@InProceedings{johnson-zhang:2017:Long,
  author    = {Johnson, Rie  and  Zhang, Tong},
  title     = {Deep Pyramid Convolutional Neural Networks for Text Categorization},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {562--570},
  abstract  = {This paper proposes a low-complexity word-level deep convolutional neural
	network (CNN) architecture for text categorization that can efficiently
	represent long-range associations in text.  In the literature, several deep and
	complex neural networks have been proposed for this task, assuming availability
	of relatively large amounts of training data.  However, the associated
	computational complexity increases as the networks go deeper, which poses
	serious challenges in practical applications.  Moreover, it was shown recently
	that shallow word-level CNNs are more accurate and much faster than the
	state-of-the-art very deep nets such as character-level CNNs even in the
	setting of large training data.  Motivated by these findings, we carefully
	studied deepening of word-level CNNs to capture global representations of text,
	and found a simple network architecture with which the best accuracy can be
	obtained by increasing the network depth without increasing computational cost
	by much.  We call it deep pyramid CNN.                                               
	  The
	proposed
	model
	with 15
	weight
	layers outperforms the previous best models on six benchmark datasets for
	sentiment classification and topic categorization.},
  url       = {http://aclweb.org/anthology/P17-1052}
}