
Jay-Yoon Lee
amr
event extraction
self-training
weakly supervised learning
neuro-symbolic ai
self-correction
llm-as-a-judge
self-refinement
constraint injection
3
presentations
SHORT BIO
Jay-Yoon Lee is an assistant professor in the Graduate School of Data Science at Seoul National University (SNU). His research interest primarily lies in injecting knowledge, and constraints into machine learning models using the tools of structured prediction, reinforcement learning, and multi-task learning. He has worked on injecting hard constraints and logical rules into neural NLP models during his Ph.D., and now he is expanding his research area towards automatically capturing constraints, human-interactive models, and science problems such as protein interaction. Prior to joining SNU, he conducted his postdoctoral research in the College of Information & Computer Sciences at UMass Amherst with Professor Andrew McCallum. Jay-Yoon received his Ph.D. in Computer Science in 2020 from Carnegie Mellon University where he was advised by Professor Jaime Carbonell and received his B.S. from KAIST in electrical engineering.
Presentations

Toward Robust RALMs: Revealing the Impact of Imperfect Retrieval on Retrieval-Augmented Language Models
Jay-Yoon Lee

An Analysis under a Unified Formulation of Learning Algorithms with Output Constraints
Mooho Song and 1 other author

Learning from a Friend: Improving Event Extraction via Self-Training with Feedback from Abstract Meaning Representation
Zhiyang Xu and 2 other authors