PeaCoK：用于一致且引人入胜叙事的人物角色常识知识
PeaCoK: Persona Commonsense Knowledge for Consistent and Engaging Narratives
AI总结：
本文提出大规模人物角色常识知识图谱 PeaCoK，包含约 10 万条经人工验证的事实，覆盖五个人物角色知识维度，以提升叙事智能体生成内容的一致性与吸引力。
摘要：
维持连贯且引人入胜的叙事要求对话或讲故事智能体理解说话者或听者的人物角色如何为叙事奠定基础。具体而言，这些智能体必须推断其听者的人物角色，以生成迎合其兴趣的陈述。它们还必须学会在整个叙事过程中为自身维持一致的说话者人物角色，从而使对话方感到参与了一场真实的对话或故事。然而，人物角色多样且复杂：它们蕴含大量丰富的相互关联的世界知识，在通用叙事系统中难以稳健地表示（例如，歌手擅长唱歌，并且可能上过音乐学院）。在本工作中，我们构建了一个新的大规模人物角色常识知识图谱 PeaCoK，包含约 10 万条经人工验证的人物角色事实。我们的知识图谱对先前人类交互行为研究中识别出的五个人物角色知识维度进行了模式化，并从现有常识知识图谱和大规模预训练语言模型中按该模式蒸馏事实。我们的分析表明，PeaCoK 包含丰富且精确的世界人物角色推断，有助于下游系统生成更一致且更引人入胜的叙事。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
