CS Colloquium: Yangfeng Ji (UVA)
Enhancing Human-AI Collaboration in Language Technology
The goal of the human-AI collaboration is to take advantage of the complementary nature of humans and AI systems, such that they can work together to achieve better application goals. In language technology, the questions of enhancing human-machine collaboration include (1) how we can build AI systems will help humans acquire language skills effectively? Moreover, (2) how can we build explainable AI systems that can be trusted by human users in real-world NLP applications? In this talk, I present some recent work as examples to answer these two questions. The first part of this talk will demonstrate the recent progress on text generation can help enhance human writing skills via a collaborative writing system. Then, in the second part, I will show our recent work on interpreting predictions from neural text classifiers with human-understandable explanations.
Bio: Yangfeng Ji is the William Wulf Assistant Professor in the Department of Computer Science at the University of Virginia, where he leads the Natural Language Processing group. His research interests include building machine learning models for text understanding and generation. His work on entity-driven story generation won an Outstanding Paper Award at NAACL 2018. Yangfeng received his Ph.D. degree from the School of Interactive Computing at Georgia Institute of Technology in 2016 and was a Postdoctoral Researcher in the Paul G. Allen School of Computer Science & Engineering at the University of Washington from 2016 to 2018.
Friday, November 8 at 11:00am