@InProceedings{gurnani:2017:RepEval,
  author    = {Gurnani, Nishant},
  title     = {Hypothesis Testing based Intrinsic Evaluation of Word Embeddings},
  booktitle = {Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {16--20},
  abstract  = {We introduce the cross-match test - an exact, distribution free,
	high-dimensional hypothesis test as an intrinsic evaluation metric for word
	embeddings. We show that cross-match is an effective means of measuring the
	distributional similarity between different vector representations and of
	evaluating the statistical significance of different vector embedding models.
	Additionally, we find that cross-match can be used to provide a quantitative
	measure of linguistic similarity for selecting bridge languages for machine
	translation. We demonstrate that the results of the hypothesis test align with
	our expectations and note that the framework of two sample hypothesis testing
	is not limited to word embeddings and can be extended to all vector
	representations.},
  url       = {http://www.aclweb.org/anthology/W17-5303}
}