演讲者： Xiaodan Zhu
地点： Room 106, ICST
Reasoning and inference are central to both human and artificial intelligence (AI). Modeling inference in natural language is notoriously challenging but is a basic problem towards true natural language understanding. In this talk, I will introduce the state-of-the-art deep learning models for natural language inference (NLI). The talk will also discuss a more fundamental problems: learning representation for semantics and composition. We will discuss not only on how deep learning models achieve the state-of-the-art performance but also on their limitations.
Xiaodan Zhu is an Assistant Professor of the Department of Electrical and Computer Engineering (ECE), Queen’s University, Canada. His research interests include Deep Learning, Natural Language Processing, Machine Learning, and Artificial Intelligence. Dr. Zhu received his Ph.D. from the Department of Computer Science at the University of Toronto in 2010 and his Master’s degree from the Department of Computer Science and Technology at Tsinghua University in 2000.
Dr. Zhu is an Associate Editor of the Computational Intelligence journal. He also served on many academic committees. Dr. Zhu is a panel member of Canada NSERC Discovery Grants (Computer Science; year 2017, 2020, 2021). He also served as an external reviewer for many government grants in Canada and other countries. Dr. Zhu also helps assess start-up companies' proposals for seed-stage programs. In the past, he worked with top government research lab (e.g., NRC) and industrial research labs such as Google (New York), IBM T.J. Watson Research Center, and Intel China Research Center.