Retrieving Chinese Questions and Answers Based on Deep-Learning Algorithm

Huan Wang, Jian Li and Jiapeng Wang ()
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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

Mathematics, 2023, vol. 11, issue 18, 1-18

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.

Keywords: dual-process theory; cognitive graph; Chinese open-domain reading comprehension question answering (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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