A study of Grammar Analysis in English Teaching With Deep Learning Algorithm

• Main Author: Gang Zhang
• Format: Article
• Language: English
• Published: Kassel University Press 2020-09-01
• Series: International Journal of Emerging Technologies in Learning (iJET)
• Subjects: deep learning, english teaching, seq2seq attention mechanism, recall rate
• Online Access: https://online-journals.org/index.php/i-jet/article/view/15425

In English teaching, grammar is a very important part. Based on the seq2seq model, a grammar analysis method combining the attention mechanism, word embedding and CNN seq2seq was designed using the deep learning algorithm, then the algorithm training was completed on NUCLE, and it was tested on CoNIL-2014. The experimental results showed that of seq2seq+attention improved 33.43% compared to the basic seq2seq; in the comparison between the method proposed in this study and CAMB, the P value of the former was 59.33% larger than that of CAMB, the R value was 8.9% larger, and the value of was 42.91% larger. Finally, in the analysis of the actual students' grammar homework, the proposed method also showed a good performance. The experimental results show that the method designed in this study is effective in grammar analysis and can be applied and popularized in actual English teaching.