Keywords: compositionality, signaling games, noisy channel, deep learning
TL;DR: We show that inductive biases are needed for the emergence of compositional communication and that it emerges in signaling games in which agents communicate over a noisy channel.
Abstract: Communication is compositional if complex signals can be represented as a combination of simpler subparts. In this paper, we theoretically show that inductive biases on both the training framework and the data are needed to develop a compositional communication. Moreover, we prove that compositionality spontaneously arises in the signaling games, where agents communicate over a noisy channel. We experimentally confirm that a range of noise levels, which depends on the model and the data, indeed promotes compositionality. Finally, we provide a comprehensive study of this dependence and report results in terms of recently studied compositionality metrics: topographical similarity, conflict count, and context independence.
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