  Recurrent Neural Networks (RNNs) continue to show outstanding performance in
sequence modeling tasks. However, training RNNs on long sequences often face
challenges like slow inference, vanishing gradients and difficulty in capturing
long term dependencies. In backpropagation through time settings, these issues
are tightly coupled with the large, sequential computational graph resulting
from unfolding the RNN in time. We introduce the Skip RNN model which extends
existing RNN models by learning to skip state updates and shortens the
effective size of the computational graph. This model can also be encouraged to
perform fewer state updates through a budget constraint. We evaluate the
proposed model on various tasks and show how it can reduce the number of
required RNN updates while preserving, and sometimes even improving, the
performance of the baseline RNN models. Source code is publicly available at
https://imatge-upc.github.io/skiprnn-2017-telecombcn/ .
