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The progress of event extraction research has been hindered by the absence ofwide-coverage, large-scale datasets. To make event extraction systems moreaccessible, we build a general-purpose event detection dataset GLEN, whichcovers 205K event mentions with 3,465 different types, making it more than 20xlarger in ontology than today's largest event dataset. GLEN is created byutilizing the DWD Overlay, which provides a mapping between Wikidata Qnodes andPropBank rolesets. This enables us to use the abundant existing annotation forPropBank as distant supervision. In addition, we also propose a new multi-stageevent detection model CEDAR specifically designed to handle the large ontologysize in GLEN. We show that our model exhibits superior performance compared toa range of baselines including InstructGPT. Finally, we perform error analysisand show that label noise is still the largest challenge for improvingperformance for this new dataset. Our dataset, code, and models are released at\url{https://github.com/ZQS1943/GLEN}.}

GLEN: General-Purpose Event Detection for Thousands of Types