Entity Guided Question Generation with Contextual Structure and Sequence Information Capturing

Authors

  • Qingbao Huang School of Software Engineering, South China University of Technology, Guangzhou, China School of Electrical Engineering, Guangxi University, Nanning, China
  • Mingyi Fu School of Electrical Engineering, Guangxi University, Nanning, China
  • Linzhang Mo School of Electrical Engineering, Guangxi University, Nanning, China
  • Yi Cai School of Software Engineering, South China University of Technology, Guangzhou, China Key Laboratory of Big Data and Intelligent Robot (SCUT), MOE of China
  • Jingyun Xu School of Software Engineering, South China University of Technology, Guangzhou, China
  • Pijian Li School of Electrical Engineering, Guangxi University, Nanning, China
  • Qing Li Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
  • Ho-fung Leung Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China

Keywords:

Generation

Abstract

Question generation is a challenging task and has attracted widespread attention in recent years. Although previous studies have made great progress, there are still two main shortcomings: First, previous work did not simultaneously capture the sequence information and structure information hidden in the context, which results in poor results of the generated questions. Second, the generated questions cannot be answered by the given context. To tackle these issues, we propose an entity guided question generation model with contextual structure information and sequence information capturing. We use a Graph Convolutional Network and a Bidirectional Long Short Term Memory Network to capture the structure information and sequence information of the context, simultaneously. In addition, to improve the answerability of the generated questions, we use an entity-guided approach to obtain question type from the answer, and jointly encode the answer and question type. Both automatic and manual metrics show that our model can generate comparable questions with state-of-the-art models. Our code is available at https://github.com/VISLANG-Lab/EGSS.

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Published

2021-05-18

How to Cite

Huang, Q., Fu, M., Mo, L., Cai, Y., Xu, J., Li, P., Li, Q., & Leung, H.- fung. (2021). Entity Guided Question Generation with Contextual Structure and Sequence Information Capturing. Proceedings of the AAAI Conference on Artificial Intelligence, 35(14), 13064-13072. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17544

Issue

Section

AAAI Technical Track on Speech and Natural Language Processing I