@InProceedings{fernandez-yu-downey:2017:EMNLP2017,
  author    = {Fernandez, Jared  and  Yu, Zhaocheng  and  Downey, Doug},
  title     = {VecShare: A Framework for Sharing Word Representation Vectors},
  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     = {316--320},
  abstract  = {Many Natural Language Processing (NLP) models rely on distributed vector
	representations of words.  Because the process of training word vectors can
	require large amounts of data and computation, NLP researchers and
	practitioners often utilize pre-trained embeddings downloaded from the Web. 
	However, finding the best embeddings for a given task is difficult, and can be
	computationally prohibitive.  We present a framework, called VecShare, that
	makes it easy to share and retrieve word embeddings on the Web.  The framework
	leverages a public data-sharing infrastructure to host embedding sets, and
	provides automated mechanisms for retrieving the embeddings most similar to a
	given corpus.  We perform an experimental evaluation of VecShare's similarity
	strategies, and show that they are effective at efficiently retrieving
	embeddings that boost accuracy in a document classification task.  Finally, we
	provide an open-source Python library for using the VecShare framework.},
  url       = {https://www.aclweb.org/anthology/D17-1032}
}