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

Moyer, Morgan, Highland Park NJ

Investigators

Abstract

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.