@InProceedings{cotterell-duh:2017:I17-2,
  author    = {Cotterell, Ryan  and  Duh, Kevin},
  title     = {Low-Resource Named Entity Recognition with Cross-lingual, Character-Level Neural Conditional Random Fields},
  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     = {91--96},
  abstract  = {Low-resource named entity recognition is still an open problem in NLP. Most
	state-of-the-art systems require tens of thousands of annotated sentences in
	order to obtain high performance. However, for most of the world's languages it
	is unfeasible to obtain such annotation. In this paper, we present a transfer
	learning scheme, whereby we train character-level neural CRFs to predict named
	entities for both high-resource languages and low-resource languages jointly.
	Learning character representations for multiple related languages allows
	knowledge transfer from the high-resource languages to the low-resource ones,
	improving F1 by up to 9.8 points.},
  url       = {http://www.aclweb.org/anthology/I17-2016}
}