Scalable pragmatic communication via self-supervision
- URL: http://arxiv.org/abs/2108.05799v1
- Date: Thu, 12 Aug 2021 15:28:30 GMT
- Title: Scalable pragmatic communication via self-supervision
- Authors: Jennifer Hu, Roger Levy, Noga Zaslavsky
- Abstract summary: 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.
- Score: 14.01704261285015
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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