Paper: Collapsed Variational Bayesian Inference for PCFGs

ACL ID W13-3519
Title Collapsed Variational Bayesian Inference for PCFGs
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2013

This paper presents a collapsed variational Bayesian inference algorithm for PCFGs that has the advantages of two dominant Bayesian training algorithms for PCFGs, namely variational Bayesian inference and Markov chain Monte Carlo. In three kinds of experiments, we illustrate that our al- gorithm achieves close performance to the Hastings sampling algorithm while using an order of magnitude less training time; and outperforms the standard variational Bayesian inference and the EM algorithms with similar training time.