@InProceedings{semeniuta-severyn-barth:2017:EMNLP2017,
  author    = {Semeniuta, Stanislau  and  Severyn, Aliaksei  and  Barth, Erhardt},
  title     = {A Hybrid Convolutional Variational Autoencoder for Text Generation},
  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     = {627--637},
  abstract  = {In this paper we explore the effect of architectural choices on learning a
	variational autoencoder (VAE) for text generation. In contrast to the
	previously introduced VAE model for text where both the encoder and decoder are
	RNNs, we propose a novel hybrid architecture that blends fully feed-forward
	convolutional and deconvolutional components with a recurrent language model.
	Our architecture exhibits several attractive properties such as faster run time
	and convergence, ability to better handle long sequences and, more importantly,
	it helps to avoid the issue of the VAE collapsing to a deterministic model.},
  url       = {https://www.aclweb.org/anthology/D17-1066}
}