Metareasoning for More Effective Human-Computer Dialogue.

摘要

The research project explores specific meta-dialogue behaviors in terms of both how a system could be made to perform them, and to what extent they can increase overall system performance. We focus on two types of meta- dialogue capabilities: ability to detect and recover from anomalous dialogue patterns in simple exchanges, and on-line extensions or changes to working vocabulary. Our main method involves detailed representation of the dialogue context, separating domain, language, and dialogue specific aspects, and significant amounts of meta-reasoning about the system's processing of these representations. An existing logical inference system, ALMA/CARNE, developed as part of a pilot study, is being used in an implementation phase of this work. We are also engaged in a study of existing dialogue corpora to investigate the range and frequency of meta-dialogue expressions in different task domains.