  Multiple extensions of Recurrent Neural Networks (RNNs) have been proposed
recently to address the difficulty of storing information over long time
periods. In this paper, we experiment with the capacity of Neural Turing
Machines (NTMs) to deal with these long-term dependencies on well-balanced
strings of parentheses. We show that not only does the NTM emulate a stack with
its heads and learn an algorithm to recognize such words, but it is also
capable of strongly generalizing to much longer sequences.
