Del av: Conversational AI for Natural Human-Centric Interaction (Springer, 2022)
We present ongoing work on a new dialogue management framework using graphs as core representation for the current dialogue state. Dialogue management tasks such as state tracking and action selection are framed as sequences of graph transformations that repeatedly update this graph based on incoming observations. Those graph transformations are expressed using a graph query language, making it possible to specify all dialogue management operations through a unified, declarative syntax. We argue that graphs are particularly well suited to model the dialogue state of complex, open-ended domains. In contrast to traditional dialogue state representations that are limited to fixed, predefined slots, graphs can naturally express dialogue domains with rich relational structures and variable numbers of entities to track. We describe how dialogue state tracking and action selection can be modelled in such graph-centric view of dialogue management, using either handcrafted rules or data-driven models. We also briefly discuss how to account for some aspects of dialogue management such as uncertainties, incremental inputs and contextual knowledge. Finally, we describe a proof-of-concept study of this dialogue management framework in a human–robot interaction scenario.