@InProceedings{gui-EtAl:2017:EMNLP20172,
  author    = {Gui, Tao  and  Zhang, Qi  and  Huang, Haoran  and  Peng, Minlong  and  Huang, Xuanjing},
  title     = {Part-of-Speech Tagging for Twitter with Adversarial Neural Networks},
  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     = {2411--2420},
  abstract  = {In this work, we study the problem of part-of-speech tagging for Tweets. In
	contrast to newswire articles, Tweets are usually informal and contain numerous
	out-of-vocabulary words. Moreover, there is a lack of large scale labeled
	datasets for this domain. To tackle these challenges, we propose a novel neural
	network to make use of out-of-domain labeled data, unlabeled in-domain data,
	and labeled in-domain data.  Inspired by adversarial neural networks, the
	proposed method tries to learn common features through adversarial
	discriminator. In addition, we hypothesize that domain-specific features of
	target domain should be preserved in some degree. Hence, the proposed method
	adopts a sequence-to-sequence autoencoder to perform this task.  Experimental
	results on three different datasets  show that our method achieves better
	performance than state-of-the-art methods.},
  url       = {https://www.aclweb.org/anthology/D17-1256}
}