AI summary

The paper introduces a novel approach to language modeling by combining various advanced techniques such as class-based models, cache models, maximum entropy models, structured language models, random forest language models, and neural network-based language models. By using linear interpolation the model achieves state-of-the-art perplexity reduction on the Penn Treebank corpus.

Abstract

We present results obtained with several advanced language modeling techniques, including class based model, cache model, maximum entropy model, structured language model, random forest language model and several types of neural network based language models. We show results obtained after combining all these models by using linear interpolation. We conclude that for both small and moderately sized tasks, we obtain new state of the art results with combination of models, that is significantly better than performance of any individual model. Obtained perplexity reductions against Good-Turing trigram baseline are over 50% and against modified Kneser-Ney smoothed 5-gram over 40%.