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Self-supervised pragmatic reasoning
Jennifer Hu · Roger Levy · Noga Zaslavsky
Event URL: https://eventhosts.gather.town/app/kR7ip0Bhhn8BXuMD/wiml-workshop-2021 »

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. Large-scale applications of RSA have relied on training models to imitate human behaviors using contextually grounded datasets, but collecting such data can be costly. 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.

Author Information

Jennifer Hu (Massachusetts Institute of Technology)
Roger Levy (Massachusetts Institute of Technology)
Noga Zaslavsky (MIT)

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