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

解决问题中的（非）详尽性：实验和计算语用学

• 批准号: 2005042
• 负责人: Morgan Moyer
• 金额: $ 13.8万
• 依托单位: Moyer, Morgan
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2005042&HistoricalAwards=false
• 关键词: Resolving; non; exhaustivity; questions; experimental

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.

该奖项是 NSF 社会、行为和经济科学 (SBE) 博士后研究奖学金 (SPRF) 计划和 SBE 语言学计划的一部分。 SPRF 项目的目标是为学术界、工业界或私营部门以及政府的科学职业培养有前途的早期职业博士级科学家。 SPRF 奖项包括在知名科学家的赞助下进行两年的培训，并鼓励博士后研究员进行独立研究。 NSF 致力于促进科学界各个领域的科学家（包括来自代表性不足群体的科学家）参与其研究计划和活动；博士后时期被认为是实现这一目标的重要专业发展阶段。每个博士后研究员必须解决推动各自学科领域发展的重要科学问题。在斯坦福大学 Judith M Degen 博士的赞助下，该博士后奖学金资助一位研究语言语义和语用学的早期职业科学家。语言话语的含义常常在几个方面未得到明确说明。语用学在提供信息方面发挥着至关重要的作用，听者可以利用这些信息来确定说话者话语的含义，然后确定她的下一步对话动作。该项目分析了影响此类不明确问题解决的语言因素（说话者对词汇项目的选择和问题的句法结构）和语言外因素（话语背景和说话者的目标）。它将提出关于问答动态中句法、语义和语用之间分工的理论建议，并将提供与理解上下文相关话语中的语言和语言外信息整合相关的争论的实证数据。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成果。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 RA 进行语料库注释。了解问题和上下文如何交互也可应用于智能会话代理的开发，这是人工智能研究的核心目标。所提出的研究以一种新颖的方式整合了语料库、心理语言学和计算模型。互补的方法从不同的角度处理这一现象：大规模语料库分析、答案评分任务、目标推理任务以及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语境线索对（非）穷举性的贡献。这将通过测试自然发生的产物和（非）穷尽性判断之间的联系来阐明背景和先验世界知识在意义中的作用。此外，计算语用模型的进步为评估有关语言、认知和经验相互作用的假设提供了有用的工具。认知模型之间的系统模型比较将由基于语料库的研究中收集的数据驱动，这些模型提供了上下文作用不同的拟议理论的形式化。目前很少有文献从这个特定的角度来检验问题的解释。拟议的研究利用实验和计算方法将语用机制的实质内容充实起来，从而解决了语言学和认知科学中涉及语义表征和语用能力之间相互作用的基本问题。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。