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.
Abstract (translated)
事件因果关系识别(ECI)旨在检测给定文本事件之间的因果关系是否存在,这是事件因果关系理解的一个重要任务。然而,ECI任务忽略了关键的事件结构和因果关系组件信息,这使得它难以为后续应用程序提供支持。在本文中,我们探讨了一个新任务,即事件因果关系提取(ECE),旨在从普通文本中提取因果关系事件及其结构化事件信息,以便确定因果关系事件对。ECE任务更加具有挑战性,因为每个事件可能包含多个事件论据,需要在事件之间提供精细的因果关系关系以确定因果关系事件对。因此,我们提出了一种使用双重网格标签方案来捕捉内部和事件之间论据关系的方法,并设计了一个事件类型增强模型架构,以实现双重网格标签方案。实验结果表明我们的方法的有效性,并广泛的分析指出了ECE的几个未来方向。
URL
https://arxiv.org/abs/2301.11621
