An Assisted Teaching Method of College English Translation Using Generative Adversarial Network

Abstract

College English translation instruction is an important part of developing students' English application skills. The generation network in GAN (generative adversarial network) is combined with reinforcement learning technology in this paper to create a basic text generation model that solves the problem that the original GAN model cannot handle discrete data. The correctness of students' English translation ability is analyzed using a neural network model trained by PSO (particle swarm optimization), which can help teachers estimate students' translation ability and provide a reference for the next teaching. The results show that the proposed model's accuracy rate is clearly higher than the comparison model's, with a maximum accuracy rate of over 85%. The findings indicate that this research model has the potential to improve the quality of English translation instruction.