Title: Neuro-Logic and Differentiable Controls

Abstract: The key challenge to neural language generation is that language models are essentially a mouth without a brain. In this talk, I’ll discuss how we can make better lemonades out of off-the-shelf neural language models via smarter decoding-time algorithms: discrete logic integration and differentiable reasoning.

Bio: 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.