  Event detection has long been troubled by the \emph{trigger curse}:
overfitting the trigger will harm the generalization ability while underfitting
it will hurt the detection performance. This problem is even more severe in
few-shot scenario. In this paper, we identify and solve the trigger curse
problem in few-shot event detection (FSED) from a causal view. By formulating
FSED with a structural causal model (SCM), we found that the trigger is a
confounder of the context and the result, which makes previous FSED methods
much easier to overfit triggers. To resolve this problem, we propose to
intervene on the context via backdoor adjustment during training. Experiments
show that our method significantly improves the FSED on ACE05, MAVEN and KBP17
datasets.
