Abstract

Abstract: How are sentences in a document connected, and why do they make the document feel “coherent”? Computational models of discourse aim to solve this myth by recovering the structural organization of texts, through which writers convey intent and meaning. In the first part of this talk, I will discuss our efforts on modeling human curiosity through question generation, and understanding its connection with discourse representations based on the linguistic theory of Questions Under Discussion. We show that LLMs, with design and training, resurface curiosity-driven questions and ground their elicitation and answers in text. Next, I will showcase the utility of such generative discourse models via applications in text simplification, as well as deriving explainable measures of information salience in LLMs using summarization as a behavioral probe.

Bio: Jessy Li is an Associate Professor in the Linguistics Department at the University of Texas at Austin. She received her Ph.D. (2017) from the Department of Computer and Information Science at the University of Pennsylvania. Her research interests are in computational linguistics and NLP , specifically discourse and document-level processing, natural language generation, and pragmatics. She is a recipient of an NSF CAREER Award, ACL and EMNLP Outstanding Paper Awards, an ACM SIGSOFT Distinguished Paper Award, among other honors. Jessy is on the leadership team of the newly established NSF -Simons CosmicAI Institute. She is also the Secretary of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL).