Advancing Grammar Correction in ESL Writing Through Deep Learning Techniques: A Comprehensive Approach

This study focuses on improving automated grammar correction in ESL (English as a Second Language) writing by combining BERT and Generative Adversarial Networks (GANs). Traditional grammar correction models often struggle with complex constructs, limiting their effectiveness for non-native learners. To address this, a hybrid approach is proposed, where fine-tuned BERT model identifies and corrects grammatical errors using contextual embeddings, while the GAN generates diverse, grammatically correct variations of sentences through adversarial training. The system was trained and evaluated on grammar correction datasets, demonstrating significant improvements over baseline models in correcting errors such as subject-verb agreement, tense consistency, and sentence structure. Evaluation metrics, including accuracy, BLEU score, and F1 score, confirmed the model's robustness and its ability to generate contextually appropriate corrections. This hybrid model not only provides error-free sentences but also offers meaningful alternatives to enhance learners' understanding of grammar. This study underscores the importance of leveraging advanced deep learning techniques to create effective tools that empower ESL learners with accurate grammar correction and meaningful language feedback.