Rapid bayesian learning for recurrent neural network language model

This paper presents Bayesian learning for recurrent neural network language model (RNN-LM). Our goal is to regularize the RNN-LM by compensating for the randomness of the estimated model parameters which is characterized by a Gaussian prior. This model is not only constructed by training the synaptic weight parameters according to the maximum a posteriori criterion but also regularized by estimating the Gaussian hyper-parameter through the type 2 maximum likelihood. However, a critical issue in Bayesian RNN-LM is the heavy computation of Hessian matrix which is formed as the sum of a large amount of outer-products of high-dimensional gradient vectors. We present a rapid approximation to reduce the redundancy due to the curse of dimensionality and speed up the calculation by summing up only the salient outer-products. Experiments on 1B-Word Benchmark, Penn Treebank and World Street Journal corpora show that rapid Bayesian RNN-LM consistently improves the perplexity and word error rate in comparison with standard RNN-LM.