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Poster
Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication
Łukasz Kuciński · Tomasz Korbak · Paweł Kołodziej · Piotr Miłoś

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @ None #None
in Poster Session 5 »

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.

Keywords: Deep Learning · Graph Learning

Author Information

Łukasz Kuciński (Polish Academy of Sciences)
Tomasz Korbak (University of Sussex)
Paweł Kołodziej (Google)
Piotr Miłoś (Polish Academy of Sciences, University of Oxford)

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