Towards Fluent Expression: The Impact of Deep Learning in Grammar Correction

Grammatical Error Correction is a crucial task in Natural Language Processing. This paper discusses the evolution of grammar error correction models from early rule-based approaches to advanced deep learning-based methodologies. This work utilizes the NAIST Lang-8 dataset, a valuable dataset offering parallel data, facilitating more straightforward evaluation and eliminating the need for additional data creation. This research explores Deep Learning frameworks like encoder-decoders, attention mechanisms, and new-age transformer models. The performance of each model is then measured with the help of a standard metric, the GLEU score. After extensive experimentation, it has been observed that the T5 model has outperformed other models by a significant margin with a GLEU Score of 0.855. The outstanding performance is primarily due to its pre-trained nature and fine-tuning parameters. This research highlights deep learning models’ pivotal role in advancing grammar error correction.