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

arXiv: https://arxiv.org/abs/2006.03002 · HTML: https://mlsanity.com/paper/2006.03002/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [Learning Functional Distributional Semantics with Visual Data](https://mlsanity.com/paper/2204.10624/) (2022)

## Abstract (arXiv)

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