Title: Simplified Gating in Long Short-term Memory (LSTM) Recurrent Neural Networks,
Abstract: The standard LSTM recurrent neural networks while very powerful in long-range
dependency sequence applications have highly complex structure and relatively
large (adaptive) parameters. In this work, we present empirical comparison
between the standard LSTM recurrent neural network architecture and three new
parameter-reduced variants obtained by eliminating combinations of the input
signal, bias, and hidden unit signals from individual gating signals. The
experiments on two sequence datasets show that the three new variants, called
simply as LSTM1, LSTM2, and LSTM3, can achieve comparable performance to the
standard LSTM model with less (adaptive) parameters.