  Models of context-sensitive communication often use the Rational Speech Act
framework (RSA; Frank & Goodman, 2012), which formulates listeners and speakers
in a cooperative reasoning process. However, the standard RSA formulation can
only be applied to small domains, and large-scale applications have relied on
imitating human behavior. Here, we propose a new approach to scalable
pragmatics, building upon recent theoretical results (Zaslavsky et al., 2020)
that characterize pragmatic reasoning in terms of general information-theoretic
principles. Specifically, we propose an architecture and learning process in
which agents acquire pragmatic policies via self-supervision instead of
imitating human data. This work suggests a new principled approach for
equipping artificial agents with pragmatic skills via self-supervision, which
is grounded both in pragmatic theory and in information theory.
