Advancing Grammatical Error Correction for English Text: A Comparative Study of Bahdanau Attention and Multi-Head Attention Models

English Grammatical Error Correction (GEC) is a vital aspect of Natural Language Processing (NLP), aimed at enhancing text accuracy and readability by resolving grammatical errors. This study examines the performance of two models: the Multi-Head Attention-Based Seq2Seq model and the Bahdanau Attention model, tested on multiple datasets. While the Multi-Head Attention model achieves strong results, the Bahdanau Attention model outperforms it, achieving a higher accuracy of 99.72%. The Bahdanau model's ability to capture contextual nuances more effectively contributes to its superior performance in correcting grammatical mistakes. These findings position the Bahdanau model as a state-of-the-art approach in GEC, demonstrating its advanced capability to handle complex linguistic structures. The research underscores the potential for the refinement of grammatical correction techniques through innovative model architectures.