DeepAI AI Chat
Log In Sign Up

Extensively Matching for Few-shot Learning Event Detection

by   Viet Dac Lai, et al.
University of Oregon

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


page 1

page 2

page 3

page 4


Exploiting the Matching Information in the Support Set for Few Shot Event Classification

The existing event classification (EC) work primarily focuseson the trad...

PILED: An Identify-and-Localize Framework for Few-Shot Event Detection

Practical applications of event extraction systems have long been hinder...

Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection

Event detection (ED) aims at detecting event trigger words in sentences ...

Dense Classification and Implanting for Few-Shot Learning

Training deep neural networks from few examples is a highly challenging ...

Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention

Event detection has long been troubled by the trigger curse: overfitting...

Extending Event Detection to New Types with Learning from Keywords

Traditional event detection classifies a word or a phrase in a given sen...

Few-shot tweet detection in emerging disaster events

Social media sources can provide crucial information in crisis situation...