  Natural language has the universal properties of being compositional and
grounded in reality. The emergence of linguistic properties is often
investigated through simulations of emergent communication in referential
games. However, these experiments have yielded mixed results compared to
similar experiments addressing linguistic properties of human language. Here we
address representational alignment as a potential contributing factor to these
results. Specifically, we assess the representational alignment between agent
image representations and between agent representations and input images. Doing
so, we confirm that the emergent language does not appear to encode human-like
conceptual visual features, since agent image representations drift away from
inputs whilst inter-agent alignment increases. We moreover identify a strong
relationship between inter-agent alignment and topographic similarity, a common
metric for compositionality, and address its consequences. To address these
issues, we introduce an alignment penalty that prevents representational drift
but interestingly does not improve performance on a compositional
discrimination task. Together, our findings emphasise the key role
representational alignment plays in simulations of language emergence.
