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Cooperative Spoken Dialogue Model Using Bayesian Network and Event Hierarchy
IEICE TRANSACTIONS on Information and Systems Vol.E78-D No.6 pp.629-635
Publication Date: 1995/06/20
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Spoken Language Processing)
spoken language understanding,
Full Text: PDF(608.9KB)
In this paper, we propose a dialogue model that reflects two important aspects of spoken dialogue system: to be robust' and to be cooperative'. For this purpose, our model has two main inference spaces: Conversational Space (CS) and Problem Solving Space (PSS). CS is a kind of dynamic Bayesian network that represents a meaning of utterance and general dialogue rule. Robust' aspect is treated in CS. PSS is a network so called Event Hierarchy that represents the structure of task domain problems. Cooperative' aspect is mainly treated in PSS. In constructing CS and making inference on PSS, system's process, from meaning understanding through response generation, is modeled by dividing into five steps. These steps are (1) meaning understanding, (2) intention understanding, (3) communicative effect, (4) reaction generation, and (5) response generation. Meaning understanding step constructs CS and response generation step composes a surface expression of system's response from the part of CS. Intention understanding step makes correspondence utterance type in CS with action in PSS. Reaction generation step selects a cooperative reaction in PSS and expands a reaction to utterance type of CS. The status of problem solving and declared user's preference are recorded in mental state by communicative effect step. Then from our point of view, cooperative problem solving dialogue is regarded as a process of constructing CS and achieving goal in PSS through these five steps.