Paper: Variational Inference for Adaptor Grammars

ACL ID N10-1081
Title Variational Inference for Adaptor Grammars
Venue Human Language Technologies
Session Main Conference
Year 2010

Adaptor grammars extend probabilistic context-free grammars to define prior dis- tributions 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 al- gorithm 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, depend...