  Neural networks are very powerful learning systems, but they do not readily
generalize from one task to the other. This is partly due to the fact that they
do not learn in a compositional way, that is, by discovering skills that are
shared by different tasks, and recombining them to solve new problems. In this
paper, we explore the compositional generalization capabilities of recurrent
neural networks (RNNs). We first propose the lookup table composition domain as
a simple setup to test compositional behaviour and show that it is
theoretically possible for a standard RNN to learn to behave compositionally in
this domain when trained with standard gradient descent and provided with
additional supervision. We then remove this additional supervision and perform
a search over a large number of model initializations to investigate the
proportion of RNNs that can still converge to a compositional solution. We
discover that a small but non-negligible proportion of RNNs do reach partial
compositional solutions even without special architectural constraints. This
suggests that a combination of gradient descent and evolutionary strategies
directly favouring the minority models that developed more compositional
approaches might suffice to lead standard RNNs towards compositional solutions.
