Bridging Large Language Models and Sequential Learning: A Responsible Hybrid BERT-RNN Approach for Grammar-Aware Text Correction

Enhancing the quality of English writing through automated error correction remains an open challenge, as traditional rule-based methods fail to effectively model language’s statistical properties. Although recent deep learning advancements have shown promise, existing techniques such as sequential RNNs and large pre-trained models exhibit limitations in both error detection and interpretable correction. This paper introduces an innovative hybrid deep learning approach that combines BERT with an improved RNN to enhance English error-handling performance. The core methodology adopts a two-stage pipeline that leverages the inherent strengths of each model. In the first stage, the input text undergoes preprocessing and formatting to prepare it for BERT integration. BERT, which relies on pre-trained knowledge from large-scale datasets, implicitly learns contextual relationships within text. This allows it to capture human-like language understanding and identify unconvincing phrases that contain grammatical or semantic errors. Specifically, its multi-headed self-attention mechanism captures both inter- and intra-sentence dependencies to evaluate the contextual appropriateness of word usage. BERT identifies potential error spans without relying on manually encoded rules. In the second stage, the detected spans are precisely corrected using an improved RNN optimized through an evolutionary algorithm to balance correction accuracy and fluency. The enhanced RNN, structured as an attentional encoder–decoder LSTM network, focuses on the most relevant input fragments when generating output. Its copying mechanism enables the reuse of original words that convey nuanced meanings. Comprehensive experiments on established datasets demonstrate up to 96% accuracy, achieving state-of-the-art improvements in precision, recall, and F1-score compared to previous approaches. Detailed ablation studies further quantify individual model contributions and highlight the advantages of the proposed unified framework for language processing.