Abstract
We consider event extraction in a generative manner with template-based conditional generation.Although there is a rising trend of casting the task of event extraction as a sequence generation problem with prompts, these generation-based methods have two significant challenges, including using suboptimal prompts and static event type information.In this paper, we propose a generative template-based event extraction method with dynamic prefix (GTEE-DynPref) by integrating context information with type-specific prefixes to learn a context-specific prefix for each context.Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model OneIE on ACE 2005 and achieves the best performances on ERE.Additionally, our model is proven to be portable to new types of events effectively.- Anthology ID:
- 2022.acl-long.358
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5216–5228
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.358
- DOI:
- 10.18653/v1/2022.acl-long.358
- Cite (ACL):
- Xiao Liu, Heyan Huang, Ge Shi, and Bo Wang. 2022. Dynamic Prefix-Tuning for Generative Template-based Event Extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5216–5228, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Dynamic Prefix-Tuning for Generative Template-based Event Extraction (Liu et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2022.acl-long.358.pdf