COFFEE: A Contrastive Oracle-Free Framework for Event Extraction
Meiru Zhang, Yixuan Su, Zaiqiao Meng, Zihao Fu, Nigel Collier
Abstract
Event extraction is a complex task that involves extracting events from unstructured text. Prior classification-based methods require comprehensive entity annotations for joint training, while newer generation-based methods rely on heuristic templates containing oracle information such as event type, which is often unavailable in real-world scenarios. In this study, we consider a more realistic task setting, namely the Oracle-Free Event Extraction (OFEE) task, where only the input context is given, without any oracle information including event type, event ontology, or trigger word. To address this task, we propose a new framework, COFFEE. This framework extracts events solely based on the document context, without referring to any oracle information. In particular, COFFEE introduces a contrastive selection model to refine the generated triggers and handle multi-event instances. Our proposed COFFEE outperforms state-of-the-art approaches in the oracle-free setting of the event extraction task, as evaluated on two public variants of the ACE05 benchmark. The code used in our study has been made publicly available.- Anthology ID:
- 2023.matching-1.4
- Volume:
- Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, ON, Canada
- Editors:
- Estevam Hruschka, Tom Mitchell, Sajjadur Rahman, Dunja Mladenić, Marko Grobelnik
- Venue:
- MATCHING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 33–44
- Language:
- URL:
- https://aclanthology.org/2023.matching-1.4
- DOI:
- 10.18653/v1/2023.matching-1.4
- Cite (ACL):
- Meiru Zhang, Yixuan Su, Zaiqiao Meng, Zihao Fu, and Nigel Collier. 2023. COFFEE: A Contrastive Oracle-Free Framework for Event Extraction. In Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023), pages 33–44, Toronto, ON, Canada. Association for Computational Linguistics.
- Cite (Informal):
- COFFEE: A Contrastive Oracle-Free Framework for Event Extraction (Zhang et al., MATCHING 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.matching-1.4.pdf