Penalized expectation propagation for graphical models over strings

Abstract

We present penalized expectation propagation (PEP), a novel algorithm for approximate inference in graphical models. Expectation propagation is a variant of loopy belief propagation that keeps messages tractable by projecting them back into a given family of functions. Our extension, PEP, uses a structuredsparsity penalty to encourage simple messages, thus balancing speed and accuracy. We specifically show how to instantiate PEP in the case of string-valued random variables, where we adaptively approximate finite-state distributions by variable-order n-gram models. On phonological inference problems, we obtain substantial speedup over previous related algorithms with no significant loss in accuracy.