@InProceedings{wang-peng-duh:2017:I17-2,
  author    = {Wang, Dingquan  and  Peng, Nanyun  and  Duh, Kevin},
  title     = {A Multi-task Learning Approach to Adapting Bilingual Word Embeddings for Cross-lingual Named Entity Recognition},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {383--388},
  abstract  = {We show how to adapt bilingual word embeddings (BWE's) to bootstrap a
	cross-lingual name-entity recognition (NER) system in a language with no
	labeled data. We assume a setting where we are given a comparable corpus with
	NER labels for the source language only; our goal is to build a NER model for
	the target language. The proposed multi-task model jointly trains bilingual
	word embeddings while optimizing a NER objective. This creates word embeddings
	that are both shared between languages and fine-tuned for the NER task.},
  url       = {http://www.aclweb.org/anthology/I17-2065}
}