Distilling Discrimination and Generalization Knowledge for Event
Detection via ?-Representation Learning
Abstract
Event detection systems rely on discrimination knowledge to distinguish ambiguous trigger words and generalization knowledge to
detect unseen/sparse trigger words. Current neural event detection approaches focus on trigger-centric representations, which
work well on distilling discrimination knowledge, but poorly on learning generalization
knowledge. To address this problem, this paper proposes a ?-learning approach to distill discrimination and generalization knowledge by effectively decoupling, incrementally
learning and adaptively fusing event representation. Experiments show that our method
significantly outperforms previous approaches
on unseen/sparse trigger words, and achieves
state-of-the-art performance on both ACE2005
and KBP2017 datasets