@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://preview.aclanthology.org/add-emnlp-2024-awards/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."
}