Utilizing Deep Learning Models for Enhanced Error Detection and Correction in English Writing Tasks

English writing tasks pose significant challenges for learners, especially when it comes to error detection and correction. In this research, we explore the potential of deep learning models to enhance error detection and correction mechanisms in English writing, aiming to improve both the accuracy and efficiency of these processes. Specifically, we employ a hybrid BERT-LSTM architecture, leveraging the language understanding capabilities of Bidirectional Encoder Representations from Transformers (BERT) and the sequential modelling strength of Long Short-Term Memory (LSTM) networks. BERT, with its contextual embeddings, captures complex linguistic patterns, while LSTM processes these representations for improved prediction of grammatical, syntactical, and stylistic errors. The model is trained on large-scale datasets containing varied error types in English writing, enabling it to identify a broad spectrum of mistakes, including subject-verb agreement, article usage, punctuation, and contextual word choices. Our methodology incorporates transfer learning from BERT’s pre-trained model, fine-tuning it to the specific domain of error detection. The integration with LSTM allows the model to not only detect errors but also suggest contextually appropriate corrections, making it a comprehensive tool for writing improvement. Preliminary results show that the BERT-LSTM hybrid outperforms traditional grammar checkers and standalone models in terms of accuracy and error type coverage. The proposed system offers significant potential for applications in educational technologies, providing personalized feedback to language learners and aiding in the development of more proficient English writing skills. This research demonstrates the efficacy of advanced deep learning models in transforming English language writing tasks, offering insights for future improvements in natural language processing and educational tools.