When: February 25 (Thursday) 10:00AM
Open-domain question answering (QA) requires a system to automatically find the answer for any natural language question. It is not only a core element of a range of real-world applications, but also a fundamental benchmark for natural language understanding. In this tak, I will first describe recent advances in open-domain QA systems that retrieve evidence text from a large collection of text. We introduce two orthogonal methods that overcome the limitation of previous, text-matching approach: an approach that constructs a graph of passages and models cross-passage interaction, and a dual encoder-based approach that builds expressive dense representations of passages. Second, I will describe a new class of the QA task – answering ambiguous questions. Previous work only focused on questions with a definite answer, ignoring intrinsic ambiguity that arises frequently in the questions written during information gathering. We propose a task that requires a system to answer a potentially ambiguous question by finding a set of all answers and disambiguating them. Together with new tasks and new models, we are making progress on significantly broadening the scope of questions that can automatically be answered.
Sewon Min is a Ph.D. student in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, advised by Hannaneh Hajishirzi and Luke Zettlemoyer. Her research focuses on natural language understanding, question answering, and knowledge representation. She is an active committee member of the natural language processing (NLP) conferences (ACL/EMNLP/NAACL) and machine learning conferences (NeurIPS/ICLR/ICML/AAAI). She is a co-organizer of the 3rd Workshop on Machine Reading for Question Answering (EMNLP 2021), Competition on Efficient Open-domain Question Answering (NeurIPS 2020), and Workshop on Structured and Unstructured KBs (AKBC 2020). During her PhD, she has been a visiting researcher at Facebook AI Research and Google Research. Prior to UW, she obtained a BS degree in Computer Science & Engineering from Seoul National University.