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


Yanfeng Wang, Ning Ma, Journal of Information Processing Systems Vol. 22, No. 1, pp. 21-33, Feb. 2026  

https://doi.org/10.3745/JIPS.02.0234
Keywords: Attention Mechanism, BERT, DuReader2, Machine Reading Comprehension
Fulltext:

Abstract

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.


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Cite this article
[APA Style]
Wang, Y. & Ma, N. (2026). Research on Chinese Machine Reading Comprehension Model Based on Enhanced Semantic Information Capture. Journal of Information Processing Systems, 22(1), 21-33. DOI: 10.3745/JIPS.02.0234.

[IEEE Style]
Y. Wang and N. Ma, "Research on Chinese Machine Reading Comprehension Model Based on Enhanced Semantic Information Capture," Journal of Information Processing Systems, vol. 22, no. 1, pp. 21-33, 2026. DOI: 10.3745/JIPS.02.0234.

[ACM Style]
Yanfeng Wang and Ning Ma. 2026. Research on Chinese Machine Reading Comprehension Model Based on Enhanced Semantic Information Capture. Journal of Information Processing Systems, 22, 1, (2026), 21-33. DOI: 10.3745/JIPS.02.0234.