Resolving (non-) exhaustivity in questions: experimental and computational pragmatics

该奖项是国家科学基金会社会、行为和经济科学（SBE）博士后研究奖学金（SPRF）项目和SBE语言学项目的一部分。SPRF计划的目标是为学术界、工业界或私营部门和政府的科学事业准备有前途的早期职业博士级科学家。SPRF奖励包括在知名科学家的赞助下进行为期两年的培训，并鼓励博士后进行独立研究。美国国家科学基金会寻求促进科学界各阶层的科学家，包括那些未被充分代表的群体的科学家，参与其研究项目和活动；博士后阶段被认为是实现这一目标的一个重要的专业发展阶段。每个博士后必须解决各自学科领域的重要科学问题。在斯坦福大学朱迪思·M·德根博士的赞助下，这个博士后奖学金奖支持一位研究语言语义和语用学的早期职业科学家。语言话语的含义往往在几个方面被低估。语用学起着至关重要的作用，它为听者提供信息，听者可以利用这些信息来确定说话人话语的意义，然后决定她下一步的会话动作。本项目分析了语言因素（说话人对词汇项目的选择和问题的句法结构）和语言外因素（话语语境和说话人的目标）对解决这种不规范问题的影响。它将提出关于问答动态中语法、语义和语用之间分工的理论建议，并将提供有关在理解上下文敏感话语时整合语言和语言外信息的辩论的经验数据。该项目将为本科生提供宝贵的机会，从实验设计和运行到在学术场所展示结果。该项目还将直接影响服务不足的人群，因为它将雇用代表性不足的本科RAs进行语料库注释。理解问题和上下文如何相互作用也适用于智能会话代理的开发，这是人工智能研究的核心目标。本研究以新颖的方式将语料库、心理语言学和计算建模相结合。互补方法从不同的角度来研究这一现象：大规模语料库分析、答案评级任务、目标推理任务和使用计算认知模型的系统理论比较。继Degen（2013, 2015）之后，语料库分析量化了语言和上下文线索对（非）穷竭性的贡献。这将通过测试自然发生的产物和（非）穷竭性判断之间的联系，阐明语境和先验世界知识在意义中的作用。此外，计算语用建模的进展为评估关于语言、认知和经验相互作用的假设提供了有用的工具。在基于语料库的研究中收集的数据将推动认知模型之间的系统模型比较，这些模型提供了不同背景作用的理论的形式化。目前很少有人从这个角度来研究问题解释。本研究利用实验和计算方法对语用机制进行深入研究，从而解决语言学和认知科学中涉及语义表征与语用能力之间相互作用的基本问题。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award was provided as part of NSF's Social, Behavioral and Economic Sciences (SBE) Postdoctoral Research Fellowships (SPRF) program and SBE's Linguistics program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Judith M Degen at Stanford University, this postdoctoral fellowship award supports an early career scientist investigating the semantics and pragmatics of language. Linguistic utterances are often underspecified for their meaning in several ways. Pragmatics plays the crucial role of providing information that the hearer can exploit to determine the meaning of the speaker's utterance, and then determine her next conversational move. This project analyzes the linguistic (the speaker’s choice of lexical items and the syntactic structure of the question) and extra-linguistic (the discourse context and the speaker’s goals) factors that influence the resolution of such underspecification. It will address theoretical proposals regarding the division of labor between syntax, semantics, and pragmatics in question-answer dynamics, and will provide empirical data that bear on debates concerning the integration of linguistic and extra-linguistic information in the understanding of context-sensitive utterances. The project will provide valuable opportunities for undergraduate students, from experimental design and running to the presentation of results in scholarly venues. This project will also directly impact underserved populations because it will employ underrepresented undergraduate RAs for corpus annotation purposes. Understanding how questions and context interact also has applications in the development of intelligent conversational agents, a core goal in AI research.The proposed research integrates corpus, psycholinguistic and computational modeling in a novel way. The complementary methods approach the phenomenon from different angles: a large-scale corpus analysis, an answer-rating task, a goal-inference task, and systematic theory comparison using computational cognitive models. Following Degen (2013, 2015), the corpus analysis quantifies the contribution of linguistic and contextual cues to (non-)exhaustivity. This will elucidate the role of context and prior world knowledge in meaning, by testing the link between the naturally occurring productions and judgements of (non-)exhaustivity. Further, advances in computational pragmatic modeling provide a useful tool for evaluating hypotheses about the interaction of language, cognition and experience. Systematic model comparison between cognitive models that offer formalizations of proposed theories that differ in the role of context, will be driven by the data collected in the corpus-based studies. There are currently few accounts which examine question interpretation in this particular light. The proposed research puts meat on the bones of pragmatic mechanisms using experimental and computational methodologies, thereby addressing fundamental questions in both linguistics and cognitive science involving the interaction between semantic representations and our pragmatic abilities.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.