@InProceedings{subramanian-EtAl:2017:RepL4NLP,
  author    = {Subramanian, Sandeep  and  Rajeswar, Sai  and  Dutil, Francis  and  Pal, Chris  and  Courville, Aaron},
  title     = {Adversarial Generation of Natural Language},
  booktitle = {Proceedings of the 2nd Workshop on Representation Learning for NLP},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {241--251},
  abstract  = {Generative Adversarial Networks (GANs) have gathered a lot of attention from
	the computer vision community, yielding impressive results for image
	generation. Advances in the adversarial generation of natural language from
	noise however are not commensurate with the progress made in generating images,
	and still lag far behind likelihood based methods. In this paper, we take a
	step towards generating natural language  with a GAN objective alone. We
	introduce a simple baseline that addresses the discrete output space problem
	without relying on gradient estimators and show that it is able to achieve
	state-of-the-art results on a Chinese poem generation dataset. We present
	quantitative results on generating sentences from context-free and
	probabilistic context-free grammars, and qualitative language modeling results.
	A conditional version is also described that can generate sequences conditioned
	on sentence characteristics.},
  url       = {http://www.aclweb.org/anthology/W17-2629}
}