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We propose an architecture and process for using the Iterated Learning Model("ILM") for artificial neural networks. We show that ILM does not lead to thesame clear compositionality as observed using DCGs, but does lead to a modestimprovement in compositionality, as measured by holdout accuracy and topologicsimilarity. We show that ILM can lead to an anti-correlation between holdoutaccuracy and topologic rho. We demonstrate that ILM can increasecompositionality when using non-symbolic high-dimensional images as input.

Compositionality Through Language Transmission, using Artificial Neural Networks