Deep Learning-based Fuzzy Translation Problem in Chinese-English Epidemic News Reporting

Abstract

Abstract To smoothly realize the information conversion from the original language information to the target language, this paper constructs a deep learning-based fuzzy translation model for news reports so that the translated text can faithfully convey the meaning of the original language text information and achieve natural semantic equivalence. A neural probabilistic language model is used to construct objective functions in speech recognition and lexical annotation so that the translated text can provide a more appropriate linguistic representation of the polysemantic words in the original language text according to the differences in contextual morphology. A deep learning occurrence mechanism model is constructed through fuzzy semantic reasoning and fuzzy translation logic, and learning state indicators such as emotional interaction are designed to evaluate the occurrence status of fuzzy translation accurately. The simulation results show that the natural language processing (GLUE) test score of the deep learning-based fuzzy translation model for news reports is 89.8, 9.2, and 6.9 points higher than 80.6 and 82.9 for the other two models, respectively. The average error discrimination ability of the model designed in this paper is 93.57, and the average training set, development set, and test set values are 98.425, 10.16, and 45.95, respectively. Thus, it can be seen that the deep learning-based fuzzy translation model for news reports can more naturally and accurately respond to the dynamic changes in language, which promotes the rapid development of translation theory and practice.