@InProceedings{boleda:2017:INLG2017,
  author    = {Boleda, Gemma},
  title     = {Talking about the world with a distributed model},
  booktitle = {Proceedings of the 10th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2017},
  address   = {Santiago de Compostela, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {114},
  abstract  = {We use language to talk about the world, and so reference is a crucial property
	of language. However, modeling reference is particularly difficult, as it
	involves both continuous and discrete as-pects of language. For instance,
	referring expressions like "the big mug" or "it" typically contain content
	words ("big", "mug"), which are notoriously fuzzy or vague in their meaning,
	and also fun-ction words ("the", "it") that largely serve as discrete pointers.
	Data-driven, distributed models based on distributional semantics or deep
	learning excel at the former, but struggle with the latter, and the reverse is
	true for symbolic models. I present ongoing work on modeling reference with a
	distribu-ted model aimed at capturing both aspects, and learns to refer
	directly from reference acts.},
  url       = {http://www.aclweb.org/anthology/W17-3515}
}