Evaluating Deep Learning Approaches for Grammatical Error Correction: A Comparative Study of Seq2Seq Models

Grammatical error correction is essential in natural language processing for improving text clarity and quality by addressing grammatical mistakes. This paper assesses the performance of two models, the Simple Encoder-Decoder model and the Multi-Head Attention-Based Seq2Seq model on various datasets. Among these, the Multi-Head Attention-Based Seq2Seq model stands out with its superior performance, achieving an accuracy of 97.50%. This model leverages a multi-head attention mechanism to effectively capture contextual dependencies, resulting in more precise grammatical corrections. The findings highlight the model’s advanced capabilities and offer recommendations for enhancing grammatical error correction methodologies, including potential architectural improvements and expanded datasets.