@InProceedings{herbelot-baroni:2017:EMNLP2017,
  author    = {Herbelot, Aur\'{e}lie  and  Baroni, Marco},
  title     = {High-risk learning: acquiring new word vectors from tiny data},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {304--309},
  abstract  = {Distributional semantics models are known to struggle with small data. It is
	generally accepted that in order to learn 'a good vector' for a word, a model
	must have sufficient examples of its usage. This contradicts the fact that
	humans can guess the meaning of a word from a few occurrences only.  In this
	paper, we show that a neural language model such as Word2Vec only necessitates
	minor modifications to its standard architecture to learn new terms from tiny
	data, using background knowledge from a previously learnt semantic space. We
	test our model on word definitions and on a nonce task involving 2-6 sentences'
	worth of context, showing a large increase in performance over state-of-the-art
	models on the definitional task.},
  url       = {https://www.aclweb.org/anthology/D17-1030}
}