@inproceedings{emerson-2020-linguists,
title = "Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics",
author = "Emerson, Guy",
editor = "Howes, Christine and
Chatzikyriakidis, Stergios and
Ek, Adam and
Somashekarappa, Vidya",
booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
month = jun,
year = "2020",
address = "Gothenburg",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.pam-1.6",
pages = "41--52",
abstract = "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.",
}
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%0 Conference Proceedings
%T Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics
%A Emerson, Guy
%Y Howes, Christine
%Y Chatzikyriakidis, Stergios
%Y Ek, Adam
%Y Somashekarappa, Vidya
%S Proceedings of the Probability and Meaning Conference (PaM 2020)
%D 2020
%8 June
%I Association for Computational Linguistics
%C Gothenburg
%F emerson-2020-linguists
%X 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.
%U https://aclanthology.org/2020.pam-1.6
%P 41-52
Markdown (Informal)
[Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics](https://aclanthology.org/2020.pam-1.6) (Emerson, PaM 2020)
ACL