February 4, 2016 | Wu & Chen Auditorium, Room 101 Levine Hall
February 4th, 2016 @ 3:00 pm
Computer Science and the Beckman Institute
University of Illinois at Urbana/Champaign
Machine Learning and Inference methods have become ubiquitous and have had a broad impact on a range of scientific advances and technologies and on our ability to make sense of large amounts of data. I will describe some of our research on developing learning and inference methods in pursue of natural language understanding. This challenge often involves assigning values to sets of interdependent variables and thus frequently necessitates performing global inference that accounts for these interdependencies. I will focus on algorithms for training these global models using indirect supervision signals. Learning models for these structured tasks is difficult partly since generating supervision signals is costly. We show that it is often easy to obtain a related indirect supervision signal, and discuss algorithmic implications as well as options for deriving this supervision signal, including inducing it from the world’s response to the model’s actions. A lot of this work is done within the unified computational framework of Constrained Conditional Models (CCMs), an Integer Linear Programming formulation that augments statistically learned models with declarative constraints as a way to support learning and reasoning. Within this framework, I will discuss old and new results pertaining to learning and inference and how they are used to push forward our ability to understand natural language.
Dan Roth is a Professor in the Department of Computer Science and the Beckman Institute at the University of Illinois at Urbana-Champaign and a University of Illinois Scholar. Roth is a Fellow of the American Association for the Advancement of Science (AAAS), the Association of Computing Machinery (ACM), the Association for the Advancement of Artificial Intelligence (AAAI), and the Association of Computational Linguistics (ACL), for his contributions to Machine Learning and to Natural Language Processing. He has published broadly in machine learning, natural language processing, knowledge representation and reasoning, and learning theory, and has developed advanced machine learning based tools for natural language applications that are being used widely by the research community and commercially. Roth is the Editor-in-Chief of the Journal of Artificial Intelligence Research (JAIR). He was the program chair of AAAI’11, ACL’03 and CoNLL’02. Prof. Roth received his B.A Summa cum laude in Mathematics from the Technion, Israel, and his Ph.D in Computer Science from Harvard University in 1995.