Personalized Grammar Refinement Using Meta-Reinforcement Learning and Transformer-Based Framework

Writing competence is an essential academic and professional proficiency, and grammatical precision and reliability a long-term issue, especially among ESL students. Conventional rule-based and statistical grammar correction models have constraints based on context, whereas contemporary Transformer-based sequence-to-sequence models like BERT, T5, and GPT have strong performance but cannot be customized or adapted to specific writer styles. To fill in these gaps, this study introduces Meta-ACGR, a meta-reinforcement learning grammar refinement system that augments Transformer-based seq2seq models with Proximal Policy Optimization (PPO) and Model-Agnostic Meta-Learning (MAML) and curriculum learning. The model promotes individualized grammar correction, which allows quick adjustment to the new learners in ESL by using meta-learning and guided error development. Meta-ACGR is written in Python with the help of PyTorch and trained on big datasets of ESL language, like NUCLE and Lang-8, which can be refined based on context and individual learners. Empirical evidence indicates that Meta-ACGR receives better grammatical accuracy (86.2 vs. 94.0 per cent), decreases inference latency (12 per cent vs. baseline Transformer models), and performs better on personalization (15 per cent vs. baseline Transformer models). Altogether, Meta-ACGR provides a scalable, adaptable, and customized grammar check system with good chances to be implemented in real-life to improve writing in ESL.