ACL '96: A Robust System for Natural Spoken Dialogue
James F. Allen, Bradford W. Miller, Eric K. Ringger, and Teresa Sikorski
Please download a copy of the paper by clicking here:
ACL '96 Paper in gzip'ed PostScript format
(59K).
TRAINS-95 v1.3 in action: 4.5 mins, 10 MB flat QuickTime,
gzipped or
StuffIt/MacBinary
Abstract:
This paper describes a system that leads us to believe in the
feasibility of constructing natural spoken dialogue systems in
task-oriented domains. It specifically addresses the issue of robust
interpretation of speech in the presence of recognition errors.
Robustness is achieved by a combination of statistical error
post-correction, syntactically- and semantically-driven robust
parsing, and extensive use of the dialogue context. We present an
evaluation of the system using time-to-completion and the quality of
the final solution that suggests that most native speakers of English
can use the system successfully with virtually no training.
Last Change 96 May 03 by
ringger@cs.rochester.edu