  Humans often communicate by using imprecise language, suggesting that fuzzy
concepts with unclear boundaries are prevalent in language use. In this paper,
we test the extent to which models trained to capture the distributional
statistics of language show correspondence to fuzzy-membership patterns. Using
the task of natural language inference, we test a recent state of the art model
on the classical case of temperature, by examining its mapping of temperature
data to fuzzy-perceptions such as "cool", "hot", etc. We find the model to show
patterns that are similar to classical fuzzy-set theoretic formulations of
linguistic hedges, albeit with a substantial amount of noise, suggesting that
models trained solely on language show promise in encoding fuzziness.
