@InProceedings{fang-cohn:2017:Short,
  author    = {Fang, Meng  and  Cohn, Trevor},
  title     = {Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {587--593},
  abstract  = {Cross-lingual model transfer is a compelling and popular method for predicting
	annotations in a low-resource language, whereby parallel corpora provide a
	bridge to a high-resource language, and its associated annotated corpora.
	However, parallel data is not readily available for many languages, limiting
	the applicability of these approaches. We address these drawbacks in our
	framework which takes advantage of cross-lingual word embeddings trained solely
	on a high coverage dictionary. We propose a novel neural network model for
	joint training from both sources of data based on cross-lingual word
	embeddings, and show substantial empirical improvements over baseline
	techniques. We also propose several active learning heuristics, which result in
	improvements over competitive benchmark methods.},
  url       = {http://aclweb.org/anthology/P17-2093}
}