Semantic Analysis Technology of English Translation Based on Deep Neural Network

Abstract

English translation plays an important role in the development of science and technology and cultural exchanges. With the increase in translation volume, intelligent translation has become inevitable, but there is no effective solution for semantic translation in English translation. To provide an effective translation improvement scheme in English translation, this paper studies and analyzes the application of deep neural network in English translation semantic analysis. Based on a brief analysis of the research progress of English translation analysis and the current situation of neural network, a neural network translation architecture is established, and a deep neural network model for English translation analysis is proposed. Aiming at the problem of gradient disappearance in RNN model, the ability of Gru neural network to deal with long-distance translation is enhanced, and the computational complexity of Gru neural network is reduced. At the same time, a bi-directional Gru model is designed to translate according to the context. For some nonlinear translation, a deep neural network model based on part of speech sequence information is proposed to realize semantic analysis, and experiments are designed to test the translation effect of the neural network model. The simulation results show that the English translation based on deep neural network can improve the translation effect, reduce errors and improve the accuracy of semantic analysis of English translation, which has certain reference significance for improving the level of English translation.