Declarative Design of Spoken Dialogue Systems with Probabilistic Rules

摘要

Spoken dialogue systems are instantiated in complex architectures comprising multiple interconnected components.These architectures often take the form of pipelines whose components are essentially black-boxes developed and optimised separately, using ad-hoc specification formats for their inputs and outputs, domain models and parameters.We present in this paper an alternative modelling approach, in which the dialogue processing steps (from understanding to management and to generation) are all declaratively specified using the same underlying formalism.The formalism is based on probabilistic rules operating on a shared belief state.These rules are expressed as structured mapping between state variables and provide a compact, probabilistic encoding for the dialogue processing models.We argue that this declarative approach yields several advantages in terms of transparency, domain-portability and adaptivity over traditional black-box architectures.We also describe the implementation and validation of this approach in an integrated architecture for human-robot interaction.