mlsanityData to 7 Oct 2026

Extensively Matching for Few-shot Learning Event Detection

arXiv 2006.10093 ↗First posted 17 Jun 2020cs.CLcited by 18

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Abstract (arXiv)

Current event detection models under super-vised learning settings fail to transfer to newevent types. Few-shot learning has not beenexplored in event detection even though it al-lows a model to perform well with high gener-alization on new event types. In this work, weformulate event detection as a few-shot learn-ing problem to enable to extend event detec-tion to new event types. We propose two novelloss factors that matching examples in the sup-port set to provide more training signals to themodel. Moreover, these training signals can beapplied in many metric-based few-shot learn-ing models. Our extensive experiments on theACE-2005 dataset (under a few-shot learningsetting) show that the proposed method can im-prove the performance of few-shot learning

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jun 2020.

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Citing papers per year: 2021: 6; 2022: 6; 2023: 3; 2024: 3.

18 papers.

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  1. 2021Document-Level Event Argument Extraction by Conditional Generationcited by 154
  2. 2021OntoED: Low-resource Event Detection with Ontology Embeddingcited by 23
  3. 2023COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspectivecited by 22
  4. 2021Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detectioncited by 13
  5. 2021A Survey on Deep Learning Event Extraction: Approaches and Applicationscited by 12
  6. 2021Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Interventioncited by 9
  7. 2022The Art of Prompting: Event Detection based on Type Specific Promptscited by 9
  8. 2022Few-Shot Document-Level Event Argument Extractioncited by 9
  9. 2021Behind the Scenes: An Exploration of Trigger Biases Problem in Few-Shot Event Classificationcited by 8
  10. 2024FEET: A Framework for Evaluating Embedding Techniquescited by 8
  11. 2023Few-shot Event Detection: An Empirical Study and a Unified Viewcited by 5
  12. 2023Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detectioncited by 4
6 more
  1. 2024Targeted Augmentation for Low-Resource Event Extractioncited by 4
  2. 2022Event Extraction: A Surveycited by 3
  3. 2024Continual Few-shot Event Detection via Hierarchical Augmentation Networkscited by 3
  4. 2022HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Thresholdcited by 2
  5. 2022P4E: Few-Shot Event Detection as Prompt-Guided Identification and Localizationcited by 1
  6. 2022Trigger-free Event Detection via Derangement Reading Comprehensioncited by 1