@InProceedings{manjavacas-EtAl:2017:StyVa,
  author    = {Manjavacas, Enrique  and  De Gussem, Jeroen  and  Daelemans, Walter  and  Kestemont, Mike},
  title     = {Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution},
  booktitle = {Proceedings of the Workshop on Stylistic Variation},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {116--125},
  abstract  = {Recent applications of neural language models have led to an increased interest
	in the automatic generation of natural language. However impressive, the
	evaluation of neurally generated text has so far remained rather informal and
	anecdotal. Here, we present an attempt at the systematic assessment of one
	aspect of the quality of neurally generated text. We focus on a specific aspect
	of neural language generation: its ability to reproduce authorial writing
	styles. Using established models for authorship attribution, we empirically
	assess the stylistic qualities of neurally generated text. In comparison to
	conventional language models, neural models generate fuzzier text, that is
	relatively harder to attribute correctly. Nevertheless, our results also
	suggest that neurally generated text offers more valuable perspectives for the
	augmentation of training data.},
  url       = {http://www.aclweb.org/anthology/W17-4914}
}