Thesis Available

[jain@rtc.atk.com]

My recently completed PhD thesis is now available as a technical
report (number CMU-CS-91-208).  To obtain a copy, please send email to
"reports+ at cs.cmu.edu" or physical mail to:

Technical Reports Request
School of Computer Science
Carnegie Mellon University
5000 Forbes Ave.
Pittsburgh, PA 15213-3890

To defray printing and mailing costs, there will be a small fee.  I
apologize to those of you who had requested copies directly from me
and have not received them.  The number of requests was high enough
that I ran out of time to process them (and copies to send!).

REMEBER: DON'T REPLY TO THE WHOLE LIST!  REPLY TO: reports+ at cs.cmu.edu


TR: CMU-CS-91-208

TITLE:

  PARSEC: A Connectionist Learning Architecture for Parsing Spoken Language

ABSTRACT:

A great deal of research has been done developing parsers for natural
language, but adequate solutions for some of the particular problems
involved in spoken language are still in their infancy. Among the
unsolved problems are: difficulty in constructing task-specific
grammars, lack of tolerance to noisy input, and inability to
effectively utilize complimentary non-symbolic information.

This thesis describes PARSEC---a system for generating connectionist
parsing networks from example parses. PARSEC networks exhibit three
strengths:

  1) They automatically learn to parse, and they generalize well compared
     to hand-coded grammars.
  2) They tolerate several types of noise without any explicit
     noise-modeling.
  3) They can learn to use multi-modal input, e.g. a combination of
     intonation, syntax, and semantics.

The PARSEC network architecture relies on a variation of supervised
back-propagation learning. The architecture differs from some other
connectionist approaches in that it is highly structured, both at the
macroscopic level of modules, and at the microscopic level of
connections. Structure is exploited to enhance system performance.

Conference registration dialogs formed the primary development testbed
for PARSEC. A separate simultaneous effort in speech recognition and
translation for conference registration provided a useful data source
for performance evaluations.

Presented in this thesis are the PARSEC architecture, its training
algorithms, and detailed performance analyses along several dimensions
that concretely demonstrate PARSEC's advantages.