Dialog Annotation for Stochastic Generation

Individuals 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 by Oh 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 of results. The current paper describes the classification and tagging procedures used for Stochastic Generation, and discusses the advantages and limitations of the techniques.