Parsing low-resource languages using Gibbs sampling for PCFGs with latent annotations

PCFGs with latent annotations have been shown to be a very effective model for phrase structure parsing. We present a Bayesian model and algorithms based on a Gibbs sampler for parsing with a grammar with latent annotations. For PCFG-LA, we present an additional Gibbs sampler algorithm to learn annotations from training data, which are parse trees with coarse (unannotated) symbols. We show that a Gibbs sampling technique is capable of parsing sentences in a wide variety of languages and producing results that are on-par with or surpass previous approaches. Our results for Kinyarwanda and Malagasy in particular demonstrate that low-resource language parsing can benefit substantially from a Bayesian approach.