What Would Happen Next? Predicting Consequences from An Event Causality Graph

Chuanhong Zhan, Wei Xiang, Liang Chao, Bang Wang


Abstract
Existing script event prediction task forcasts the subsequent event based on an event script chain. However, the evolution of historical events are more complicated in real world scenarios and the limited information provided by the event script chain also make it difficult to accurately predict subsequent events. This paper introduces a Causality Graph Event Prediction(CGEP) task that forecasting consequential event based on an Event Causality Graph (ECG). We propose a Semantic Enhanced Distance-sensitive Graph Prompt Learning (SeDGPL) Model for the CGEP task. In SeDGPL, (1) we design a Distance-sensitive Graph Linearization (DsGL) module to reformulate the ECG into a graph prompt template as the input of a PLM; (2) propose an Event-Enriched Causality Encoding (EeCE) module to integrate both event contextual semantic and graph schema information; (3) propose a Semantic Contrast Event Prediction (ScEP) module to enhance the event representation among numerous candidate events and predict consequential event following prompt learning paradigm. Experiment results validate our argument our proposed SeDGPL model outperforms the advanced competitors for the CGEP task.
Anthology ID:
2024.findings-emnlp.45
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
820–832
Language:
URL:
https://aclanthology.org/2024.findings-emnlp.45/
DOI:
10.18653/v1/2024.findings-emnlp.45
Bibkey:
Cite (ACL):
Chuanhong Zhan, Wei Xiang, Liang Chao, and Bang Wang. 2024. What Would Happen Next? Predicting Consequences from An Event Causality Graph. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 820–832, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
What Would Happen Next? Predicting Consequences from An Event Causality Graph (Zhan et al., Findings 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.findings-emnlp.45.pdf