@InProceedings{cotterell-sylakglassman-kirov:2017:EACLshort,
  author    = {Cotterell, Ryan  and  Sylak-Glassman, John  and  Kirov, Christo},
  title     = {Neural Graphical Models over Strings for Principal Parts Morphological Paradigm Completion},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {759--765},
  abstract  = {Many of the world's languages contain an abundance of inflected forms for each
	lexeme. A critical task in processing such languages is predicting these
	inflected forms. We develop a novel statistical model for the problem, drawing
	on graphical modeling techniques and recent advances in deep learning. We
	derive a Metropolis-Hastings algorithm to jointly decode the model. Our
	Bayesian network draws inspiration from principal parts morphological analysis.
	We demonstrate improvements on 5 languages.},
  url       = {http://www.aclweb.org/anthology/E17-2120}
}