Group Sparse CNNs for Question Classification with Answer Sets

Mingbo Ma, Liang Huang, Bing Xiang, Bowen Zhou


Abstract
Question classification is an important task with wide applications. However, traditional techniques treat questions as general sentences, ignoring the corresponding answer data. In order to consider answer information into question modeling, we first introduce novel group sparse autoencoders which refine question representation by utilizing group information in the answer set. We then propose novel group sparse CNNs which naturally learn question representation with respect to their answers by implanting group sparse autoencoders into traditional CNNs. The proposed model significantly outperform strong baselines on four datasets.
Anthology ID:
P17-2053
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
335–340
Language:
URL:
https://www.aclweb.org/anthology/P17-2053
DOI:
10.18653/v1/P17-2053
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-2053.pdf