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    <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.</abstract>
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%0 Conference Proceedings
%T COFFEE: A Contrastive Oracle-Free Framework for Event Extraction
%A Zhang, Meiru
%A Su, Yixuan
%A Meng, Zaiqiao
%A Fu, Zihao
%A Collier, Nigel
%Y Hruschka, Estevam
%Y Mitchell, Tom
%Y Rahman, Sajjadur
%Y Mladenić, Dunja
%Y Grobelnik, Marko
%S Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, ON, Canada
%F zhang-etal-2023-coffee
%X 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.
%R 10.18653/v1/2023.matching-1.4
%U https://aclanthology.org/2023.matching-1.4
%U https://doi.org/10.18653/v1/2023.matching-1.4
%P 33-44

Markdown (Informal)

[COFFEE: A Contrastive Oracle-Free Framework for Event Extraction](https://aclanthology.org/2023.matching-1.4) (Zhang et al., MATCHING 2023)

• COFFEE: A Contrastive Oracle-Free Framework for Event Extraction (Zhang et al., MATCHING 2023)

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.