@InProceedings{heyman-vulic-moens:2017:EACLlong,
  author    = {Heyman, Geert  and  Vuli\'{c}, Ivan  and  Moens, Marie-Francine},
  title     = {Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations},
  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     = {1085--1095},
  abstract  = {We study the problem of bilingual lexicon induction (BLI) in a setting where
	some translation resources are available, but unknown translations are sought
	for certain, possibly domain-specific terminology. We frame BLI as a
	classification problem for which we design a neural network based
	classification architecture composed of recurrent long short-term memory and
	deep feed forward networks. 
	The results show that word- and character-level representations each improve
	state-of-the-art results for BLI, and the best results are obtained by
	exploiting the synergy between these word- and character-level representations
	in the classification model.},
  url       = {http://www.aclweb.org/anthology/E17-1102}
}