@InProceedings{zhang-EtAl:2016:COLING6,
  author    = {Zhang, Meng  and  Liu, Yang  and  Luan, Huanbo  and  Liu, Yiqun  and  Sun, Maosong},
  title     = {Inducing Bilingual Lexica From Non-Parallel Data With Earth Mover's Distance Regularization},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {3188--3198},
  abstract  = {Being able to induce word translations from non-parallel data is often a
	prerequisite for cross-lingual processing in resource-scarce languages and
	domains. Previous endeavors typically simplify this task by imposing the
	one-to-one translation assumption, which is too strong to hold for natural
	languages. We remove this constraint by introducing the Earth Mover's Distance
	into the training of bilingual word embeddings. In this way, we take advantage
	of its capability to handle multiple alternative word translations in a natural
	form of regularization. Our approach shows significant and consistent
	improvements across four language pairs. We also demonstrate that our approach
	is particularly preferable in resource-scarce settings as it only requires a
	minimal seed lexicon.
	Author{5}{Affiliation}},
  url       = {http://aclweb.org/anthology/C16-1300}
}