Analysis of deep classification grammar error correction algorithm for online English grammar teaching

Abstract To solve the insufficient educational resources in offline classroom English teaching, the research focuses on online teaching and designs an online grammar correction algorithm to help students realize automatic online error correction. The algorithm is based on the Transformer model algorithm based on multi-head attention mechanism (MHAM), and integrates word order information into the encoding process. Three pseudo-parallel corpora are used to expand the number of training data. Finally, Adam is used to optimize the model parameters to improve the model performance. The accuracy, recall, and F 0.5 of the algorithm formed after two one-way optimization are the highest values in the same type of optimization model. The SP + Pre human + TF copy algorithm formed after double optimization has the best comprehensive performance. The accuracy rate reached 68.53 %, and the F 0.5 value reached 58.26 %, both of which were the highest values in the comparison model. Moreover, this method can also provide certain technical support for the establishment of multilingual interaction platforms and high-quality natural language generation.