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Huan Sun "Towards Interactive, Transparent, and Cost-Effective Question Answering with Applications in the Clinical Domain"

Event Type
Seminar/Symposium
Sponsor
The Department of Computer Science, University of Illinois, BLENDER Lab
Location
https://illinois.zoom.us/j/8167899060?pwd=YkZrQ09zODRzL0txRGF5bnhWdmk0UT09
Virtual
wifi event
Date
Oct 30, 2020   10:00 am  
Speaker
Dr. Huan Sun, Assistant Professor, The Ohio State University
Contact
Candice Steidinger
E-Mail
steidin2@illinois.edu
Phone
217-300-8564
Views
75
Originating Calendar
Computer Science Speakers Calendar

Abstract: Finding relevant information quickly is integral to effective and efficient decision making. This becomes increasingly difficult as the scale and heterogeneity of data continue to grow rapidly. Question answering (QA) systems, which aim to find precise answers to natural language questions from users, have shown great potential to address this problem. However, state-of-the-art QA systems still largely fall short in many scenarios, such as (1) when questions are ambiguous, complex (e.g., involving multiple relations and operators), or from new domains where no/little training data is available, (2) when answering questions requires background knowledge that is not readily available in the data, and (3) when users need to understand the system’s answering process in order to better judge its trustworthiness. Such scenarios are prevalent in real application domains of QA (such as healthcare, finance, and sciences), and must be addressed in building practical systems. In this talk, I will discuss our recent work on some of these aspects, including building interactive semantic parsing systems, strategies for cost-effective QA such as pretraining and annotation policy learning, as well as QA in the clinical domain. I will conclude the talk with a summary of our planned work and collaboration opportunities.

 

Bio: Huan Sun is an assistant professor in the CSE Department at the Ohio State University. She was a visiting scientist at the University of Washington (2016) and received a Ph.D. in Computer Science from University of California, Santa Barbara (2015) and a B.S. in EEIS from the University of Science and Technology of China (2010). Her research interests lie in natural language processing and data mining, with emphasis on (interactive) question answering, text mining and understanding, as well as the intersection between NLP and software engineering. Huan received the NSF CAREER Award (2020), Google Faculty Award (2020), OSU Lumley Research Award (2020), SIGKDD Ph.D. Dissertation Runner-Up Award (2016), the honor of being MIT EECS Rising Stars (2015), Outstanding Dissertation Award from UCSB CS (2015), the UC Regents’ Special Fellowship (2010, 2014). 

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