@InProceedings{qin-EtAl:2017:Long,
  author    = {Qin, Lianhui  and  Zhang, Zhisong  and  Zhao, Hai  and  Hu, Zhiting  and  Xing, Eric},
  title     = {Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1006--1017},
  abstract  = {Implicit discourse relation classification is of great challenge due to the
	lack of connectives as strong linguistic cues, which motivates the use of
	annotated implicit connectives to improve the recognition. We propose a feature
	imitation framework in which an implicit relation network is driven to learn
	from another neural network with access to connectives, and thus encouraged to
	extract similarly salient features for accurate classification. We develop an
	adversarial model to enable an adaptive imitation scheme through competition
	between the implicit network and a rival feature discriminator. Our method
	effectively transfers discriminability of connectives to the implicit features,
	and achieves state-of-the-art performance on the PDTB benchmark.},
  url       = {http://aclweb.org/anthology/P17-1093}
}