Title: Persistent Hidden States and Nonlinear Transformation for Long Short-Term Memory,
Abstract: Recurrent neural networks (RNNs) have been drawing much attention with great
success in many applications like speech recognition and neural machine
translation. Long short-term memory (LSTM) is one of the most popular RNN units
in deep learning applications. LSTM transforms the input and the previous
hidden states to the next states with the affine transformation, multiplication
operations and a nonlinear activation function, which makes a good data
representation for a given task. The affine transformation includes rotation
and reflection, which change the semantic or syntactic information of
dimensions in the hidden states. However, considering that a model interprets
the output sequence of LSTM over the whole input sequence, the dimensions of
the states need to keep the same type of semantic or syntactic information
regardless of the location in the sequence. In this paper, we propose a simple
variant of the LSTM unit, persistent recurrent unit (PRU), where each dimension
of hidden states keeps persistent information across time, so that the space
keeps the same meaning over the whole sequence. In addition, to improve the
nonlinear transformation power, we add a feedforward layer in the PRU
structure. In the experiment, we evaluate our proposed methods with three
different tasks, and the results confirm that our methods have better
performance than the conventional LSTM.