  We present a framework for measuring the compositional inductive bias of
models in the context of emergent communications. We devise corrupted
compositional grammars that probe for limitations in the compositional
inductive bias of frequently used models. We use these corrupted compositional
grammars to compare and contrast a wide range of models, and to compare the
choice of soft, Gumbel, and discrete representations. We propose a hierarchical
model which might show an inductive bias towards relocatable atomic groups of
tokens, thus potentially encouraging the emergence of words. We experiment with
probing for the compositional inductive bias of sender and receiver networks in
isolation, and also placed end-to-end, as an auto-encoder.
