@inproceedings{tong-etal-2020-improving,
title = "Improving Event Detection via Open-domain Trigger Knowledge",
author = "Tong, Meihan and
Xu, Bin and
Wang, Shuai and
Cao, Yixin and
Hou, Lei and
Li, Juanzi and
Xie, Jun",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.522",
doi = "10.18653/v1/2020.acl-main.522",
pages = "5887--5897",
abstract = "Event Detection (ED) is a fundamental task in automatically structuring texts. Due to the small scale of training data, previous methods perform poorly on unseen/sparsely labeled trigger words and are prone to overfitting densely labeled trigger words. To address the issue, we propose a novel Enrichment Knowledge Distillation (EKD) model to leverage external open-domain trigger knowledge to reduce the in-built biases to frequent trigger words in annotations. Experiments on benchmark ACE2005 show that our model outperforms nine strong baselines, is especially effective for unseen/sparsely labeled trigger words. The source code is released on \url{https://github.com/shuaiwa16/ekd.git}.",
}
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<abstract>Event Detection (ED) is a fundamental task in automatically structuring texts. Due to the small scale of training data, previous methods perform poorly on unseen/sparsely labeled trigger words and are prone to overfitting densely labeled trigger words. To address the issue, we propose a novel Enrichment Knowledge Distillation (EKD) model to leverage external open-domain trigger knowledge to reduce the in-built biases to frequent trigger words in annotations. Experiments on benchmark ACE2005 show that our model outperforms nine strong baselines, is especially effective for unseen/sparsely labeled trigger words. The source code is released on https://github.com/shuaiwa16/ekd.git.</abstract>
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%0 Conference Proceedings
%T Improving Event Detection via Open-domain Trigger Knowledge
%A Tong, Meihan
%A Xu, Bin
%A Wang, Shuai
%A Cao, Yixin
%A Hou, Lei
%A Li, Juanzi
%A Xie, Jun
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F tong-etal-2020-improving
%X Event Detection (ED) is a fundamental task in automatically structuring texts. Due to the small scale of training data, previous methods perform poorly on unseen/sparsely labeled trigger words and are prone to overfitting densely labeled trigger words. To address the issue, we propose a novel Enrichment Knowledge Distillation (EKD) model to leverage external open-domain trigger knowledge to reduce the in-built biases to frequent trigger words in annotations. Experiments on benchmark ACE2005 show that our model outperforms nine strong baselines, is especially effective for unseen/sparsely labeled trigger words. The source code is released on https://github.com/shuaiwa16/ekd.git.
%R 10.18653/v1/2020.acl-main.522
%U https://aclanthology.org/2020.acl-main.522
%U https://doi.org/10.18653/v1/2020.acl-main.522
%P 5887-5897
Markdown (Informal)
[Improving Event Detection via Open-domain Trigger Knowledge](https://aclanthology.org/2020.acl-main.522) (Tong et al., ACL 2020)
ACL
- Meihan Tong, Bin Xu, Shuai Wang, Yixin Cao, Lei Hou, Juanzi Li, and Jun Xie. 2020. Improving Event Detection via Open-domain Trigger Knowledge. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5887–5897, Online. Association for Computational Linguistics.