Edinburgh Napier University

  Humans can engage in a conversation to collaborate on multi-step tasks and divert briefly to complete essential sub-tasks, such as asking for confirmation or clarification, before resuming the overall task. This communication is necessary as some knowledge in instructional documents can be implicit rather than grounded in the dialogue, meaning that people must rely on their own and others’ knowledge for problem-solving. We often attribute this capability to common sense, i.e., the assumption that interlocutors perceive behaviours, temporality, context, space and object properties in a similar way. To explore the significance of emulating such problem-solving capabilities, we developed a novel hybrid document-grounded dialogue system (DGDS) called ChefBot1 leveraging the contextual understanding of a pre-trained language model and the structuring of a sequence-to-sequence model trained on a series of commonsense knowledge databases. In a human evaluation, the hybrid system proved more effective in capturing object knowledge (utility, appearance, storage, relationships, handling) and contextual knowledge (understanding of events and situations) compared to a rule-based baseline. A key finding of this paper is demonstrating how inferring context from different document sources enhances the dialogue by allowing richer and more fluid interaction. To our knowledge, this research is innovative in its scope as the first effort to model task-based dialogue grounded in commonsense knowledge across multiple documents.