TY - JOUR AU - Li, Zhaohui AU - Feng, Yue AU - Xu, Jun AU - Guo, Jiafeng AU - Lan, Yanyan AU - Cheng, Xueqi PY - 2019/07/17 Y2 - 2024/03/28 TI - Teaching Machines to Extract Main Content for Machine Reading Comprehension JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - Student Abstract Track DO - 10.1609/aaai.v33i01.33019973 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5123 SP - 9973-9974 AB - <p>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 <em>main content</em> 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.</p> ER -