CS&E Colloquium: Muhao Chen
This week's speaker, Muhao Chen (University of Southern California), will be giving a talk titled, "Robust and Indirectly Supervised Knowledge Acquisition from Natural Language".
Information extraction (IE) refers to the process of automatically determining the concepts and relations present in natural language text. IE is not only the fundamental task for evaluating a machine's ability to understand natural language. More importantly, it is the essential step for acquiring structured knowledge representation required by any knowledge-driven AI systems. Despite the importance, obtaining direct supervision for IE tasks is always challenging due to the difficulty in locating complex structures in long documents by expert annotators. Therefore, a robust and accountable IE model has to be achievable with minimal and imperfect supervision. Towards this mission, this talk presents recent advances of machine learning and inference technologies that (i) grant robustness against noise and perturbation, (ii) prevent systematic errors caused by spurious correlations, and (iii) provide indirect supervision for label-efficient and logically consistent IE.
Muhao Chen is an Assistant Research Professor of Computer Science at USC, and the director of the USC Language Understanding and Knowledge Acquisition (LUKA) Lab (https://luka-group.github.io/). His research focuses on robust and minimally supervised machine learning for natural language understanding, structured data processing, and knowledge acquisition from unstructured data. His work has been recognized with an NSF CRII Award, faculty research awards from Amazon, Cisco and the Keston Foundation, an ACM SIGBio Best Student Paper Award and a best paper nomination at CoNLL. Dr. Chen obtained his Ph.D. degree from UCLA Department of Computer Science in 2019, and was a postdoctoral researcher at UPenn prior to joining USC.