Teaching Machines to Extract Main Content for Machine Reading Comprehension


  • Zhaohui Li Chinese Academy of Sciences
  • Yue Feng Renmin University of China
  • Jun Xu Chinese Academy of Sciences
  • Jiafeng Guo Chinese Academy of Sciences
  • Yanyan Lan Chinese Academy of Sciences
  • Xueqi Cheng Chinese Academy of Sciences




Machine reading comprehension, whose goal is to find answers from the candidate passages for a given question, has attracted a lot of research efforts in recent years. One of the key challenge in machine reading comprehension is how to identify the main content from a large, redundant, and overlapping set of candidate sentences. In this paper we propose to tackle the challenge with Markov Decision Process in which the main content identification is formalized as sequential decision making and each action corresponds to selecting a sentence. Policy gradient is used to learn the model parameters. Experimental results based on MSMARCO showed that the proposed model, called MC-MDP, can select high quality main contents and significantly improved the performances of answer span prediction.




How to Cite

Li, Z., Feng, Y., Xu, J., Guo, J., Lan, Y., & Cheng, X. (2019). Teaching Machines to Extract Main Content for Machine Reading Comprehension. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9973-9974. https://doi.org/10.1609/aaai.v33i01.33019973



Student Abstract Track