  We show that it is possible to craft transformations that, applied to
compositional grammars, result in grammars that neural networks can learn
easily, but humans do not. This could explain the disconnect between current
metrics of compositionality, that are arguably human-centric, and the ability
of neural networks to generalize to unseen examples. We propose to use the
transformations as a benchmark, ICY, which could be used to measure aspects of
the compositional inductive bias of networks, and to search for networks with
similar compositional inductive biases to humans. As an example of this
approach, we propose a hierarchical model, HU-RNN, which shows an inductive
bias towards position-independent, word-like groups of tokens.
