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arXiv · 2301.11621

Event Causality Extraction with Event Argument Correlations

Shiyao Cui·Jiawei Sheng·Xin Cong·QuanGang Li·Tingwen Liu·Jinqiao Shi
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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 explore 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 causeeffect 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.

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Shiyao Cui, Jiawei Sheng, Xin Cong, QuanGang Li, Tingwen Liu, Jinqiao Shi. 2023-01-27. Event Causality Extraction with Event Argument Correlations. https://arxiv.org/abs/2301.11621

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