@InProceedings{zhang-duh-vandurme:2017:EACLshort,
  author    = {Zhang, Sheng  and  Duh, Kevin  and  Van Durme, Benjamin},
  title     = {MT/IE: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {64--70},
  abstract  = {Cross-lingual information extraction is the task of distilling facts from
	foreign language (e.g. Chinese text) into representations in another language
	that is preferred by the user (e.g. English tuples). Conventional pipeline
	solutions decompose the task as machine translation followed by information
	extraction (or vice versa). We propose a joint solution with a neural sequence
	model, and show that it outperforms the pipeline in a cross-lingual open
	information extraction setting by 1-4 BLEU and 0.5-0.8 F1.},
  url       = {http://www.aclweb.org/anthology/E17-2011}
}