@InProceedings{wu-bamman-russell:2017:EMNLP2017,
  author    = {Wu, Yi  and  Bamman, David  and  Russell, Stuart},
  title     = {Adversarial Training for Relation Extraction},
  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     = {1778--1783},
  abstract  = {Adversarial training is a mean of regularizing classification algorithms by
	generating adversarial noise to the training data. We apply adversarial
	training in relation extraction within the multi-instance multi-label learning
	framework. We evaluate various neural network architectures on two different
	datasets. Experimental results demonstrate that adversarial training is
	generally effective for both CNN and RNN models and significantly improves the
	precision of predicted relations.},
  url       = {https://www.aclweb.org/anthology/D17-1187}
}