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Comparison Question Generation Based on Potential Compared Attributes Extraction

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Web and Big Data (APWeb-WAIM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12859))

Abstract

Question generation (QG) aims to automatically generate questions from a given passage, which is widely used in education. Existing studies on the QG task mainly focus on the answer-aware QG, which only asks an independent object related to the expected answer. However, to prompt students to develop comparative thinking skills, multiple objects need to be simultaneously focused on the QG task, which can be used to attract students to explore the differences and similarities between them. Towards this end, we consider a new task named comparison question generation (CQG). In this paper, we propose a framework that includes an attribute extractor and an attribute-attention seq2seq module. Specially, the attribute extractor is based on Stanford CoreNLP Toolkit to recognize the attributes related to the multiple objects that can be used for comparison. Then, the attribute-attention seq2seq module utilizes an attention mechanism to generate questions with the assistance of the attributes. Extensive experiments conducted on the HotpotQA dataset manifest the effectiveness of our framework, which outperforms the neural-based model and generates reliable comparison questions.

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Acknowledgement

This work was supported by National Natural Science Foundation of China (No. 620761 00), National Key Research and Development Program of China (Standard knowledge graph for epidemic prevention and production recovering intelligent service platform and its applications), the Fundamental Research Funds for the Central Universities, SCUT (No. D2201300, D2210010), the Science and Technology Programs of Guangzhou(201902010046), the Science and Technology Planning Project of Guangdong Province (No. 2020B0101100002).

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Authors and Affiliations

  1. Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education, Guangzhou, China

    Jiayuan Xie, Wenhao Fang & Yi Cai

  2. South China University of Technology, Guangzhou, China

    Jiayuan Xie, Wenhao Fang & Yi Cai

  3. The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Zehang Lin

Authors
  1. Jiayuan Xie
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  2. Wenhao Fang
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  3. Yi Cai
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  4. Zehang Lin
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Corresponding author

Correspondence to Yi Cai .

Editor information

Editors and Affiliations

  1. University of Macau, Macau, China

    Leong Hou U

  2. University of Caen Normandie, Caen, France

    Marc Spaniol

  3. Osaka University, Osaka, Japan

    Yasushi Sakurai

  4. South China University of Technology, Guangzhou, China

    Junying Chen

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Xie, J., Fang, W., Cai, Y., Lin, Z. (2021). Comparison Question Generation Based on Potential Compared Attributes Extraction. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science(), vol 12859. Springer, Cham. https://doi.org/10.1007/978-3-030-85899-5_18

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  • DOI: https://doi.org/10.1007/978-3-030-85899-5_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85898-8

  • Online ISBN: 978-3-030-85899-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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