Dense Recurrent Neural Network with Attention Gate

Yong-Ho Yoo, Kook Han, Sanghyun Cho, Kyoung-Chul Koh, Jong-Hwan Kim

Abstract: We propose the dense RNN, which has the fully connections from each hidden state to multiple preceding hidden states of all layers directly. As the density of the connection increases, the number of paths through which the gradient flows can be increased. It increases the magnitude of gradients, which help to prevent the vanishing gradient problem in time. Larger gradients, however, can also cause exploding gradient problem. To complement the trade-off between two problems, we propose an attention gate, which controls the amounts of gradient flows. We describe the relation between the attention gate and the gradient flows by approximation. The experiment on the language modeling using Penn Treebank corpus shows dense connections with the attention gate improve the model’s performance.

TL;DR: Dense RNN that has fully connections from each hidden state to multiple preceding hidden states of all layers directly.

Keywords: recurrent neural network, language modeling, dense connection