AI summary

This paper introduces several modifications to the recurrent neural network language model (RNN LM) to reduce computational complexity. The modifications lead to a 15x speedup in training and testing. The paper also shows the importance of using a backpropagation through time algorithm.

Abstract

We present several modifications of the original recurrent neural network language model (RNN LM). While this model has been shown to significantly outperform many competitive language modeling techniques in terms of accuracy, the remaining problem is the computational complexity. In this work, we show approaches that lead to more than 15 times speedup for both training and testing phases. Next, we show importance of using a backpropagation through time algorithm. An empirical comparison with feedforward networks is also provided. In the end, we discuss possibilities how to reduce the amount of parameters in the model. The resulting RNN model can thus be smaller, faster both during training and testing, and more accurate than the basic one.