Advances in Structured Learning for Text and Speech Processing
Westin Resort, Whistler, BC
December 9, 2005
WORKSHOP PROGRAM:
The program for Advances in Structured Learning for Text and Speech
Processing is now available. For details please see:
http://www.cis.upenn.edu/~crammer/workshop-index.html
WORKSHOP DESCRIPTION:
This workshop is intended for researchers and students interested in
developing and applying structured classification methods to text and
speech processing problems. Recent advances in structured
classification provide promising alternatives to the probabilistic
generative models that have been the mainstay of speech recognition
and statistical language processing. However, powerful features of
probabilistic generative models, such as hidden variables and
compositional combination of several kinds of evidence, do not
transfer cleanly to all structured classification methods. Starting
with surveys of the state-of-the-art in structured classification for
text and speech, the workshop will focus on successes, failures, and
directions for improvement of structured classification methods for
text and speech and possible syntheses between the new structured
classification methods and traditional generative models. Comparison
will also be made with "generative" vs. "discriminative" training
procedures in structure classification problems. A successful workshop
will identify critical questions that current methods are not yet
capable of solving, and promising directions for solution. For
instance, we hope to achieve a better understanding of how
discriminative models may work with missing information, such as
under-specified alignments or syntactic analyses --- we plan, more
generally, answer questions such as why, when, and where use a
generative model. Such problems arise in both speech, language, and
text processing, and will serve as unifying themes for the workshop.
Among questions to be discussed, we expect:
* Discriminative vs. generative models and algorithms
* Max margin, perceptron, and other criterion
* Incorporating prior knowledge
* Using data from multiple domains
* Adaptation of structured classifiers to new conditions
* Using unlabeled data
* Combining text and speech
* Integrated inference for complex language processing tasks
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