A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation


Frank Wood, Yee Whye Teh ;
Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:607-614, 2009.


In this paper we present a doubly hierarchical Pitman-Yor process language model. Its bottom layer of hierarchy consists of multiple hierarchical Pitman-Yor process language models, one each for some number of domains. The novel top layer of hierarchy consists of a mechanism to couple together multiple language models such that they share statistical strength. Intuitively this sharing results in the ?adaptation? of a latent shared language model to each domain. We introduce a general formalism capable of describing the overall model which we call the graphical Pitman-Yor process and explain how to perform Bayesian inference in it. We present encouraging language model domain adaptation results that both illustrate the potential benefits of our new model and suggest new avenues of inquiry.

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