mlsanityData to 7 Oct 2026

GLEN: General-Purpose Event Detection for Thousands of Types

arXiv 2303.09093 ↗First posted 16 Mar 2023cs.CLcited by 13

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Abstract (arXiv)

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 urlhttps://github.com/ZQS1943/GLEN.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 10 of its 36 references that are in the map. First posted on arXiv 16 Mar 2023.

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10 of 36 references.

References · 10

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,160
  3. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 719
  4. 2021Document-Level Event Argument Extraction by Conditional Generationcited by 154
  5. 2020MAVEN: A Massive General Domain Event Detection Datasetcited by 103
  6. 2019Multi-Sentence Argument Linkingcited by 80
  7. 2019Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extractioncited by 54
  8. 2019Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detectioncited by 54
  9. 2017Scale Up Event Extraction Learning via Automatic Training Data Generationcited by 11
  10. 2022Efficient Zero-shot Event Extraction with Context-Definition Alignmentcited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 5; 2025: 2; 2026: 2.

13 papers.

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  1. 2023TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extractioncited by 27
  2. 2024Improving Event Definition Following For Zero-Shot Event Detectioncited by 7
  3. 2023Few-shot Event Detection: An Empirical Study and a Unified Viewcited by 5
  4. 2023Calibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typingcited by 5
  5. 2025Event Extraction in Large Language Modelcited by 5
  6. 2024Creating an AI Observer: Generative Semantic Workspacescited by 3
  7. 2023Zero- and Few-Shot Event Detection via Prompt-Based Meta Learningcited by 2
  8. 2024RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Promptscited by 2
  9. 2025LEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real Worldcited by 2
  10. 2024Plausible-Parrots @ MSP2023: Enhancing Semantic Plausibility Modeling using Entity and Event Knowledgecited by 0
  11. 2024Beyond Exact Match: Semantically Reassessing Event Extraction by Large Language Modelscited by 0
  12. 2026NEST: Narrative Event Structures in Time for Long Video Understandingcited by 0
1 more
  1. 2026A Scalable Cross-Domain Event Extraction System via a Unified Generative Training Frameworkcited by 0