Cross-Lingual Pre-Trained Models for Chinese-English QA Systems

The growing need for productive second language (L2) writing has created demand for automated tools that deliver accurate and context-sensitive grammatical error correction. The conventional rule-based systems often fail at dealing with the nuanced errors made by L2 learners, creating a need for more sophisticated solutions using natural language processing (NLP). This research presents an adaptive error correction system for L2 writing based on fine-tuned BERT models to improve error detection and error correction while adding value with pedagogical components and insight. The paper provides a modular architecture that offers a combination of BERT's context understanding with specific error pattern detection that offers real-time feedback based on student proficiency. The system is evaluated with quantitative experimentation to compare the performance of the inبرلיר learning tool with existing systems, as well as usability studies with L2 learning students and teachers. The findings show a 15% improvement in error correction than traditional means and positive comments regarding its ability to teach. The contribution of this research offers a scalable and accessible response to improve student engagement with L2 learning, and the references to automated writing systems and personalized learning experiences may have broader impact across education.