Wang Daphne, Sadrzadeh Mehrnoosh
Department of Computer Science, University College London, London, UK.
Philos Trans A Math Phys Eng Sci. 2024 Mar 18;382(2268):20230013. doi: 10.1098/rsta.2023.0013. Epub 2024 Jan 29.
Sheaves are mathematical objects that describe the globally compatible data associated with open sets of a topological space. Original examples of sheaves were continuous functions; later they also became powerful tools in algebraic geometry, as well as logic and set theory. More recently, sheaves have been applied to the theory of contextuality in quantum mechanics. Whenever the local data are not necessarily compatible, sheaves are replaced by the simpler setting of presheaves. In previous work, we used presheaves to model lexically ambiguous phrases in natural language and identified the order of their disambiguation. In the work presented here, we model syntactic ambiguities and study a phenomenon in human parsing called garden-pathing. It has been shown that the information-theoretic quantity known as 'surprisal' correlates with human reading times in natural language but fails to do so in garden-path sentences. We compute the degree of signalling in our presheaves using probabilities from the large language model BERT and evaluate predictions on two psycholinguistic datasets. Our degree of signalling outperforms surprisal in two ways: (i) it distinguishes between hard and easy garden-path sentences (with a [Formula: see text]-value [Formula: see text]), whereas existing work could not, (ii) its garden-path effect is larger in one of the datasets (32 ms versus 8.75 ms per word), leading to better prediction accuracies. This article is part of the theme issue 'Quantum contextuality, causality and freedom of choice'.
层是一种数学对象,用于描述与拓扑空间的开集相关的全局兼容数据。层的原始例子是连续函数;后来它们在代数几何以及逻辑和集合论中也成为了强大的工具。最近,层已被应用于量子力学中的语境性理论。只要局部数据不一定兼容,层就会被更简单的预层设置所取代。在之前的工作中,我们使用预层对自然语言中的词汇歧义短语进行建模,并确定了它们的歧义消解顺序。在本文所展示的工作中,我们对句法歧义进行建模,并研究人类句法分析中一种称为“花园路径”的现象。研究表明,被称为“意外性”的信息理论量与自然语言中的人类阅读时间相关,但在花园路径句子中却并非如此。我们使用来自大语言模型BERT的概率来计算预层中的信号传递程度,并在两个心理语言学数据集上评估预测结果。我们的信号传递程度在两个方面优于意外性:(i)它能够区分难和易的花园路径句子([公式:见正文]值为[公式:见正文]),而现有工作无法做到这一点;(ii)在其中一个数据集中,它的花园路径效应更大(每个单词为32毫秒,而之前为8.75毫秒),从而带来更好的预测准确率。本文是主题为“量子语境性、因果性和选择自由”的一部分。