# Extensively Matching for Few-shot Learning Event Detection

arXiv: https://arxiv.org/abs/2006.10093 · HTML: https://mlsanity.com/paper/2006.10093/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 18 of 18)

- [Document-Level Event Argument Extraction by Conditional Generation](https://mlsanity.com/paper/2104.05919/) (2021)
- [OntoED: Low-resource Event Detection with Ontology Embedding](https://mlsanity.com/paper/2105.10922/) (2021)
- [COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective](https://mlsanity.com/paper/2305.05191/) (2023)
- [Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection](https://mlsanity.com/paper/2105.09509/) (2021)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention](https://mlsanity.com/paper/2109.05747/) (2021)
- [The Art of Prompting: Event Detection based on Type Specific Prompts](https://mlsanity.com/paper/2204.07241/) (2022)
- [Few-Shot Document-Level Event Argument Extraction](https://mlsanity.com/paper/2209.02203/) (2022)
- [Behind the Scenes: An Exploration of Trigger Biases Problem in Few-Shot Event Classification](https://mlsanity.com/paper/2108.12844/) (2021)
- [FEET: A Framework for Evaluating Embedding Techniques](https://mlsanity.com/paper/2411.01322/) (2024)
- [Few-shot Event Detection: An Empirical Study and a Unified View](https://mlsanity.com/paper/2305.01901/) (2023)
- [Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detection](https://mlsanity.com/paper/2305.14163/) (2023)
- [Targeted Augmentation for Low-Resource Event Extraction](https://mlsanity.com/paper/2405.08729/) (2024)
- [Event Extraction: A Survey](https://mlsanity.com/paper/2210.03419/) (2022)
- [Continual Few-shot Event Detection via Hierarchical Augmentation Networks](https://mlsanity.com/paper/2403.17733/) (2024)
- [HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold](https://mlsanity.com/paper/2210.08806/) (2022)
- [P4E: Few-Shot Event Detection as Prompt-Guided Identification and Localization](https://mlsanity.com/paper/2202.07615/) (2022)
- [Trigger-free Event Detection via Derangement Reading Comprehension](https://mlsanity.com/paper/2208.09659/) (2022)

## 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