Title: Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition,
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.