@InProceedings{zhang-EtAl:2017:Long5,
  author    = {Zhang, Meng  and  Liu, Yang  and  Luan, Huanbo  and  Sun, Maosong},
  title     = {Adversarial Training for Unsupervised Bilingual Lexicon Induction},
  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     = {1959--1970},
  abstract  = {Word embeddings are well known to capture linguistic regularities of the
	language on which they are trained. Researchers also observe that these
	regularities can transfer across languages. However, previous endeavors to
	connect separate monolingual word embeddings typically require cross-lingual
	signals as supervision, either in the form of parallel corpus or seed lexicon.
	In this work, we show that such cross-lingual connection can actually be
	established without any form of supervision. We achieve this end by formulating
	the problem as a natural adversarial game, and investigating techniques that
	are crucial to successful training. We carry out evaluation on the unsupervised
	bilingual lexicon induction task. Even though this task appears intrinsically
	cross-lingual, we are able to demonstrate encouraging performance without any
	cross-lingual clues.},
  url       = {http://aclweb.org/anthology/P17-1179}
}