The development of event extraction systems has been hindered by the absence
of wide-coverage, large-scale datasets. To make event extraction systems more
accessible, we build a general-purpose event detection dataset GLEN, which
covers 3,465 different event types, making it over 20x larger in ontology than
any current dataset. GLEN is created by utilizing the DWD Overlay, which
provides a mapping between Wikidata Qnodes and PropBank rolesets. This enables
us to use the abundant existing annotation for PropBank as distant supervision.
In addition, we also propose a new multi-stage event detection model
specifically designed to handle the large ontology size and partial labels in
GLEN. We show that our model exhibits superior performance (~10% F1 gain)
compared to both conventional classification baselines and newer
definition-based models. Finally, we perform error analysis and show that label
noise is still the largest challenge for improving performance.

本论文提供了一个实用的通用事件检测数据集 GLEN，它使用了现有注释的 PropBank 作为远程监督，提出了一个新的多阶段事件检测模型，能够有效处理大本体大小和标签噪声问题，并在性能上表现出优越性。