@InProceedings{fang-li-cohn:2017:EMNLP2017,
  author    = {Fang, Meng  and  Li, Yuan  and  Cohn, Trevor},
  title     = {Learning how to Active Learn: A Deep Reinforcement Learning Approach},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {595--605},
  abstract  = {Active learning aims to select a small subset of data for annotation such that
	a classifier learned on the data is highly accurate. This is usually done using
	heuristic selection methods, however the effectiveness of such methods is
	limited and moreover, the performance of heuristics varies between datasets. To
	address these shortcomings, we introduce a novel formulation by reframing the
	active learning as a reinforcement learning problem and explicitly learning a
	data selection policy, where the policy takes the role of the active learning
	heuristic. Importantly, our method allows the selection policy learned using
	simulation to one language to be transferred to other languages. We demonstrate
	our method using cross-lingual named entity recognition, observing uniform
	improvements over traditional active learning algorithms.},
  url       = {https://www.aclweb.org/anthology/D17-1063}
}