Type-based MCMC for Sampling Tree Fragments from Forests
Xiaochang Peng, Daniel Gildea · 2014
This paper applies type-based Markov Chain Monte Carlo (MCMC) algorithms to the problem of learning Synchronous Context-Free Grammar (SCFG) rules from a forest that represents all possible rules consistent with a fixed word align-ment. While type-based MCMC has been shown to be effective in a number of NLP applications, our setting, where the tree structure of the sentence is itself a hid-den variable, presents a number of chal-lenges to type-based inference. We de-scribe methods for defining variable types and efficiently indexing variables in or-der to overcome these challenges. These methods lead to improvements in both log likelihood and BLEU score in our experi-ments.