Doctoral Dissertation Research: Developing a scalable theory of alternatives in pragmatics

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。人类以非常灵活的方式解释语言。特别是，我们的解释不仅涉及说话者实际说了什么，还包括说话者本来可以说但没有说的话。例如，如果有人说“一些学生通过了考试”，听众可能会认为这意味着并不是所有的学生都通过了，因为如果是这样的话，说话者本可以使用信息量更大的替代方案“所有学生都通过了考试”。关于这些语言选择的推理似乎在语言理解中起着关键作用，但还不清楚这些选择本身到底是如何决定的。当演讲者说什么时，是什么让一句话比另一句话更好或更差？备选方案如何依赖于不同语法结构的先验经验？孩子们如何才能学会对其他选择进行推理呢？本研究项目通过结合语言学理论、行为实验、概率建模和机器学习来研究这些问题，以检验备选方案的竞争理论。通过这样做，这项工作有可能促进我们对人类思维中的语言以及以类似人类的方式使用语言的人工模型的理解。该项目还为本科生研究人员创造了机会，并产生了新的代码和数据集，将向更广泛的科学界公开发布。与现有理论认为替代方案是通过对句法结构的操作生成的不同，本研究评估了替代方案捕捉词汇、语法和上下文之间基于经验的可及性关系的想法。一系列有计划的行为实验评估了替代项的可及性如何影响英语周边致使结构中事件使役的解释。此外，模型模拟调查了替代方案的知识如何在语言学习过程中出现，同时建立了一个框架，用于在更具生态有效性的领域测试替代方案的理论。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Humans interpret language in remarkably flexible ways. In particular, our interpretations involve not only what a speaker actually says, but also what the speaker could have said but didn't. For example, if someone says "some students passed the exam," listeners probably take this to mean that not all students passed, because the speaker could have used the more informative alternative "all students passed the exam" if that had been the case. Reasoning about these linguistic alternatives appears to play a key role in language comprehension, but it remains unclear how exactly the alternatives themselves are determined. When a speaker says something, what makes one sentence a better or worse alternative than another? How do alternatives depend on prior experience with different grammatical structures? And how might children learn the ability to reason about alternatives? This research project investigates these questions by combining linguistic theory, behavioral experiments, probabilistic modeling, and machine learning to test competing theories of alternatives. In doing so, this work has the potential to advance our understanding of language in the human mind, as well as artificial models that use language in human-like ways. The project also establishes opportunities for undergraduate researchers, and produce new code and datasets that will be publicly released to the broader scientific community.In contrast to existing theories which suggest that alternatives are generated through operations on syntactic structures, this research evaluates the idea that alternatives capture experience-based accessibility relationships between the lexicon, grammar, and context. A planned series of behavioral experiments assess how the accessibility of alternatives affects the interpretation of event causation in English periphrastic causative constructions. In addition, model simulations investigate how knowledge of alternatives may emerge over the course of language learning, simultaneously establishing a framework for testing theories of alternatives in more ecologically valid domains.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.