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Unsupervised dependency parsing aims to learn a dependency parser fromunannotated sentences. Existing work focuses on either learning generativemodels using the expectation-maximization algorithm and its variants, orlearning discriminative models using the discriminative clustering algorithm.In this paper, we propose a new learning strategy that learns a generativemodel and a discriminative model jointly based on the dual decompositionmethod. Our method is simple and general, yet effective to capture theadvantages of both models and improve their learning results. We tested ourmethod on the UD treebank and achieved a state-of-the-art performance on thirtylanguages.

Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition