Learning Structured Classifiers with Dual Coordinate Ascent

摘要

We present a unified framework for online learning of structured classifiers that handles a wide family of convex loss functions, properly including CRFs, structured SVMs, and the structured perceptron. We introduce a new aggressive online algorithm that optimizes any loss in this family. For the structured hinge loss, this algorithm reduces to 1-best MIRA; in general, it can be regarded as a dual coordinate ascent algorithm. The approximate inference scenario is also addressed. Our experiments on two NLP problems show that the algorithm converges to accurate models at least as fast as stochastic gradient descent, without the need to specify any learning rate parameter.