Graph-Based Posterior Regularization for Semi-Supervised Structured Prediction
Luheng He, Jennifer Gillenwater, Ben Taskar · 2013
We present a flexible formulation of semisupervised learning for structured models, which seamlessly incorporates graphbased and more general supervision by extending the posterior regularization (PR) framework. Our extension allows for any regularizer that is a convex, differentiable function of the appropriate marginals. We show that surprisingly, non-linearity of such regularization does not increase the complexity of learning, provided we use multiplicative updates of the structured exponentiated gradient algorithm. We illustrate the extended framework by learning conditional random fields (CRFs) with quadratic penalties arising from a graph Laplacian. On sequential prediction tasks of handwriting recognition and part-ofspeech (POS) tagging, our method makes significant gains over strong baselines. 1