@InProceedings{athiwaratkun-wilson:2017:Long,
  author    = {Athiwaratkun, Ben  and  Wilson, Andrew},
  title     = {Multimodal Word Distributions},
  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     = {1645--1656},
  abstract  = {Word embeddings provide point representations of words containing useful
	semantic information. 
	We introduce multimodal word distributions formed from Gaussian mixtures, for
	multiple word meanings, entailment, and rich uncertainty information.  To learn
	these distributions, we propose an energy-based max-margin objective. We show
	that the resulting approach captures uniquely  expressive semantic information,
	and outperforms alternatives, such as word2vec skip-grams, and Gaussian
	embeddings, on benchmark datasets such as word similarity and entailment.},
  url       = {http://aclweb.org/anthology/P17-1151}
}