Collapsed Variational Bayesian Inference for PCFGs

摘要

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 algorithm 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.