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

Neural networks can understand compositional functions that humans do not, in the context of emergent communication