Jessy Li (University of Texas at Austin – Virtual Visit) “New Challenges in Text Simplification”

When:
September 17, 2021 @ 12:00 pm – 1:15 pm
2021-09-17T12:00:00-04:00
2021-09-17T13:15:00-04:00
Where:
Hackerman Hall B17
3400 N. Charles Street
Baltimore
MD 21218
Cost:
Free

Abstract

Text simplification aims to help audiences read and understand a piece of text through lexical, syntactic, and discourse modifications, while remaining faithful to its central idea and meaning. Thanks to large-scale parallel corpora derived from Wikipedia and News, much of modern-day text simplification research focuses on sentence simplification, transforming original, more complex sentences into simplified versions. In this talk, I present new frontiers that focus on discourse operations. First, we consider the challenging task of simplifying highly technical language, in our case, medical texts. We introduce a new corpus of parallel texts in English comprising technical and lay summaries of all published evidence pertaining to different clinical topics. We then propose a new metric to quantify stylistic differentiates between the two, and models for paragraph-level simplification. Second, we present the first data-driven study of inserting elaborations and explanations during simplification, and illustrate the richness and complexities of this phenomenon.

Biography

Jessy Li is an assistant professor in the Department of Linguistics at UT Austin where she works on in computational linguistics and natural language processing. Her work focuses on discourse processing, text generation, and language pragmatics in social media. She received her Ph.D. in 2017 from the University of Pennsylvania. She received an ACM SIGSOFT Distinguished Paper Award at FSE 2019, an Area Chair Favorite at COLING 2018, and a Best Paper nomination at SIGDIAL 2016.

Johns Hopkins University

Johns Hopkins University, Whiting School of Engineering

Center for Language and Speech Processing
Hackerman 226
3400 North Charles Street, Baltimore, MD 21218-2680

Center for Language and Speech Processing