Online Adaptor Grammars with Hybrid Inference
Ke Zhai, Jordan Lee Boyd-Graber, Shay B. Cohen · Transactions of the Association for Computational Linguistics · 2014
Adaptor grammars are a flexible, powerful formalism for defining nonparametric, unsupervised models of grammar productions. This flexibility comes at the cost of expensive inference. We address the difficulty of inference through an online algorithm which uses a hybrid of Markov chain Monte Carlo and variational inference. We show that this inference strategy improves scalability without sacrificing performance on unsupervised word segmentation and topic modeling tasks.