Grammar Error Correction using Monotonic Attention

Grammar error correction is an essential task in natural language processing (NLP), aimed at identifying and correcting errors in written texts. While various approaches have been proposed to address this problem, most of them rely on rule-based or statistical methods such as bayesian Networks, attention mechanism, monotonic attention and much more that have limitations in dealing with the complexity and ambiguity of natural language.Previously, the correction of errors mostly used attention mechanism, which assigns weights to the input sequence based on their relevance to the current output token. The assigned weights are then utilized to calculate a sum of the input tokens, taking into account the weight of each token, generating a context vector that informs the next step of the output generation process. This mechanism has been used successfully in many NLP tasks and grammar error correction.Here, we worked on monotonic attention in a model for grammar error correction, the model can improve its allignment accuracy, training efficiency, and overall performance. This can lead to more accurate and efficient error correction, which is essential for improving language proficiency and streamlining the writing process. The goal is to improve the performance of the model by reducing the amount of redundant computation and increasing the interpretability of the attention weights.