Abstract
Backoff smoothing and topic modeling are crucial issues in n-gram language model. This paper presents a Bayesian non-parametric learning approach to tackle these two issues. We develop a topic-based language model where the numbers of topics and n-grams are automatically determined from data. To cope with this model selection problem, we introduce the nonparametric priors for topics and backoff n-grams. The infinite language models are constructed through the hierarchical Dirichlet process compound Pitman-Yor (PY) process. We develop the topic-based hierarchical PY language model (THPY-LM) with power-law behavior. This model can be simplified to the hierarchical PY (HPY) LM by disregarding the topic information and also the modified Kneser-Ney (MKN) LM by further disregarding the Bayesian treatment. In the experiments, the proposed THPY-LM outperforms state-of-art methods using MKN-LM and HPY-LM.
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