Open Event Causality Extraction by the Assistance of LLM in Task Annotation, Dataset, and Method

Kun Luo, Tong Zhou, Yubo Chen, Jun Zhao, Kang Liu

Abstract
Event Causality Extraction (ECE) aims to extract explicit causal relations between event pairs from the text. However, the event boundary deviation and the causal event pair mismatching are two crucial challenges that remain unaddressed. To address the above issues, we propose a paradigm to utilize LLM to optimize the task definition, evolve the datasets, and strengthen our proposed customized Contextual Highlighting Event Causality Extraction framework (CHECE). Specifically in CHECE, we propose an Event Highlighter and an Event Concretization Module, guiding the model to represent the event by a higher-level cluster and consider its causal counterpart in event boundary prediction to deal with event boundary deviation. And we propose a Contextual Event Causality Matching mechanism, meanwhile, applying LLM to diversify the content templates to force the model to learn causality from context to targeting on causal event pair mismatching. Experimental results on two ECE datasets demonstrate the effectiveness of our method.
Anthology ID:
2024.neusymbridge-1.4
Volume:
Proceedings of the Workshop: Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning (NeusymBridge) @ LREC-COLING-2024
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Tiansi Dong, Erhard Hinrichs, Zhen Han, Kang Liu, Yangqiu Song, Yixin Cao, Christian F. Hempelmann, Rafet Sifa
Venues:
NeusymBridge | WS
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
33–44
Language:
URL:
https://aclanthology.org/2024.neusymbridge-1.4
DOI:
Bibkey:
Cite (ACL):
Kun Luo, Tong Zhou, Yubo Chen, Jun Zhao, and Kang Liu. 2024. Open Event Causality Extraction by the Assistance of LLM in Task Annotation, Dataset, and Method. In Proceedings of the Workshop: Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning (NeusymBridge) @ LREC-COLING-2024, pages 33–44, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Open Event Causality Extraction by the Assistance of LLM in Task Annotation, Dataset, and Method (Luo et al., NeusymBridge-WS 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.neusymbridge-1.4.pdf
PDF Search