Harvesting Event Schemas from Large Language Models
Jialong Tang Hongyu Lin Zhuoqun Li Yaojie Lu Xianpei Han Le Sun
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.