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
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
Bibkey:
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)
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https://preview.aclanthology.org/remove-xml-comments/2023.matching-1.4.pdf
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