English Long and Short Sentence Translation and Recognition Method Based on Deep GLR Model

Abstract

The translation recognition of English long and short sentence information is an important issue to obtain the focus and core of English articles. Based on the deep GLR model, this paper constructs a method framework for English long and short sentence translation and recognition, using different embedding layer parameter initialization methods and using multi-layer computing methods in the sentence decoder. The initial corpus text is segmented and tagged with part-of-speech, then, the part-of-speech tag is appropriately corrected to reduce ambiguity, and then it is manually syntactically tagged. In the simulation process, the English long and short sentence summary and translation components are designed and developed, which can accurately and efficiently obtain the key information of English long and short sentences. The experimental results show that the English long and short sentence translation and recognition method of the deep GLR model improves the accuracy of the model parameters. In terms of model structure, the deep GLR value can be improved by 70.77% by reproducing the multi-layer representation fusion of semantic translation; in terms of data enhancement, the deep GLR value can be increased by 70.35% by means of “back translation,” and the improved model is effective. It promotes the translation and recognition generalization ability of English long and short sentences.