A Study on Enhancing Semantic Accuracy in English Translation Teaching Using Convolutional Neural Networks

Semantic accuracy plays an important role in improving the quality of English translation teaching. This paper proposes a semantic translation model based on convolutional neural network. It is based on the semantic correlation expression and the statistical machine translation model of hierarchical phrases, and combines the convolutional neural network to propose a translation model optimization method that integrates sentence and document information. The method evaluates the semantic match between source language phrases and candidate target phrases by utilizing the sentence context of the source language phrases and the topic information of the documents in which they are located. The optimization method for evaluating the accuracy of English semantic translation is also given. In the simulated translation experiments, the accuracy of the translation correctness evaluation of this method is maintained at 92.5% and above, with high semantic accuracy. The research constructs a high and stable English semantic translation model, which provides informative aids for English translation teaching.