Speaker: Grace Hui Yang, Associate Professor at Georgetown University
Talk Title: Corpus-Level End-to-End Exploration for Interactive Systems
Date: Friday - October 9, 2020, 1:00 - 2:00 PM
Abstract: A core interest in building Artificial Intelligence (AI) agents is to let them interact with and assist humans. One example is Dynamic Search (DS), which models the process that a human works with a search engine agent to accomplish a complex and goal-oriented task. Early DS agents using Reinforcement Learning (RL) have only achieved limited success for (1) their lack of direct control over which documents to return and (2) the difficulty to recover from wrong search trajectories. In this paper, we present a novel corpus-level end-to-end exploration (CE3) method to address these issues. In our method, an entire text corpus is compressed into a global low-dimensional representation, which enables the agent to gain access to the full state and action spaces, including the under-explored areas. We also propose a new form of retrieval function, whose linear approximation allows end-to-end manipulation of documents. Experiments on the Text REtrieval Conference (TREC) Dynamic Domain (DD) Track show that CE3 outperforms the state-of-the-art DS systems.
Bio: Dr. Grace Hui Yang is an Associate Professor in the Department of Computer Science at Georgetown University. Dr. Yang is leading the InfoSense (Information Retrieval and Sense-Making) group at Georgetown University, Washington D.C. Dr. Yang obtained her Ph.D. from Carnegie Mellon University in 2011. Her current research interests include deep reinforcement learning, interactive agents, and privacy-preserving information retrieval. Prior to this, she conducted research on question answering, automatic ontology construction, near-duplicate detection, multimedia information retrieval, and opinion and sentiment detection. Dr. Yang's research has been supported by the Defense Advanced Research Projects Agency (DARPA) and the National Science Foundation (NSF). Dr. Yang co-organized the Text Retrieval Conference (TREC) Dynamic Domain Track from 2015 to 2017 and led the effort for SIGIR privacy-preserving information retrieval workshops from 2014 to 2016. Dr. Yang served on the editorial board of Information Retrieval Journal from 2014 to 2017 and has actively served as either organizing or program committee member in many conferences such as SIGIR, ECIR, ACL, AAAI, ICTIR, CIKM, WSDM, and WWW. She is a recipient of the NSF Faculty Early Career Development Program (CAREER) Award.