Improving English Writing Skills Through NLP-Driven Error Detection and Correction Systems

Abstract

Error detection and correction is an important activity that ensures the quality of written communication, especially in education, business, and legal documentation. State-of-the-art NLP approaches have several issues, including overcorrection, poor handling of multilingual texts, and poor adaptability to domain-specific errors. Traditional methods, based on rule-based approaches or single-task models, fail to capture the complexity of real-world applications, especially in code-switched (multilingual) contexts and resource-scarce languages. To overcome these limitations, this research proposes an advanced error detection and correction framework based on transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT). The hybrid approach integrates a Seq2Seq architecture with attention mechanisms and error-specific layers for handling grammatical and spelling errors. Synthetic data augmentation techniques, including back-translation, improve the system's robustness across diverse languages and domains. The architecture attains maximum accuracy of 99%, surpassing the state-of-the-art models, in this case, GPT-3 fine-tuned for grammatical error correction at 98%. It demonstrates superior performance in various multilingual and domain-specific settings, in addition to complex spelling challenges such as homophones and visually similar words. The system was realized using Python with TensorFlow and PyTorch. The system applies C4-200M for training and evaluation. The precision and recall rates, with real-time processing of text, render the model highly useful for practice applications in the areas of education, content development, and platforms for communication. This research fills a gap in present systems and hence contributes to an enhancement of automated improvement of writing skills in the English language, with a sound and scalable solution.