  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.
