@InProceedings{jiang-han-tu:2017:EMNLP2017,
  author    = {Jiang, Yong  and  Han, Wenjuan  and  Tu, Kewei},
  title     = {Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition},
  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     = {1689--1694},
  abstract  = {Unsupervised dependency parsing aims to learn a dependency parser from
	unannotated sentences. Existing work focuses on either learning generative
	models using the expectation-maximization algorithm and its variants, or
	learning discriminative models using the discriminative clustering algorithm.
	In this paper, we propose a new learning strategy that learns a generative
	model and a discriminative model jointly based on the dual decomposition
	method. Our method is simple and general, yet effective to capture the
	advantages of both models and improve their learning results. We tested our
	method on the UD treebank and achieved a state-of-the-art performance on thirty
	languages.},
  url       = {https://www.aclweb.org/anthology/D17-1177}
}