress in multilingual tasks. However, in English-Chinese translation, especially in the generation of long and complex sentences, it still faces challenges such as semantic drift and syntactic misalignment. To address this problem, this paper proposes an optimized BERT model that integrates a semantic alignment mechanism and a syntax-aware decoding strategy to improve the accuracy and structural naturalness of sentence generation in English-Chinese translation tasks. The model introduces a semantic fusion layer based on the standard BERT structure, enhances the source-target alignment capability through contextual semantic matching, and introduces a structural prior guidance mechanism at the decoding end to explicitly control the syntactic structure of the translation. The experimental part is trained and evaluated based on a parallel corpus constructed from college English writing courses, and the performance is compared with mainstream translation models. The results show that the proposed method performs well in traditional indicators such as BLEU and METEOR, and achieves significant improvements in the syntactic diversity index (SVI), effectively improving the naturalness and professional expression accuracy of the generated text. This work not only verifies the feasibility of structure-enhanced BERT in complex translation tasks, but also provides theoretical basis and practical support for the application of intelligent translation systems in academic scenarios.