AIC Seminar Series
Amortized inference for structured output prediction
Notice: Hosted by Rodrigo Braz
Date: 2013-11-07 at 16:30
Location: EK255 (SRI E building) (Directions)
Structured output prediction in NLP consists of using a learned model
with an inference algorithm to predict structures for inputs.
Typically, at prediction time, each example is considered to be
independent of all other examples.
In this talk, I will argue that we can take advantage of regularities
of outputs over entire datasets to give an amortized improvement in
inference time over the lifetime of the predictor. I will present
different amortized inference algorithms, which can re-use computation
from previously solved instances to speed up any standard inference
algorithm and retain their exactness properties. Then, I will discuss
an approach for decomposed amortized inference that allows us to apply
this idea for sub-parts of the output structure. Through the talk, I
will present results which show that, for the tasks of semantic role
labeling and entity-relation extraction, the various amortized
inference algorithms give significant decrease in the number of calls
to the underlying inference engine.
I am a post-doc at Stanford University with Chris Manning and the
Stanford NLP group. I obtained my Ph.D. from the Computer Science
Department of the University of Illinois at Urbana-Champaign, where I
worked with Dan Roth and the Cognitive Computation group.
My research deals with Machine Learning and Natural Language Processing.
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