Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition
This work addresses the problem of learning dependency parsers from unannotated text for natural language processing, representing an incremental improvement over existing methods.
The paper tackled unsupervised dependency parsing by jointly learning generative and discriminative models using dual decomposition, achieving state-of-the-art performance on thirty languages in the UD treebank.
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.