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
- 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)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/P19-1429.pdf
- Code
- luyaojie/delta-learning-for-ed