Relations World: A Possibilistic Graphical Model
This work addresses the challenge of creating more robust and context-aware dialog systems, though it appears incremental as it focuses on a specific domain (family relations) as a first step.
The paper tackles the problem of building a world model for dialog systems by developing a system that uses text-based dialog to derive a model of the user's family relations, resulting in capabilities to infer relational triples, recover from coreference errors, and learn context-dependent paraphrase models.
We explore the idea of using a "possibilistic graphical model" as the basis for a world model that drives a dialog system. As a first step we have developed a system that uses text-based dialog to derive a model of the user's family relations. The system leverages its world model to infer relational triples, to learn to recover from upstream coreference resolution errors and ambiguities, and to learn context-dependent paraphrase models. We also explore some theoretical aspects of the underlying graphical model.