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Abstract
In this talk, I will present AD3 (Alternating Directions Dual Decomposition), an algorithm for approximate MAP inference in loopy graphical models with discrete random variables, including structured prediction problems. AD3 is simple to implement and well-suited to problems with hard constraints expressed in first-order logic. It often finds the exact MAP solution, giving a certificate when it does; when it doesn’t, it can be embedded within an exact branch and bound technique. I’ll show experimental results on two natural language processing tasks, dependency parsing and frame-semantic parsing. This work was done in collaboration with Andre Martins, Dipanjan Das, Pedro Aguiar, Mario Figueiredo, and Eric Xing.
Biography
I am the Finmeccanica Associate Professor of Language Technologies and Machine Learning in the School of Computer Science at Carnegie Mellon University. I received my Ph.D. in Computer Science, as a Hertz Foundation Fellow, from Johns Hopkins University in 2006 and my B.S. in Computer Science and B.A. in Linguistics from the University of Maryland in 2001. My research interests include statistical natural language processing, especially unsupervised methods, machine learning for structured data, and applications of natural language processing. My book, Linguistic Structure Prediction, covers many of these topics. I serve on the editorial board of the journal Computational Linguistics and the Journal of Artificial Intelligence Research and received a best paper award at the ACL 2009 conference. My research group, Noah’s ARK, is supported by the NSF (including an NSF CAREER award), DARPA, Qatar NRF, IARPA, ARO, Portugal FCT, and gifts from Google, HP Labs, IBM Research,and Yahoo Research.