@InProceedings{gan-EtAl:2017:EMNLP2017,
  author    = {Gan, Zhe  and  Pu, Yunchen  and  Henao, Ricardo  and  Li, Chunyuan  and  He, Xiaodong  and  Carin, Lawrence},
  title     = {Learning Generic Sentence Representations Using Convolutional Neural Networks},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2390--2400},
  abstract  = {We propose a new encoder-decoder approach to learn distributed sentence
	representations that are applicable to multiple purposes. The model is learned
	by using a convolutional neural network as an encoder to map an input sentence
	into a continuous vector, and using a long short-term memory recurrent neural
	network as a decoder. Several tasks are considered, including sentence
	reconstruction and future sentence prediction. Further, a hierarchical
	encoder-decoder model is proposed to encode a sentence to predict multiple
	future sentences. By training our models on a large collection of novels, we
	obtain a highly generic convolutional sentence encoder that performs well in
	practice. Experimental results on several benchmark datasets, and across a
	broad range of applications, demonstrate the superiority of the proposed model
	over competing methods.},
  url       = {https://www.aclweb.org/anthology/D17-1254}
}