Optimized English Translation System Using Multi-Level Semantic Extraction and Text Matching

The domain of machine text translation and matching is undergoing substantial transformations amidst the perpetual evolution of deep learning methodologies. By amalgamating the contemporary realm of generative models and networks with the multi-faceted attentiveness of multiple heads, there has been a pronounced enhancement in the efficacy of existing text translation and matching endeavors. Consequently, this manuscript endeavors to elucidate the intricacies of the text-matching conundrum within the ambit of English translation. It posits a novel MA-Transformer text-matching framework that seamlessly integrates multi-tiered semantic feature extraction methodologies to actualize the text-matching task in the English translation process. The framework initiates its journey by employing Continuous Bag of Words (CBOW) for word vector embedding, thereby accomplishing the generation and embedding of word vectors. Subsequently, it expeditiously conducts the multilevel amalgamation of data features through the expeditious execution of the multi-head Transformer model. Following the culmination of feature fusion, a judicious sequence of data downgrading and feature screening ensues, ultimately culminating in the attainment of high-precision text matching. The experimental results show that the constructed MA Transformer model performs well in public and actual data testing, with an average precision of 0.867 and 0.722, respectively, on the two types of datasets. The accuracy of the text-matching is higher than that of the current common method frameworks, which provide technical support and references for the future construction of English translation systems.