Inside-Outside Estimation Meets Dynamic EM

Abstract

We briefly review the inside-outside and EM algorithm for probabilistic context-free grammars. As a result, we formally prove that inside-outside estimation is a dynamic-programming variant of EM. This is interesting in its own right, but even more when considered in a theoretical context since the well-known convergence behavior of inside-outside estimation has been confirmed by many experiments but apparently has never been formally proved. However, being a version of EM, inside-outside estimation also inherits the good convergence behavior of EM. Therefore, the as yet imperfect line of argumentation can be transformed into a coherent proof.