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Recurrent Neural Networks (RNNs) have dominated language modeling because oftheir superior performance over traditional N-gram based models. In manyapplications, a large Recurrent Neural Network language model (RNNLM) or anensemble of several RNNLMs is used. These models have large memory footprintsand require heavy computation. In this paper, we examine the effect of applyingknowledge distillation in reducing the model size for RNNLMs. In addition, wepropose a trust regularization method to improve the knowledge distillationtraining for RNNLMs. Using knowledge distillation with trust regularization, wereduce the parameter size to a third of that of the previously published bestmodel while maintaining the state-of-the-art perplexity result on Penn Treebankdata. In a speech recognition N-bestrescoring task, we reduce the RNNLM modelsize to 18.5% of the baseline system, with no degradation in word errorrate(WER) performance on Wall Street Journal data set.

Knowledge Distillation For Recurrent Neural Network Language Modeling With Trust Regularization