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

Guy Emerson


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.
Anthology ID:
2020.pam-1.6
Volume:
Proceedings of the Probability and Meaning Conference (PaM 2020)
Month:
June
Year:
2020
Address:
Gothenburg
Venue:
PaM
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
41–52
Language:
URL:
https://aclanthology.org/2020.pam-1.6
DOI:
Bibkey:
Cite (ACL):
Guy Emerson. 2020. Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics. In Proceedings of the Probability and Meaning Conference (PaM 2020), pages 41–52, Gothenburg. Association for Computational Linguistics.
Cite (Informal):
Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics (Emerson, PaM 2020)
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PDF:
https://aclanthology.org/2020.pam-1.6.pdf