Inside-Outside Algorithm for Macro Grammars

Ryuta Kambe, Naoki Kobayashi, Ryosuke Sato, Ayumi Shinohara, Ryo Yoshinaka

Abstract: We propose an inside-outside algorithm for stochastic macro grammars. Our approach is based on types, which has been inspired by type-based approaches to reasoning about functional programs and higher-order grammars. By considering type derivations instead of ordinary word derivation sequences, we can naturally extend the standard inside-outside algorithm for stochastic context-free grammars to obtain the algorithm for stochastic macro grammars. We have implemented the algorithm and confirmed its effectiveness through an application to the learning of macro grammars.