|头衔职位||Assistant Research Professor，Department of Computer Science, USC|
Human languages evolve to communicate about events happening in the real world. Therefore, understanding events plays a critical role in natural language understanding (NLU). A key challenge to this mission lies in the fact that events are not just simple, standalone predicates. Rather, they are often described at different granularities, temporally form event processes, and are directed by specific central goals in a context. This talk covers recent advances in event process understanding in natural language. In this context, I will first introduce how to recognize the evolution of events from natural language, then how to solve fundamental problems of event process completion, intention prediction and membership prediction, and how knowledge about event processes can benefit various downstream NLU and machine perception tasks. I will also briefly present some open problems in this area, along with a system demonstration.
Muhao Chen is an Assistant Research Professor at the Department of Computer Science, USC. Prior to USC, he was a postdoctoral fellow at UPenn. He received his Ph.D. from the Department of Computer Science at UCLA in 2019, and B.S. in Computer Science from Fudan University in 2014. His research focuses on data-driven machine learning approaches for processing structured data, and knowledge acquisition from unstructured data. Particularly, he is interested in developing knowledge-aware learning systems with generalizability and requiring minimal supervision, and with concrete applications to natural language understanding, knowledge base construction, computational biology and medicine. Muhao has published over 50 papers in leading Artificial Intelligence, Natural Language Processing and Computational Biology venues. His work has received an NSF CRII Award, a best student paper award at ACM BCB, and a best paper award nomination at CoNLL. His research has also been supported by DARPA, IARPA and Air Force Research Laboratory. Additional information is available at https://muhaochen.github.io/