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.
|頁（從 - 到）||2212-2216|
|期刊||Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH|
|出版狀態||Published - 1 一月 2013|
|事件||14th Annual Conference of the International Speech Communication Association, INTERSPEECH 2013 - Lyon, France|
持續時間: 25 八月 2013 → 29 八月 2013