@InProceedings{ostling:2016:VarDial3,
  author    = {\"{O}stling, Robert},
  title     = {How Many Languages Can a Language Model Model?},
  booktitle = {Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {62},
  abstract  = {One of the purposes of the VarDial workshop series is to encourage research
	into NLP methods that treat human languages as a continuum, by designing
	models that exploit the similarities between languages and variants.
	In my work, I am using a continuous vector representation of languages that
	allows modeling and exploring the language continuum in a very direct way.
	The basic tool for this is a character-based recurrent neural network language
	model conditioned on language vectors whose values are learned during training.
	By feeding the model Bible translations in a thousand languages, not only does
	the learned vector space capture language similarity, but by interpolating
	between the learned vectors it is possible to generate text in unattested
	intermediate forms between the training languages.},
  url       = {http://aclweb.org/anthology/W16-4808}
}