@InProceedings{hopkins-kiela:2017:Long,
  author    = {Hopkins, Jack  and  Kiela, Douwe},
  title     = {Automatically Generating Rhythmic Verse with Neural Networks},
  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     = {168--178},
  abstract  = {We propose two novel methodologies for the automatic generation of rhythmic
	poetry in a variety of forms. The first approach uses a neural language model
	trained on a phonetic encoding to learn an implicit representation of both the
	form and content of English poetry. This model can effectively learn common
	poetic devices such as rhyme, rhythm and alliteration. The second approach
	considers poetry generation as a constraint satisfaction problem where a
	generative neural language model is tasked with learning a representation of
	content, and a discriminative weighted finite state machine constrains it on
	the basis of form. By manipulating the constraints of the latter model, we can
	generate coherent poetry with arbitrary forms and themes. A large-scale
	extrinsic evaluation demonstrated that participants consider machine-generated
	poems to be written by humans 54% of the time. In addition, participants rated
	a machine-generated poem to be the best amongst all evaluated.},
  url       = {http://aclweb.org/anthology/P17-1016}
}