Warm (for winter): Comparison class understanding in vague language

Speakers often refer to context only implicitly when using language. The utterance “it’s warm outside” could signal it’s warm relative to other days of the year or just relative to the current season (e.g., it’s warm for winter). Warm vaguely conveys that the temperature is high relative to some contextual comparison class, but little is known about how a listener decides upon such a standard of comparison. Here, we formalize how world knowledge and listeners’ internal models of speech production can drive the resolution of a comparison class in context. We introduce a Rational Speech Act model and derive two novel predictions from it, which we validate using a paraphrase experiment to measure listeners’ beliefs about the likely comparison class used by a speaker. Our model makes quantitative predictions given prior world knowledge for the domains in question. We triangulate this knowledge with a follow-up language task in the same domains, using Bayesian data analysis to infer priors from both data sets.