GLEN: General-Purpose Event Detection for Thousands of Types
This addresses the problem of limited event extraction data for researchers, though it is incremental in building upon existing annotation methods.
The authors tackled the lack of wide-coverage event extraction datasets by creating GLEN, a dataset with 205K event mentions and 3,465 types, which is over 20 times larger than existing ones, and proposed a model CEDAR that outperforms baselines including InstructGPT.
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}.}