Design and Application of Chinese English Machine Translation Model Based on Improved Bidirectional Neural Network Fusion Attention Mechanism

Abstract

In order to improve the effect of Chinese-English machine translation, this paper combines attention mechanism and neural network algorithm and applies it to Chinese-English machine learning translation. Moreover, this paper uses Gaussian distribution instead of chi-square distribution to analyze the approximate error introduced by the Chinese and English speech energy detection method. In addition, this paper studies the overall and specific approximation errors by establishing the normalized mean square error function and the absolute error function, respectively. Finally, this paper proposes a new model for machine translation based on logarithmic position representation and self-attention mechanism. Through the experimental research, it can be seen that the Chinese-English machine translation model integrating attention mechanism and bidirectional neural network proposed in this paper has a good practical translation effect.