Dialog Annotation for Stochastic GenerationProceedings of the ISLE Workshop on Dialogue Tagging for Human Computer Interaction
Date of Original Version1-1-2002
Abstract or DescriptionIndividuals who successfully make their livelihood by talking with others, for example travel agents, can be presumed to have optimized their language for the task at hand in terms of conciseness and intelligibility. It makes sense to exploit this effort for the purpose of building better generation components for a spoken dialog system. The Stochastic Generation technique, introduced byOh and Rudnicky (2002), is one such approach. In this approach, utterances in a corpus of domain expert utterances are classiﬁed as to speech act and individual concepts tagged. Statistical n-gram models are built for each speech-act class then used generatively to create novel utterances. These have been shown to be comparable in quality to human productions. The class and tag scheme is concrete and closely tied to the domain at hand; we believe this produces a distinct advantage in speed of implementation and quality ofresults. The current paper describes the classiﬁcation and tagging procedures used for Stochastic Generation, and discusses the advantages and limitations of the techniques.
Citation InformationAlexander I Rudnicky and Alice H Oh. "Dialog Annotation for Stochastic Generation" Proceedings of the ISLE Workshop on Dialogue Tagging for Human Computer Interaction (2002)
Available at: http://works.bepress.com/alexander_rudnicky/37/