Highway State Gating for Recurrent Highway Networks: improving information flow through time

TL;DR

This paper introduces Highway State Gating, a novel mechanism for Recurrent Highway Networks that enhances information flow through time, enabling deeper models to improve performance and converge faster.

Contribution

The paper proposes Highway State Gating, a simple variation that allows deeper RNNs to maintain performance and improve training efficiency by better managing information flow.

Findings

HSG improves performance across all depths of RHNs.

Deeper RHNs with HSG outperform previous models.

HSG enables faster convergence during training.

Abstract

Recurrent Neural Networks (RNNs) play a major role in the field of sequential learning, and have outperformed traditional algorithms on many benchmarks. Training deep RNNs still remains a challenge, and most of the state-of-the-art models are structured with a transition depth of 2-4 layers. Recurrent Highway Networks (RHNs) were introduced in order to tackle this issue. These have achieved state-of-the-art performance on a few benchmarks using a depth of 10 layers. However, the performance of this architecture suffers from a bottleneck, and ceases to improve when an attempt is made to add more layers. In this work, we analyze the causes for this, and postulate that the main source is the way that the information flows through time. We introduce a novel and simple variation for the RHN cell, called Highway State Gating (HSG), which allows adding more layers, while continuing to improve performance. By using a gating mechanism for the state, we allow the net to "choose" whether to pass information directly through time, or to gate it. This mechanism also allows the gradient to back-propagate directly through time and, therefore, results in a slightly faster convergence. We use the Penn Treebank (PTB) dataset as a platform for empirical proof of concept. Empirical results show that the improvement due to Highway State Gating is for all depths, and as the depth increases, the improvement also increases.