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Sustaining coherent and engaging narratives requires dialogue or storytellingagents to understand how the personas of speakers or listeners ground thenarrative. Specifically, these agents must infer personas of their listeners toproduce statements that cater to their interests. They must also learn tomaintain consistent speaker personas for themselves throughout the narrative,so that their counterparts feel involved in a realistic conversation or story.  However, personas are diverse and complex: they entail large quantities ofrich interconnected world knowledge that is challenging to robustly representin general narrative systems (e.g., a singer is good at singing, and may haveattended conservatoire). In this work, we construct a new large-scale personacommonsense knowledge graph, PeaCoK, containing ~100K human-validated personafacts. Our knowledge graph schematizes five dimensions of persona knowledgeidentified in previous studies of human interactive behaviours, and distilsfacts in this schema from both existing commonsense knowledge graphs andlarge-scale pretrained language models. Our analysis indicates that PeaCoKcontains rich and precise world persona inferences that help downstream systemsgenerate more consistent and engaging narratives.

PeaCoK: Persona Commonsense Knowledge for Consistent and Engaging Narratives