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    <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.</abstract>
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%0 Journal Article
%T Online Adaptor Grammars with Hybrid Inference
%A Zhai, Ke
%A Boyd-Graber, Jordan
%A Cohen, Shay B.
%J Transactions of the Association for Computational Linguistics
%D 2014
%V 2
%I MIT Press
%C Cambridge, MA
%F zhai-etal-2014-online
%X 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.
%R 10.1162/tacl_a_00196
%U https://aclanthology.org/Q14-1036
%U https://doi.org/10.1162/tacl_a_00196
%P 465-476

Markdown (Informal)

[Online Adaptor Grammars with Hybrid Inference](https://aclanthology.org/Q14-1036) (Zhai et al., TACL 2014)

• Online Adaptor Grammars with Hybrid Inference (Zhai et al., TACL 2014)

ACL

• Ke Zhai, Jordan Boyd-Graber, and Shay B. Cohen. 2014. Online Adaptor Grammars with Hybrid Inference. Transactions of the Association for Computational Linguistics, 2:465–476.