Computer Science Colloquium
Lagrangian Relaxation for Natural Language Processing
Alexander Rush, MIT
April 07, 2014
Warren Weaver Hall, 1302
251 Mercer Street
New York, NY 10012
Spring 2014 Colloquia Calendar
Statistical methods have revolutionized natural language processing
(NLP) and have led to advances in applications such as automatic
translation, question answering, and structured search. Fueled by
access to large amounts of textual data, researchers have developed
increasingly detailed models of language to improve accuracy on these
tasks. However, these improvements often come at the cost of vastly
increasing the search-space of the underlying prediction
problems. Solving these prediction problems optimally is very
challenging, leading most large-scale NLP systems to rely heavily on
heuristic search algorithms.
This talk presents methods for finding provably optimal predictions
for important models in natural language processing. I will focus on two
core NLP tasks, syntactic parsing and machine translation. For both of
these problems I will show how Lagrangian relaxation, a classical method
from combinatorial optimization, can be used to decompose challenging
prediction problems into problems that are solvable with efficient
combinatorial algorithms. Experiments show that the methods find
optimal solutions for the vast majority of examples, with
comparable efficiency to commonly-used heuristic algorithms.
Alexander Rush is a Ph.D. candidate at MIT under the supervision of
Professor Michael Collins and is currently a visiting scholar at Columbia University.
He received a BA in computer science from Harvard University. He has won best paper
awards at EMNLP 2010 and NAACL 2012. His research interests are in
statistical natural language processing and structured prediction.