@InProceedings{malmasi-EtAl:2017:Long,
  author    = {Malmasi, Shervin  and  Dras, Mark  and  Johnson, Mark  and  Du, Lan  and  Wolska, Magdalena},
  title     = {Unsupervised Text Segmentation Based on Native Language Characteristics},
  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     = {1457--1469},
  abstract  = {Most work on segmenting text does so on the basis of topic changes,
	but it can be of interest to segment by other, stylistically expressed
	characteristics such as change of authorship or native language.  We
	propose a Bayesian unsupervised text segmentation approach to the
	latter.  While baseline models achieve essentially random segmentation
	on our task, indicating its difficulty, a Bayesian model that
	incorporates appropriately compact language models and alternating
	asymmetric priors can achieve scores on the standard metrics around
	halfway to perfect segmentation.},
  url       = {http://aclweb.org/anthology/P17-1134}
}