@InProceedings{ma-EtAl:2017:Short1,
  author    = {Ma, Mingbo  and  Huang, Liang  and  Xiang, Bing  and  Zhou, Bowen},
  title     = {Group Sparse CNNs for Question Classification with Answer Sets},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {335--340},
  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.},
  url       = {http://aclweb.org/anthology/P17-2053}
}