论文标题
使用概率模型的语言学家喜欢它们：功能分布语义中的量化
Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics
论文作者
论文摘要
功能分布语义提供了一个可从语料库学习真实条件语义的计算障碍框架。此框架中的先前工作提供了一阶逻辑的概率版本，将定量重新铸造为贝叶斯推断。在本文中，我展示了当精确的量词与模糊谓词一起使用时，先前的公式如何给出微不足道的真实价值。我提出了一个改进的帐户，通过将模糊的谓词视为精确谓词的分布来避免此问题。我将此帐户连接到有关建模通用量化的《理性语音法案》框架中的最新工作，然后将其扩展到建模驴句。最后，我解释了通用量词如何务实地务实地复杂，却比精确的量词更简单。
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.
