Variational Inference for Adaptor Grammars

S. B. Cohen, D. M. Blei, N. A. Smith

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Adaptor grammars extend probabilistic context-free grammars to define prior distributions over trees with ``rich get richer'' dynamics. Inference for adaptor grammars seeks to find parse trees for raw text. This paper describes a variational inference algorithm for adaptor grammars, providing an alternative to Markov chain Monte Carlo methods. To derive this method, we develop a stick-breaking representation of adaptor grammars, a representation that enables us to define adaptor grammars with recursion. We report experimental results on a word segmentation task, showing that variational inference performs comparably to MCMC. Further, we show a significant speed-up when parallelizing the algorithm. Finally, we report promising results for a new application for adaptor grammars, dependency grammar induction.
Original languageEnglish
Title of host publicationProceedings of NAACL
Pages564-572
Number of pages9
Publication statusPublished - 2010

Fingerprint Dive into the research topics of 'Variational Inference for Adaptor Grammars'. Together they form a unique fingerprint.

Cite this