  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.

Machine Learning;Artificial Intelligence;Machine Learning