December 2, 2019

12:00 pm / 1:15 pm

Venue

Hackerman Hall B17 @ 3400 N. Charles Street, Baltimore, MD 21218

Abstract
Despite their often-discussed advantages, deep learning methods largely disregard theories of both learning and language.  This makes their prediction behavior hard to understand and explain.  In this talk, I will present a path toward more understandable (but still ?deep?) natural language processing models, without sacrificing accuracy.  Rational recurrences comprise a family of recurrent neural networks that obeya particular set of rules about how to calculate hidden states, and hence correspond to parallelized weighted finite-state pattern matching.  Manyrecently introduced models turn out to be members of this family, and the weighted finite-state view lets us derive some new ones.  I’ll introducerational RNNs and present some of the ways we have used them in NLP.  My collaborators on this work include Jesse Dodge, Hao Peng, Roy Schwartz,and Sam Thomson.
Biography
 
Noah Smith is a Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, as well as a Senior Research Manager at the Allen Institute for Artificial Intelligence. Previously, he was an Associate Professor of Language Technologies and Machine Learning in the School of Computer Science at Carnegie Mellon University. He received his Ph.D. in Computer Science fromJohns Hopkins University in 2006 and his B.S. in Computer Science and B.A. in Linguistics from the University of Maryland in 2001. His research interests include statistical natural language processing, machine learning, and applications of natural language processing, especially to the social sciences. His book, Linguistic Structure Prediction, covers many of these topics. He has served on the editorial boards of the journals Computational Linguistics (2009?2011), Journal of Artificial Intelligence Research (2011?present), and Transactions of the Association for Computational Linguistics (2012?present), as the secretary-treasurer of SIGDAT (2012?2015 and 2018?present), and as program co-chair of ACL 2016. Alumni of his research group, Noah’s ARK, are international leaders in NLP in academiaand industry; in 2017 UW’s Sounding Board team won the inaugural Amazon Alexa Prize. Smith’s work has been recognized with a UW Innovation award (2016?2018), a Finmeccanica career development chair at CMU (2011?2014), an NSF CAREER award (2011?2016), a Hertz Foundation graduate fellowship (2001?2006), numerous best paper nominations and awards, and coverage by NPR, BBC, CBC, New York Times, Washington Post, and Time.