@InProceedings{pezzelle-marelli-bernardi:2017:EACLshort,
  author    = {Pezzelle, Sandro  and  Marelli, Marco  and  Bernardi, Raffaella},
  title     = {Be Precise or Fuzzy: Learning the Meaning of Cardinals and Quantifiers from Vision},
  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     = {337--342},
  abstract  = {People can refer to quantities in a visual scene by using either exact
	cardinals (e.g. one, two, three) or natural language quantifiers (e.g. few,
	most, all). In humans, these two processes underlie fairly different cognitive
	and neural mechanisms. Inspired by this evidence, the present study proposes
	two models for learning the objective meaning of cardinals and quantifiers from
	visual scenes containing multiple objects. We show that a model capitalizing on
	a 'fuzzy' measure of similarity is effective for learning quantifiers,
	whereas the learning of exact cardinals is better accomplished when information
	about number is provided.},
  url       = {http://www.aclweb.org/anthology/E17-2054}
}