Extensively Matching for Few-shot Learning Event Detection
This addresses the issue of limited generalization in event detection for NLP applications, but it is incremental as it builds on existing metric-based few-shot learning models.
The paper tackles the problem of event detection models failing to transfer to new event types by formulating it as a few-shot learning problem, and the proposed method improves performance on the ACE-2005 dataset under this setting.
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