@InProceedings{cai-jiang-tu:2017:EMNLP2017,
  author    = {Cai, Jiong  and  Jiang, Yong  and  Tu, Kewei},
  title     = {CRF Autoencoder for Unsupervised Dependency Parsing},
  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     = {1638--1643},
  abstract  = {Unsupervised dependency parsing, which tries to discover linguistic dependency
	structures from unannotated data, is a very challenging task. Almost all
	previous work on this task focuses on learning generative models. In this
	paper, we develop an unsupervised dependency parsing model based on the CRF
	autoencoder. The encoder part of our model is discriminative and globally
	normalized which allows us to use rich features as well as universal linguistic
	priors. We propose an exact algorithm for parsing as well as a tractable
	learning algorithm. We evaluated the performance of our model on eight
	multilingual treebanks and found that our model achieved comparable performance
	with state-of-the-art approaches.},
  url       = {https://www.aclweb.org/anthology/D17-1171}
}