Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models

This paper introduces adaptor grammars, a class of probabil istic models of lan- guage that generalize probabilistic context-free grammar s (PCFGs). Adaptor grammars augment the probabilistic rules of PCFGs with "adaptors" that can in- duce dependencies among successive uses. With a particular choice of adaptor, based on the Pitman-Yor process, nonparametric Bayesian models of language using Dirichlet processes and hierarchical Dirichlet proc esses can be written as simple grammars. We present a general-purpose inference algorithm for adaptor grammars, making it easy to define and use such models, and ill ustrate how several existing nonparametric Bayesian models can be expressed within this framework.