  Recurrent neural network (RNN) has been widely studied in sequence learning
tasks, while the mainstream models (e.g., LSTM and GRU) rely on the gating
mechanism (in control of how information flows between hidden states). However,
the vanilla gates in RNN (e.g., the input gate in LSTM) suffer from the problem
of gate undertraining, which can be caused by various factors, such as the
saturating activation functions, the gate layouts (e.g., the gate number and
gating functions), or even the suboptimal memory state etc.. Those may result
in failures of learning gating switch roles and thus the weak performance. In
this paper, we propose a new gating mechanism within general gated recurrent
neural networks to handle this issue. Specifically, the proposed gates directly
short connect the extracted input features to the outputs of vanilla gates,
denoted as refined gates. The refining mechanism allows enhancing gradient
back-propagation as well as extending the gating activation scope, which can
guide RNN to reach possibly deeper minima. We verify the proposed gating
mechanism on three popular types of gated RNNs including LSTM, GRU and MGU.
Extensive experiments on 3 synthetic tasks, 3 language modeling tasks and 5
scene text recognition benchmarks demonstrate the effectiveness of our method.
