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Retrieving Chinese Questions and Answers Based on Deep-Learning Algorithm

Author

Listed:
  • Huan Wang

    (Beijing Modern Manufacturing Industry Development Research Base, College of Economics and Management, Beijing University of Technology, Beijing 100124, China)

  • Jian Li

    (Beijing Modern Manufacturing Industry Development Research Base, College of Economics and Management, Beijing University of Technology, Beijing 100124, China)

  • Jiapeng Wang

    (Information Technology Department, Xiaomi Inc., Beijing 100085, China)

Abstract

Chinese open-domain reading comprehension question answering is a task in the field of natural language processing. Traditional neural network-based methods lack interpretability in answer reasoning when addressing open-domain reading comprehension questions. This research is grounded in cognitive science’s dual-process theory, where System One performs question reading and System Two handles reasoning, resulting in a novel Chinese open-domain question-answering retrieval algorithm. The experiment employs the publicly available WebQA dataset and is compared against other reading comprehension methods, with the F1-score reaching 78.66%, confirming the effectiveness of the proposed approach. Therefore, adopting a reading comprehension question-answering model based on cognitive graphs can effectively address Chinese reading comprehension questions.

Suggested Citation

  • Huan Wang & Jian Li & Jiapeng Wang, 2023. "Retrieving Chinese Questions and Answers Based on Deep-Learning Algorithm," Mathematics, MDPI, vol. 11(18), pages 1-18, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3843-:d:1235220
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    References listed on IDEAS

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    1. S. E. Robertson & K. Sparck Jones, 1976. "Relevance weighting of search terms," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 27(3), pages 129-146, May.
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