Techniques for utterance disambiguation in a human-computer dialogue system

Disambiguating an utterance occurring in a dialogue context is a complex task, which requires input from many different sources of information— some syntactic, some semantic, and some pragmatic. The central question addressed by this thesis is how to integrate data sources for utterance disambiguation within a bilingual human-computer dialogue system. First, a simple scheme is proposed for classifying disambiguation data sources; then this scheme is used to develop a method for combining data sources in a principled manner. Next, several actual sources of disambiguation data are explored; each is fitted into the previously described implementation framework. In particular, a probabilistic grammar is developed and augmented using novel techniques to increase its performance with respect to the local dialogue context. In a dialogue system, ambiguities which cannot be resolved automatically can be clarified by asking the user what was meant. This thesis also presents a model of clarification subdialogues which is integrated within the utterance disambiguation framework. This is followed by a brief treatment of how user errors may be accommodated, and how this process can also be fitted—conceptually and in implementation—into the previously described disambiguation framework. Finally, I describe the details of implementing these techniques within an existing dialogue system, and give examples demonstrating their effectiveness.