@inproceedings{jiang-etal-2017-combining,
    title = "Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition",
    author = "Jiang, Yong  and
      Han, Wenjuan  and
      Tu, Kewei",
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/D17-1177/",
    doi = "10.18653/v1/D17-1177",
    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."
}