Improving Event Detection via Open-domain Trigger Knowledge
Meihan Tong, Bin Xu, Shuai Wang, Yixin Cao, Lei Hou, Juanzi Li, Jun Xie
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.- Anthology ID:
- 2020.acl-main.522
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5887–5897
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.522
- DOI:
- 10.18653/v1/2020.acl-main.522
- Cite (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.
- Cite (Informal):
- Improving Event Detection via Open-domain Trigger Knowledge (Tong et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2020.acl-main.522.pdf
- Code
- shuaiwa16/ekd