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 generalpurpose 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. 1

Revanth Gangi Reddy, Sai Chetan Chinthakindi, Zhenhailong Wang, Yi Fung, Kathryn Conger, Ahmed ELsayed, Martha Palmer, Preslav Nakov, Eduard Hovy, Kevin Small, Heng Ji. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.

This paper presents a proposition bank for Russian (RuPB), a resource for semantic role labeling (SRL). The motivating goal for this resource is to automatically project semantic role labels from English to Russian. This paper describes frame creation strategies, coverage, and the process of sense disambiguation. It discusses language-specific issues that complicated the process of building the PropBank and how these challenges were exploited as language-internal guidance for consistency and coherence.