Doctoral Dissertation Research: Linguistic cues and speaker goals in the question/answer exchange

本项目将分析影响问题解释和答案的语言和非语言因素。语言因素包括表层线索，如说话人对词条的选择和疑问句的句法结构。非语言因素包括语篇语境和说话人提问的目的。该项目将讨论关于问答动态中句法、语义和语用之间的分工的理论建议，并将提供与关于特定类型的问题的特定答案的许可的辩论有关的经验数据。这项研究试图以一种新颖的方式整合语料库、心理语言学和发展方法，以及计算模型。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。研究人员将通过学术会议和出版物传播这些成果，并参与社区外联活动。研究人员与认知科学和计算机科学同事之间的现有联系，以及这些结果在计算模型中的拟议实现，确保了研究结果将适用于对话研究中的工作，涉及人工智能智能对话代理的设计。虽然以前关于问题的工作主要集中在详尽答案的性质上，但是什么许可了自然对话中经常出现的非详尽(或提及一些)答案，却没有得到足够的调查。因此，有必要系统地调查决定是否以及何时对一个问题进行非详尽解释的因素。本研究采用三管齐下的跨学科方法，结合实验语言学、发展心理学和计算机科学的工具，从自然语言语料库以及心理语言学和发展研究中收集经验数据，并在计算模型中捕捉到研究结果。语料库数据告诉我们说话人实际上做了什么。成人心理语言学研究告诉我们什么是被允许的，什么是可能的或不可能的。发展研究告诉我们语义和语用之间的权衡，以及输入的作用。计算模型旨在以概率框架捕捉这些数据。总而言之，这些相互关联的研究方法代表了对问题和问题的语言和认知基础的综合和整体研究。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 project will analyze the linguistic and extra-linguistic factors that influence the interpretations of questions and the answers to them. Linguistic factors include surface-level cues such as a speaker's choice of lexical items and the syntactic structure of the question. Extra-linguistic factors include the discourse context and the speaker's goals in asking the question. The project 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 what licenses certain kinds of answers to certain kinds of questions. The proposed research seeks to integrate corpus, psycholinguistic and developmental methods, and computational modeling in a novel way. The project will provide valuable opportunities for undergraduate students, from experimental design and running to the presentation of results in scholarly venues. The researchers will disseminate these results through academic conferences and publications, and engage in community outreach events. Both the existing connections between the researchers and colleagues in cognitive and computer science, and the proposed implementation of these results in a computational model ensure that the results of the research will be applicable to work in dialogue research, concerning the design of intelligent conversational agents for AI. While previous work on questions has focused primarily on the nature of exhaustive answers, what licenses non-exhaustive (or mention-some) answers, which are frequent in natural dialogue, has gone under-investigated. Thus there is a need to systematically investigate the factors that determine whether and when a question is interpreted non-exhaustively. This research uses a three-pronged interdisciplinary approach, combining tools from experimental linguistics, developmental psychology, and computer science, gathering empirical data from natural language corpora and psycholinguistic and developmental studies, and capturing the findings in a computational model. Corpus data inform us about what speakers actually do. Adult psycholinguistic studies inform us about what is allowed and assessed as likely or unlikely. Developmental studies inform us about the trade-off between semantics and pragmatics, and the role of the input. A computational model aims to capture these data in a probabilistic framework. Together, these interconnected research approaches represent an integrated and holistic study of the linguistic and cognitive underpinnings of questions and questioning.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.