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In this paper, we introduce a new method for the problem of unsupervised dependency parsing. Most current approaches are based on generative models. Learning the parameters of such models relies on solving a non-convex optimization problem, thus making them sensitive to initialization. We propose a new convex formulation to the task of dependency grammar induction. Our approach is discriminative, allowing the use of different kinds of features. We describe an efficient optimization algorithm to learn the parameters of our model, based on the Frank-Wolfe algorithm. Our method can easily be generalized to other unsupervised learning problems. We evaluate our approach on ten languages belonging to four different families, showing that our method is competitive with other state-of-the-art methods.

A convex and feature-rich discriminative approach to dependency grammar induction