A Multi-Agent Communication Framework for Question-Worthy Phrase Extraction and Question Generation

Question generation aims to produce questions automatically given a piece of text as input. Existing research follows a sequence-to-sequence fashion that constructs a single question based on the input. Considering each question usually focuses on a specific fragment of the input, especially in the scenario of reading comprehension, it is reasonable to identify the corresponding focus before constructing the question. In this paper, we propose to identify question-worthy phrases first and generate questions with the assistance of these phrases. We introduce a multi-agent communication framework, taking phrase extraction and question generation as two agents, and learn these two tasks simultaneously via message passing mechanism. The results of experiments show the effectiveness of our framework: we can extract question-worthy phrases, which are able to improve the performance of question generation. Besides, our system is able to extract more than one question worthy phrases and generate multiple questions accordingly.

Authors

• Siyuan Wang University of Southern California
• Zhongyu Wei Fudan University
• Zhihao Fan
• Yang Liu College of Computer Science, Sichuan University Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education
• Xuanjing Huang School of Computer Science, Fudan University, Shanghai, China Shanghai Collaborative Innovation Center of Intelligent Visual Computing, Shanghai, China

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