Research on Chinese Machine Reading Comprehension Model Based on Enhanced Semantic Information Capture

Machine reading comprehension (MRC) is a fundamental task in natural language processing (NLP), with existing models struggling to capture long-range dependencies and handle complex semantic nuances, particularly in Chinese. This paper proposes the Collaborative Semantic Reader (C-S Reader), a novel model that combines RoBERTa_wwm_ext pre-training and multi-level attention mechanisms to enhance semantic understanding. Experiments on the DuReader2 dataset show that C-S Reader significantly outperforms baseline models in both the Rouge-L and BLEU-4 scores, demonstrating its effectiveness in processing long documents and capturing complex semantic relationships. Our work provides a scalable solution for Chinese MRC tasks and highlights future challenges, including long-range dependency modeling and ambiguity in complex questions.