Accurate dependency parsing requires large treebanks, which are only available for a few languages. We propose a method that takes advantage of shared structure across languages to build a mature parser using less training data. We propose a model for learning a shared “univer-sal ” parser that operates over an inter-lingual continuous representation of lan-guage, along with language-specific map-ping components. Compared with super-vised learning, our methods give a con-sistent 8-10 % improvement across several treebanks in low-resource simulations. 1