Optimization method for academic English content based on generative adversarial networks and data augmentation

With the globalization of academic exchanges, the importance of academic English writing quality has become increasingly prominent. Especially for non-native speakers, grammar and language quality in academic English writing significantly affect the readability and academic value of articles. Therefore, this study proposes an academic English content optimization method based on generative adversarial networks and data augmentation. The method uses Transformer as the generator, combines generative adversarial networks with data augmentation techniques to generate high-quality pseudo error correction sentence pairs, and optimizes model performance through policy gradient methods. Although academic English is used as the application context in this study, the architecture can be adapted to other English writing genres given appropriate training corpora. From the results, when the iteration reached 500, the precision was 0.98 and the recall was 0.10. The accuracy-2, F1 score, mean absolute error, correlation coefficient index, and accuracy-7 values of the proposed academic English content optimization model were 87.8, 89.2, 0.05, 0.69, and 97.6. The proposed model has higher accuracy and efficiency on multiple datasets, which can effectively optimize various types of English grammar errors, providing new solutions for content optimization in academic English writing.