Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models
Abstract
This paper introduces adaptor grammars, a class of probabilistic models of lan- guage that generalize probabilistic context-free grammars (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 processes 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 illustrate how several existing nonparametric Bayesian models can be expressed within this framework.
Cite
Text
Johnson et al. "Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models." Neural Information Processing Systems, 2006.Markdown
[Johnson et al. "Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models." Neural Information Processing Systems, 2006.](https://mlanthology.org/neurips/2006/johnson2006neurips-adaptor/)BibTeX
@inproceedings{johnson2006neurips-adaptor,
title = {{Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models}},
author = {Johnson, Mark and Griffiths, Thomas L. and Goldwater, Sharon},
booktitle = {Neural Information Processing Systems},
year = {2006},
pages = {641-648},
url = {https://mlanthology.org/neurips/2006/johnson2006neurips-adaptor/}
}