Event Causality Extraction with Event Argument Correlations

Shiyao Cui, Jiawei Sheng, Xin Cong, Quangang Li, Tingwen Liu, Jinqiao Shi


Abstract
Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understanding. However, the ECI task ignores crucial event structure and cause-effect causality component information, making it struggle for downstream applications. In this paper, we introduce a novel task, namely Event Causality Extraction (ECE), aiming to extract the cause-effect event causality pairs with their structured event information from plain texts. The ECE task is more challenging since each event can contain multiple event arguments, posing fine-grained correlations between events to decide the cause-effect event pair. Hence, we propose a method with a dual grid tagging scheme to capture the intra- and inter-event argument correlations for ECE. Further, we devise a event type-enhanced model architecture to realize the dual grid tagging scheme. Experiments demonstrate the effectiveness of our method, and extensive analyses point out several future directions for ECE.
Anthology ID:
2022.coling-1.201
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2300–2312
Language:
URL:
https://aclanthology.org/2022.coling-1.201
DOI:
Bibkey:
Cite (ACL):
Shiyao Cui, Jiawei Sheng, Xin Cong, Quangang Li, Tingwen Liu, and Jinqiao Shi. 2022. Event Causality Extraction with Event Argument Correlations. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2300–2312, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Event Causality Extraction with Event Argument Correlations (Cui et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.201.pdf
Code
 cuishiyao96/ece