@InProceedings{jhamtani-EtAl:2017:StyVa,
  author    = {Jhamtani, Harsh  and  Gangal, Varun  and  Hovy, Eduard  and  Nyberg, Eric},
  title     = {Shakespearizing Modern Language Using Copy-Enriched Sequence to Sequence Models},
  booktitle = {Proceedings of the Workshop on Stylistic Variation},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {10--19},
  abstract  = {Variations in writing styles are commonly used to adapt the content to a
	specific context, audience, or purpose. However, applying stylistic variations
	is still by and large a manual process, and there have been little efforts
	towards automating it. In this paper we explore automated methods to transform
	text from modern English to Shakespearean English using an end to end trainable
	neural model with pointers to enable copy action. To tackle limited amount of
	parallel data, we pre-train embeddings of words by leveraging external
	dictionaries mapping Shakespearean words to modern English words as well as
	additional text. Our methods are able to get a BLEU score of 31+, an
	improvement of ≈ 6 points above the strongest baseline. We publicly release
	our code to foster further research in this area.},
  url       = {http://www.aclweb.org/anthology/W17-4902}
}