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CIS Seminar:”David V.S. Goliath: the Art of Leaderboarding in the Era of Extreme-Scale Neural Models”
October 12 at 3:30 PM - 4:30 PM
Scale appears to be the winning recipe in today’s leaderboards. And yet, extreme-scale neural models are still brittle to make errors that are often nonsensical and even counterintuitive. In this talk, I will argue for the importance of knowledge, especially commonsense knowledge, and demonstrate how smaller models developed in academia can still have an edge over larger industry-scale models, if powered with knowledge.
First, I will introduce “symbolic knowledge distillation”, a new framework to distill larger neural language models into smaller commonsense models, which leads to a machine-authored KB that wins, for the first time, over a human-authored KB in all criteria: scale, accuracy, and diversity. Next, I will introduce a new conceptual framework for language-based commonsense moral reasoning, and discuss how we can teach neural language models about complex social norms and human values, so that the machine can reason that “helping a friend” is generally a good thing to do, but “helping a friend spread fake news” is not. Finally, I will discuss an approach to multimodal script knowledge, which leads to new SOTA performances on a dozen leaderboards that require grounded, temporal, and causal commonsense reasoning.
School of Computer Science and Engineering, University of Washington
Yejin Choi is Brett Helsel Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington and also a senior research manager at AI2 overseeing the project Mosaic. Her research focuses on commonsense knowledge and reasoning, language grounding with vision and perception, and AI for social good. She is a co-recepient of the ACL Test of Time award in 2021, the CVPR Longuet-Higgins Prize (test of time award) in 2021, the AAAI Outstanding Paper Award (best paper award) in 2020, the Borg Early Career Award (BECA) in 2018, the inaugural Alexa Prize Challenge in 2017, IEEE AI’s 10 to Watch in 2016, and the Marr Prize (best paper award) at ICCV 2013. She received her Ph.D. in Computer Science at Cornell University and BS in Computer Science and Engineering at Seoul National University in Korea.