Study on the Flipped Classroom Teaching Model of “Business English Translation” in the Context of Big Data

Abstract

Abstract As a new teaching method born in the changing times, the flipped classroom also provides a new way to reform the teaching mode of business English translation. In this paper, we propose a new neural machine translation model--TwinGAN model, which includes two generators and two discriminators and combines “similarity selection” and “strategy gradient” of reinforcement learning and adversarial learning strategy of the generative adversarial network to improve the translation quality of the model. The model combines “similarity selection”, “policy gradient,” reinforcement learning, and the adversarial learning strategy of the generative adversarial network to improve the translation quality of the model while mitigating the exposure bias problem to improve the translation performance of the model further. Finally, the learning-assisted translation system based on the TwinGAN model is combined with the deep learning-based flipped classroom teaching model and applied to business English translation teaching. In the eight-week teaching test, 38.16% of the students in the experimental class achieved excellent learning levels in the first week of the test, and 57.47% in the eighth week of the test, an increase of 18.55% compared with the first week. In contrast, 36.48% of the students in the control class reached an excellent level in the first week of testing, and 35.73% in the control class reached an excellent level in the eighth week. The flipped classroom model based on deep learning is effective in teaching effectiveness.