Intelligent Test Algorithm for English Writing Using English Semantic and Neural Networks

Abstract

English writing is considered by English learners to be the portion of English learning with the greatest application, the most thorough understanding, and the most challenging instruction. It automatically detects and corrects (DAC) grammatical faults in English writing, which is critical in the English learning and teaching processes. The goal of this research is to investigate the sequence annotation model and the Seq2Seq NN model based on cyclic NN, and to use these two models to detect grammatical faults in English (EGE). This paper provides an EGE DAC approach based on sequence annotation with the aid of the sequence annotation model developed in this paper. Simultaneously, this work presents an EGE DAC approach based on Seq2Seq that integrates the sequence annotation model. The model is no longer trained on a single form of grammatical error, but rather on all types of errors combined, allowing it to respond to any EGE. This work considers the DAC of grammatical errors with fixed confusion sets, such as prepositions and articles. This model's F1 value for article error correction is 38.05 percent, which is 33.40 percent higher than the F1 value for UIUC article error correction. The F1 value for preposition error correction is 28.89 percent, which is 7.22 percent higher than the F1 value for UIUC preposition error correction.