Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning

Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun


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 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.
Anthology ID:
P19-1429
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4366–4376
Language:
URL:
https://aclanthology.org/P19-1429
DOI:
10.18653/v1/P19-1429
Bibkey:
Cite (ACL):
Yaojie Lu, Hongyu Lin, Xianpei Han, and Le Sun. 2019. Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4366–4376, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning (Lu et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1429.pdf
Video:
 https://aclanthology.org/P19-1429.mp4
Code
 luyaojie/delta-learning-for-ed