  Compositionality is a hallmark of human language that not only enables
linguistic generalization, but also potentially facilitates acquisition. When
simulating language emergence with neural networks, compositionality has been
shown to improve communication performance; however, its impact on imitation
learning has yet to be investigated. Our work explores the link between
compositionality and imitation in a Lewis game played by deep neural agents.
Our contributions are twofold: first, we show that the learning algorithm used
to imitate is crucial: supervised learning tends to produce more average
languages, while reinforcement learning introduces a selection pressure toward
more compositional languages. Second, our study reveals that compositional
languages are easier to imitate, which may induce the pressure toward
compositional languages in RL imitation settings.
