@InProceedings{zhang-EtAl:2017:EMNLP20175,
  author    = {Zhang, Meng  and  Liu, Yang  and  Luan, Huanbo  and  Sun, Maosong},
  title     = {Earth Mover's Distance Minimization for Unsupervised Bilingual Lexicon Induction},
  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     = {1934--1945},
  abstract  = {Cross-lingual natural language processing hinges on the premise that there
	exists invariance across languages. At the word level, researchers have
	identified such invariance in the word embedding semantic spaces of different
	languages. However, in order to connect the separate spaces, cross-lingual
	supervision encoded in parallel data is typically required. In this paper, we
	attempt to establish the cross-lingual connection without relying on any
	cross-lingual supervision. By viewing word embedding spaces as distributions,
	we propose to minimize their earth mover's distance, a measure of divergence
	between distributions. We demonstrate the success on the unsupervised bilingual
	lexicon induction task. In addition, we reveal an interesting finding that the
	earth mover's distance shows potential as a measure of language difference.
	Author{4}{Affiliation}},
  url       = {https://www.aclweb.org/anthology/D17-1207}
}