The progress of event extraction research 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 205K event mentions with 3,465 different types, making it more than 20x larger in ontology than today's largest event 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 CEDAR specifically designed to handle the large ontology size in GLEN. We show that our model exhibits superior performance compared to a range of baselines including InstructGPT. Finally, we perform error analysis and show that label noise is still the largest challenge for improving performance for this new dataset. Our dataset, code, and models are released at \url{https://github.com/ZQS1943/GLEN}.}

翻译：事件抽取研究的进展一直受到缺乏覆盖广泛、大规模数据集的阻碍。为使事件抽取系统更易获取，我们构建了一个通用事件检测数据集GLEN，该数据集涵盖20.5万条事件提及，包含3,465种不同类型，其本体规模比现有最大事件数据集大20倍以上。GLEN通过利用DWD Overlay创建，该映射提供了维基数据Q节点与PropBank角色集之间的关联，使我们能够利用PropBank现有的大量标注作为远程监督。此外，我们还提出了一个专门针对GLEN大型本体规模设计的多阶段事件检测模型CEDAR。研究表明，我们的模型相比包括InstructGPT在内的一系列基线方法展现出更优性能。最后，我们通过误差分析发现，标签噪声仍是提升该新数据集性能的最大挑战。我们的数据集、代码及模型已发布于\url{https://github.com/ZQS1943/GLEN}。