Related papers: Extensively Matching for Few-shot Learning Event Detection

Extensively Matching for Few-shot Learning Event Detection

URL: http://arxiv.org/abs/2006.10093v1
Date: Wed, 17 Jun 2020 18:30:30 GMT
Title: Extensively Matching for Few-shot Learning Event Detection
Authors: Viet Dac Lai, Franck Dernoncourt, Thien Huu Nguyen
Abstract summary: Event detection models under super-vised learning settings fail to transfer to new event types. Few-shot learning has not beenexplored in event detection. We propose two novelloss factors that matching examples in the sup-port set to provide more training signals to themodel.
Score: 66.31312496170139
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: 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

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