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Functional Distributional Semantics provides a computationally tractableframework for learning truth-conditional semantics from a corpus. Previous workin this framework has provided a probabilistic version of first-order logic,recasting quantification as Bayesian inference. In this paper, I show how theprevious formulation gives trivial truth values when a precise quantifier isused with vague predicates. I propose an improved account, avoiding thisproblem by treating a vague predicate as a distribution over precisepredicates. I connect this account to recent work in the Rational Speech Actsframework on modelling generic quantification, and I extend this to modellingdonkey sentences. Finally, I explain how the generic quantifier can be bothpragmatically complex and yet computationally simpler than precise quantifiers.

Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics