Convolutional Neural Network Model for Syntax Error Detection in Language Learning Environments

Existing models have difficulties in identifying complicated grammatical patterns and generalizing to various types of learner inputs, which is a significant obstacle for the improvement of computer-assisted language learning. Automatic syntax mistake detection is essential for this improvement. This research presents GED-HCA, which stands for "Grammatical Error Detection with Hierarchical Convolutional Attention"a novel CNN-based model that combines multiple change filters with attention mechanisms. To capture local and universal syntax, the model processes the input text through parallel writing and word-state embeddings. By using shallow convolutions, the model can capture localized syntax, and by utilizing deep convolutional branching, it can generalize across more extensive language forms. Writing patterns and word-state data are both captured simultaneously by the GED-HCA algorithm, which may assist it in identifying regions of student-generated text that are prone to include mistakes. The GED-HCA gets an F1 score of 87.3% by preserving its efficiency for real-time feedback. This score is higher than the baselines based on traditional CRF, BiLSTM, and Transformer on the FCE dataset by a margin of up to 4.1% compared to the baselines. These findings suggest that it is suitable for use with AI-driven grammar learning systems that prioritize precision and clarity.