Research on Syntactic Optimization and Semantic Reconstruction Strategies for English Translation Based on Machine Learning

The development of globalization has contributed to the increasing demand for cross-language communication, and machine translation, as an effective language conversion tool, has improved the quality and efficiency of English translation.The article discusses the syntactic optimization and semantic reconstruction strategies for English translation based on machine learning.The machine translation model of English syntax optimization and semantic reconstruction based on EM algorithm is constructed by using key technologies such as EM algorithm and multi-head attention mechanism.The model adopts a joint learning method, combining the Transformer model with the EM algorithm.The dependency between any two words in the input sequence is captured using the multi-head attention mechanism, and the new translation corpus is generated by multi-task joint training algorithm.The training phase of this paper's model has good translation effect, and the model of this paper gets the highest BLEU score of 32.86 when the number of multi-head attention layers is 1.The distribution of semantic features of translation reconstruction under this paper's method is basically consistent with the simulation results, and the error elimination rate of semantic reconstruction is 99.64% when the number of samples is 500.The method in this paper is more effective in syntactic structure optimization, with the highest BLEU scores on "Chinese to English" and "English to Chinese", and the syntactic correctness rate on English long sentences of different topics reaches 88.69%~96.57%.