ABSTRACT:

Information extraction (IE) is the process of automatically inducing structures of concepts and relations described in natural language text. It is the fundamental task to assess the machine’s ability for natural language understanding, as well as the essential step for acquiring structural knowledge representation that is integral to any knowledge-driven AI systems. Despite the importance, obtaining direct supervision for IE tasks is always very difficult, as it requires expert annotators to read through long documents and identify complex structures. Therefore, a robust and accountable IE model has to be achievable with minimal and imperfect supervision. Towards this mission, this talk covers recent advances of machine learning and inference technologies that (i) grant robustness against noise and perturbation, (ii) mitigate spurious correlations, and (iii) provide indirect supervision for logically consistent and label-efficient IE.

BIO:

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 data-driven 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 Cisco and Amazon, and an ACM SIGBio Best Student Paper Award. 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.

Hosted by Professor Quanquan Gu