Entity Guided Question Generation with Contextual Structure and Sequence Information Capturing

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.

Authors

• Qingbao Huang School of Electrical Engineering, Guangxi University, Nanning, China Guangxi Key Laboratory of Multimedia Communications and Network Technology, Nanning, China
• Mingyi Fu
• Linzhang Mo
• Yi Cai South China University of Technology
• Jingyun Xu
• Pijian Li School of Electrical Engineering, Guangxi University, Nanning, Guangxi, China
• Qing Li The Hong Kong Polytechnic University
• Ho-fung Leung Independent Researcher

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