@inproceedings{lu-etal-2019-distilling,
title = "Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning",
author = "Lu, Yaojie and
Lin, Hongyu and
Han, Xianpei and
Sun, Le",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1429",
doi = "10.18653/v1/P19-1429",
pages = "4366--4376",
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.",
}
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<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.</abstract>
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%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
Markdown (Informal)
[Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning](https://aclanthology.org/P19-1429) (Lu et al., ACL 2019)
ACL