Improving event detection via open-domain event trigger knowledge

Author

Meihan TONG
Bin XU
Shuai WANG
Yixin CAO, Singapore Management UniversityFollow
Lei HOU
Juanzi LI
Jun XIE

Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2020

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.

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Virtual Conference, 2020 July 5-10

First Page

5887

Last Page

5897

Identifier

10.18653/v1/2020.acl-main.522

Publisher

Association for Computational Linguistics

City or Country

Virtual Conference

Citation

TONG, Meihan; XU, Bin; WANG, Shuai; CAO, Yixin; HOU, Lei; LI, Juanzi; and XIE, Jun. Improving event detection via open-domain event trigger knowledge. (2020). Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Virtual Conference, 2020 July 5-10. 5887-5897.
Available at: https://ink.library.smu.edu.sg/sis_research/7450

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Additional URL

http://doi.org/10.18653/v1/2020.acl-main.522