  In supervised event detection, most of the mislabeling occurs between a small
number of confusing type pairs, including trigger-NIL pairs and sibling
sub-types of the same coarse type. To address this label confusion problem,
this paper proposes cost-sensitive regularization, which can force the training
procedure to concentrate more on optimizing confusing type pairs. Specifically,
we introduce a cost-weighted term into the training loss, which penalizes more
on mislabeling between confusing label pairs. Furthermore, we also propose two
estimators which can effectively measure such label confusion based on
instance-level or population-level statistics. Experiments on TAC-KBP 2017
datasets demonstrate that the proposed method can significantly improve the
performances of different models in both English and Chinese event detection.
