Event schema provides a conceptual, structural and formal language to represent events and model the world event knowledge. Unfortunately, it is challenging to automatically induce high-quality and high-coverage event schemas due to the open nature of real-world events, the diversity of event expressions, and the sparsity of event knowledge. In this paper, we propose a new paradigm for event schema induction -- knowledge harvesting from large-scale pre-trained language models, which can effectively resolve the above challenges by discovering, conceptualizing and structuralizing event schemas from PLMs. And an Event Schema Harvester (ESHer) is designed to automatically induce high-quality event schemas via in-context generation-based conceptualization, confidence-aware schema structuralization and graph-based schema aggregation. Empirical results show that ESHer can induce high-quality and high-coverage event schemas on varying domains.

翻译：事件模式提供了一种概念性、结构性和形式化的语言来表示事件，并对世界事件知识进行建模。然而，由于现实世界事件的开放性、事件表达方式的多样性以及事件知识的稀疏性，自动归纳出高质量和高覆盖度的事件模式具有挑战性。在本文中，我们提出了一种新的事件模式归纳范式——从大规模预训练语言模型中收获知识，该方法通过从PLMs中发现、概念化和结构化事件模式，有效解决了上述挑战。我们设计了一个事件模式收获器（ESHer），它通过基于上下文生成的概念化、置信度感知的模式结构化和基于图的模式聚合，自动归纳出高质量的事件模式。实验结果表明，ESHer能在不同领域归纳出高质量和高覆盖度的事件模式。