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arXiv · 2108.05799

Scalable pragmatic communication via self-supervision

Jennifer Hu·Roger Levy·Noga Zaslavsky
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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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Jennifer Hu, Roger Levy, Noga Zaslavsky. 2021-08-12. Scalable pragmatic communication via self-supervision. https://arxiv.org/abs/2108.05799

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