Research on adaptive model of English translation based on data fusion

Abstract

This research is based on the attention mechanism English translation adaptive model. After analyzing the key factors that affect English language translation, the attention mechanism is used to extract the detailed features of such factors in each region to form a feature sample set, and the feature sample set is fused and normalized, so as to obtain a brand-new feature sample set. Input to build an English language translation model and output the translation results, According to the results, the overall translation effect of the model is predicted. The results show that the prediction model of this method has high prediction accuracy in training and testing.