Preserving activations in recurrent neural networks based on surprisal

Abstract

Learning hierarchical abstractions from sequences is a challenging and open problem for recurrent neural networks (RNNs). This is mainly due to the difficulty of detecting features that span over long time distances with also different frequencies. In this paper, we address this challenge by introducing surprisal-based activation, a novel method to preserve activations and skip updates depending on encoding-based information content. The preserved activations can be considered as temporal shortcuts with perfect memory. We present a preliminary analysis by evaluating surprisal-based activation on language modeling with the Penn Treebank corpus and find that it can improve performance when compared to baseline RNNs and Long Short-Term Memory (LSTM) networks.