An improved LSTM model for correcting grammatical errors in English text

Abstract

In English teaching, grammar teaching is still an indispensable part. Teachers pay more attention to the correction of written form than oral form in grammar teaching so that students can see their mistakes intuitively and clearly. Whether from the perspective of teachers or students, the correction of grammatical errors and good learning results are inseparable. At present, the public grammar error correction corpus is too small and the quality is uneven, which makes the parameters of the grammar error correction model can not be fully trained. The performance of the model is also a bottleneck. Graph neural network-based model for grammar error detection is studied. The errors in the text data are detected. Neural network modeling is adopted as the basic structure of the model. In addition to predicting the correct label of each word in the sentence, an auxiliary task of predicting the context word of the word is introduced to further improve the detection performance of the model. Furthermore, the graph neural network with a gating mechanism is adopted to model the dependency syntax tree of the statement, which provides important information features for error detection and effectively improves the performance of model checking. Finally, good results in English grammar error detection and test data sets are achieved. As one of the core technologies in online education and other fields, the research of grammar error correction has great research and application value.