Deep Reinforcement Learning Approach Based Grammatical Error Correction

Abstract

Abstract Natural Language Processing (NLP) is a field of study that focuses on the interaction between computers and human language. It encompasses various techniques and methodologies aimed at enabling machines to understand, interpret, and generate human language text or speech. NLP plays a crucial role in many applications, including machine translation, sentiment analysis, information retrieval, and Grammatical Error Correction (GEC). Grammatical Error Correction (GEC) is an important task in natural language processing that aims to automatically correct errors in written text. It involves detecting and correcting errors related to grammar, syntax, spelling, punctuation, and other linguistic aspects. However, existing study is solely based on classical machine learning and deep learning methods for GEC. This study proposes a new approach using a DQN model and leverages the C4_200M dataset to automate the GEC process. The main goal of this study is to optimize the selection of action-value function (Q-function) and train a DRL model for automatic grammatical error correction and set baseline results using reinforcement learning (RL) techniques. Findings show that the proposed DQN model outperformed machine learning and rule‑based techniques.