Optimization of English Machine Translation Model Based on Neural Network

Machine translation (MT) is an advanced technology that automatically converts the source language into the target language through the use of computers. As communication between countries around the world becomes increasingly close, the need for mutual translation between languages is becoming increasingly evident. In view of the low accuracy and poor vividness of the MT model, this paper uses neural network (NN) to optimize the MT model. The accuracy and vividness of MT models can be improved by using NN. Through the optimization of English MT model based on NN, the accuracy rate has increased from 87.3% to 96%, a full 8.7%. The accuracy of the traditional MT model has increased from 87.5% to 88.1%, an increase of only 0.6%. The experimental results show that the accuracy of MT can be effectively improved by using NN to optimize the translation model.