Adaptor grammars are a flexible, powerful formalism for defining nonparametric, un-supervised models of grammar productions. This flexibility comes at the cost of expensive inference. We address the difficulty of infer-ence through an online algorithm which uses a hybrid of Markov chain Monte Carlo and variational inference. We show that this in-ference strategy improves scalability without sacrificing performance on unsupervised word segmentation and topic modeling tasks.
