Computer Science Department Colloquium
Learning and Using Knowledge for Text
Tuesday, November 16, 2021, 11:00am - 12:15pm
Speaker: Karl Stratos
Karl Stratos is an assistant professor in the Computer Science Department at Rutgers University. His research centers on unsupervised representation learning and knowledge-intensive language processing. He completed a PhD in computer science from Columbia University in 2016. During PhD, he was advised by Michael Collins and also worked closely with Daniel Hsu. After PhD, he was a senior research scientist at Bloomberg LP (2016-2017) and a research assistant professor at Toyota Technological Institute at Chicago (2017-2019).
Location : Via Zoom
Event Type: Computer Science Department Colloquium
Abstract: Two key problems in automatic text understanding are (1) how to learn high-level representations that capture useful knowledge from noisy unlabeled data, and conversely (2) how to use existing knowledge resources to robustly handle unknown facts. In this talk, I will present our recent works along the two thrusts. The first is AMMI, a general framework for learning discrete structured latent variables from noisy signals by adversarially maximizing mutual information (ICML 2020). The second is a theoretical and empirical investigation of the use of "hard" negative examples in noise contrastive estimation (NAACL 2021). The third is EntQA, a new paradigm for entity linking that reduces the task as inverse open-domain question answering and fundamentally solves the dilemma of having to predict mentions without knowing their entities (under review). We achieve new state-of-the-art results in document hashing, zero-shot entity retrieval, and entity linking on numerous datasets.
Rutgers university School of Arts and Sciences
Contact Host: Matthew Stone