使用概率模型的语言学家喜欢它们：功能分布语义学中的量化（CS CL）---蔡秋纯

功能分布语义为从语料库中学习真值条件语义提供了一个可计算处理的框架。在此框架中的先前工作提供了一阶逻辑的概率版本，将量化重铸为贝叶斯推理。在这篇文章中，我展示了当一个精确的量词与vague谓词一起使用时，前面的公式如何给出平凡的真值。我提出了一个改进的方案，通过将vague谓词视为精确谓词上的分布来避免这个问题。我将这篇文章与最近在《理性言语行为框架》中关于建立一般量化模型的工作联系起来，并将其扩展到建立驴句模型。最后，我解释了泛型量词在语用上是如何复杂的，但在计算上却比精确量词简单。

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

原文：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.

原文作者：Guy Emerson

原文地址：https://arxiv.org/abs/2006.03002