@article{zhai-etal-2014-online,
    title = "Online {A}daptor {G}rammars with Hybrid Inference",
    author = "Zhai, Ke  and
      Boyd-Graber, Jordan  and
      Cohen, Shay B.",
    editor = "Lin, Dekang  and
      Collins, Michael  and
      Lee, Lillian",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "2",
    year = "2014",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/Q14-1036/",
    doi = "10.1162/tacl_a_00196",
    pages = "465--476",
    abstract = "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."
}