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:
- 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)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2020.pam-1.6.pdf