Sustaining coherent and engaging narratives requires dialogue or storytelling agents to understand how the personas of speakers or listeners ground the narrative. Specifically, these agents must infer personas of their listeners to produce statements that cater to their interests. They must also learn to maintain 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 of rich interconnected world knowledge that is challenging to robustly represent in general narrative systems (e.g., a singer is good at singing, and may have attended conservatoire). In this work, we construct a new large-scale persona commonsense knowledge graph, PeaCoK, containing ~100K human-validated persona facts. Our knowledge graph schematizes five dimensions of persona knowledge identified in previous studies of human interactive behaviours, and distils facts in this schema from both existing commonsense knowledge graphs and large-scale pretrained language models. Our analysis indicates that PeaCoK contains rich and precise world persona inferences that help downstream systems generate more consistent and engaging narratives.

翻译：摘要：维持连贯且引人入胜的叙事，要求对话或故事生成智能体理解说话者或听者的人格如何奠定叙事基础。具体而言，这些智能体必须推断听者的人格，以生成迎合其兴趣的表述；同时，它们还需学会在叙事过程中保持自身说话者人格的一致性，从而使对话对象感受到真实对话或故事中的参与感。然而，人格具有多样性与复杂性：其蕴含大量相互关联的丰富世界知识，而这些知识难以在通用叙事系统（例如，歌手擅长歌唱且可能就读于音乐学院）中得到稳健表征。本研究构建了一个大规模人物常识知识图谱PeaCoK，包含约10万个经过人工验证的人格事实。该知识图谱基于先前关于人类互动行为的研究，将人格知识归纳为五个维度，并从现有常识知识图谱和大规模预训练语言模型中提炼出符合这一模式的事实。分析表明，PeaCoK包含丰富且精准的世界人格推理，能够帮助下游系统生成更连贯且引人入胜的叙事。