Abstract

This paper introduces lattice based language models, a new language modeling paradigm. These models construct multi-dimensional hierarchies of partitions and select the most promising partitions to generate the estimated distributions. We discussed a specific two dimensional lattice and propose two primary features to measure the usefulness of each node: the training-set history count and the smoothed entropy of its prediction. Smoothing techniques are reviewed and a generalization of the conventional backoff strategy to multiple dimensions is proposed. Preliminary experimental results are obtained on the SWITCHBOARD corpus which lead to a 6.5 % perplexity reduction over a word trigram model. Project sponsored by the National Security Agency under Grant No. MDA904-97-10006. The United States Government is authorized to reproduce and distribute reprints notwithstanding any copyright notation hereon. y Current address: D'ept. Math., Universit'e Jean Monnet, 23, rue P. Michelon, 42023 S...

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