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Wireless Communications and Mobile Computing
Volume 2018, Article ID 2678976, 8 pages
https://doi.org/10.1155/2018/2678976
Research Article

Using Sentence-Level Neural Network Models for Multiple-Choice Reading Comprehension Tasks

1School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
2Key Laboratory of Computation Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, China
3School of Foreign Languages, Shanxi University, Taiyuan 030006, China

Correspondence should be addressed to Yuanlong Wang; nc.ude.uxs@gnawly

Received 28 March 2018; Accepted 13 June 2018; Published 3 July 2018

Academic Editor: Tianyong Hao

Copyright © 2018 Yuanlong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Comprehending unstructured text is a challenging task for machines because it involves understanding texts and answering questions. In this paper, we study the multiple-choice task for reading comprehension based on MC Test datasets and Chinese reading comprehension datasets, among which Chinese reading comprehension datasets which are built by ourselves. Observing the above-mentioned training sets, we find that “sentence comprehension” is more important than “word comprehension” in multiple-choice task, and therefore we propose sentence-level neural network models. Our model firstly uses LSTM network and a composition model to learn compositional vector representation for sentences and then trains a sentence-level attention model for obtaining the sentence-level attention between the sentence embedding in documents and the optional sentences embedding by dot product. Finally, a consensus attention is gained by merging individual attention with the merging function. Experimental results show that our model outperforms various state-of-the-art baselines significantly for both the multiple-choice reading comprehension datasets.