Event detection (ED) is aimed to identify the key trigger words in
unstructured text and predict the event types accordingly. Traditional ED
models are too data-hungry to accommodate real applications with scarce labeled
data. Besides, typical ED models are facing the context-bypassing and disabled
generalization issues caused by the trigger bias stemming from ED datasets.
Therefore, we focus on the true few-shot paradigm to satisfy the low-resource
scenarios. In particular, we propose a multi-step prompt learning model
(MsPrompt) for debiasing few-shot event detection, that consists of the
following three components: an under-sampling module targeting to construct a
novel training set that accommodates the true few-shot setting, a multi-step
prompt module equipped with a knowledge-enhanced ontology to leverage the event
semantics and latent prior knowledge in the PLMs sufficiently for tackling the
context-bypassing problem, and a prototypical module compensating for the
weakness of classifying events with sparse data and boost the generalization
performance. Experiments on two public datasets ACE-2005 and FewEvent show that
MsPrompt can outperform the state-of-the-art models, especially in the strict
low-resource scenarios reporting 11.43% improvement in terms of weighted
F1-score against the best-performing baseline and achieving an outstanding
debiasing performance