Teaching Machines to Extract Main Content for Machine Reading Comprehension
Abstract
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.
Cite
Text
Li et al. "Teaching Machines to Extract Main Content for Machine Reading Comprehension." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33019973Markdown
[Li et al. "Teaching Machines to Extract Main Content for Machine Reading Comprehension." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/li2019aaai-teaching/) doi:10.1609/AAAI.V33I01.33019973BibTeX
@inproceedings{li2019aaai-teaching,
title = {{Teaching Machines to Extract Main Content for Machine Reading Comprehension}},
author = {Li, Zhaohui and Feng, Yue and Xu, Jun and Guo, Jiafeng and Lan, Yanyan and Cheng, Xueqi},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {9973-9974},
doi = {10.1609/AAAI.V33I01.33019973},
url = {https://mlanthology.org/aaai/2019/li2019aaai-teaching/}
}