  We propose a new STAckable Recurrent cell (STAR) for recurrent neural
networks (RNNs), which has fewer parameters than widely used LSTM and GRU while
being more robust against vanishing or exploding gradients. Stacking recurrent
units into deep architectures suffers from two major limitations: (i) many
recurrent cells (e.g., LSTMs) are costly in terms of parameters and computation
resources; and (ii) deep RNNs are prone to vanishing or exploding gradients
during training. We investigate the training of multi-layer RNNs and examine
the magnitude of the gradients as they propagate through the network in the
"vertical" direction. We show that, depending on the structure of the basic
recurrent unit, the gradients are systematically attenuated or amplified. Based
on our analysis we design a new type of gated cell that better preserves
gradient magnitude. We validate our design on a large number of sequence
modelling tasks and demonstrate that the proposed STAR cell allows to build and
train deeper recurrent architectures, ultimately leading to improved
performance while being computationally more efficient.
