Hierarchical pitman-yor and dirichlet process for language model

Jen-Tzung Chien*, Ying Lan Chang

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

This paper presents a nonparametric interpretation for modern language model based on the hierarchical Pitman-Yor and Dirichlet (HPYD) process. We propose the HPYD language model (HPYD-LM) which flexibly conducts backoff smoothing and topic clustering through Bayesian nonparametric learning. The nonparametric priors of backoff n-grams and latent topics are tightly coupled in a compound process. A hybrid probability measure is drawn to build the smoothed topic-based LM. The model structure is automatically determined from training data. A new Chinese restaurant scenario is proposed to implement HPYD-LM via Gibbs sampling. This process reflects the power-law property and extracts the semantic topics from natural language. The superiority of HPYD-LM to the related LMs is demonstrated by the experiments on different corpora in terms of perplexity and word error rate.

Original languageEnglish
Pages (from-to)2212-2216
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
StatePublished - 1 Jan 2013
Event14th Annual Conference of the International Speech Communication Association, INTERSPEECH 2013 - Lyon, France
Duration: 25 Aug 201329 Aug 2013

Keywords

  • Backoff model
  • Bayesian learning
  • Language model
  • Topic model

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