@InProceedings{duong-EtAl:2017:EACLlong,
  author    = {Duong, Long  and  Kanayama, Hiroshi  and  Ma, Tengfei  and  Bird, Steven  and  Cohn, Trevor},
  title     = {Multilingual Training of Crosslingual Word Embeddings},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {894--904},
  abstract  = {Crosslingual word embeddings represent lexical items from different languages 
	using the same vector space, enabling crosslingual transfer. Most prior 
	work constructs embeddings for a pair of languages, with English on one side.
	We investigate methods for building high quality crosslingual word embeddings
	for many languages in a unified vector space.In this way, we can exploit and
	combine strength of many languages.
	We obtained high performance on bilingual lexicon induction, monolingual
	similarity and crosslingual document classification tasks.},
  url       = {http://www.aclweb.org/anthology/E17-1084}
}