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.
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