# Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection

arXiv: https://arxiv.org/abs/1910.11621 · HTML: https://mlsanity.com/paper/1910.11621/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 30 of 54)

- [Meta-learning via Language Model In-context Tuning](https://mlsanity.com/paper/2110.07814/) (2021)
- [Document-Level Event Argument Extraction by Conditional Generation](https://mlsanity.com/paper/2104.05919/) (2021)
- [Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2108.13161/) (2021)
- [LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities](https://mlsanity.com/paper/2305.13168/) (2023)
- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021)
- [ClusterLLM: Large Language Models as a Guide for Text Clustering](https://mlsanity.com/paper/2305.14871/) (2023)
- [Instruct and Extract: Instruction Tuning for On-Demand Information Extraction](https://mlsanity.com/paper/2310.16040/) (2023)
- [AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba](https://mlsanity.com/paper/2106.01686/) (2021)
- [TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction](https://mlsanity.com/paper/2311.09562/) (2023)
- [Ontology-enhanced Prompt-tuning for Few-shot Learning](https://mlsanity.com/paper/2201.11332/) (2022)
- [OntoED: Low-resource Event Detection with Ontology Embedding](https://mlsanity.com/paper/2105.10922/) (2021)
- [Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2106.15167/) (2021)
- [DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population](https://mlsanity.com/paper/2201.03335/) (2022)
- [MLBiNet: A Cross-Sentence Collective Event Detection Network](https://mlsanity.com/paper/2105.09458/) (2021)
- [GLEN: General-Purpose Event Detection for Thousands of Types](https://mlsanity.com/paper/2303.09093/) (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)
- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (2021)
- [Information Extraction in Low-Resource Scenarios: Survey and Perspective](https://mlsanity.com/paper/2202.08063/) (2022)
- [Prompt Tuning based Adapter for Vision-Language Model Adaption](https://mlsanity.com/paper/2303.15234/) (2023)
- [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)
- [Event Detection from Social Media for Epidemic Prediction](https://mlsanity.com/paper/2404.01679/) (2024)
- [SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness](https://mlsanity.com/paper/2410.18393/) (2024)
- [Scaling Laws for Many-Shot In-Context Learning with Self-Generated Annotations](https://mlsanity.com/paper/2503.03062/) (2025)
- [This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text](https://mlsanity.com/paper/2210.08500/) (2022)
- [Few-shot Event Detection: An Empirical Study and a Unified View](https://mlsanity.com/paper/2305.01901/) (2023)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)

## Abstract (arXiv)

Event detection (ED), a sub-task of event extraction, involves identifying triggers and categorizing event mentions. Existing methods primarily rely upon supervised learning and require large-scale labeled event datasets which are unfortunately not readily available in many real-life applications. In this paper, we consider and reformulate the ED task with limited labeled data as a Few-Shot Learning problem. We propose a Dynamic-Memory-Based Prototypical Network (DMB-PN), which exploits Dynamic Memory Network (DMN) to not only learn better prototypes for event types, but also produce more robust sentence encodings for event mentions. Differing from vanilla prototypical networks simply computing event prototypes by averaging, which only consume event mentions once, our model is more robust and is capable of distilling contextual information from event mentions for multiple times due to the multi-hop mechanism of DMNs. The experiments show that DMB-PN not only deals with sample scarcity better than a series of baseline models but also performs more robustly when the variety of event types is relatively large and the instance quantity is extremely small.