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Event understanding is an essential ability for humans to acquire information. With the rise of multimedia, automated event understanding and narration require machines to not only obtain the local structures of events from multimedia data (i.e., who, what, where, and when), but also performs global understanding and inference (i.e., what is likely to happen, and why). However, current event understanding is text-only, local, and lacks reasoning. Real events that are multimedia, inter-connected, and probabilistic. This talk will present Multimedia Event Extraction to extract events and their arguments from multimedia data, and use event knowledge to enhance multimedia pre-training models. Based on the extracted knowledge, I will introduce how to induce event schemas (knowledge of complex event patterns) by learning a temporal graph model. After that, I will talk about how to use event knowledge to support real applications, such as timeline summarization.
Manling Li is a fourth-year Ph.D. student at the Computer Science Department of University of Illinois Urbana-Champaign. Manling has won the Best Demo Paper Award at ACL'20, the Best Demo Paper Award at NAACL'21, C.L. Dave and Jane W.S. Liu Award, and has been selected as Mavis Future Faculty Fellow. She is a recipient of the Microsoft Research PhD Fellowship. She has more than 30 publications on knowledge extraction and reasoning from multimedia data. Additional information is available at https://limanling.github.io