%0 Conference Proceedings %T Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning %A Lu, Yaojie %A Lin, Hongyu %A Han, Xianpei %A Sun, Le %Y Korhonen, Anna %Y Traum, David %Y Màrquez, Lluís %S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics %D 2019 %8 July %I Association for Computational Linguistics %C Florence, Italy %F lu-etal-2019-distilling %X 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 Delta-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. %R 10.18653/v1/P19-1429 %U https://aclanthology.org/P19-1429 %U https://doi.org/10.18653/v1/P19-1429 %P 4366-4376