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.
翻译:事件因果关系识别(ECI)旨在检测两种特定文字事件之间是否存在因果关系,这是了解事件因果关系的一项重要任务,然而,ECI的任务忽略了关键事件结构和因果关系部分信息,使其难以在下游应用中挣扎。在本文中,我们探索了一项新颖的任务,即Causility Explicationon(ECE),目的是从简单的文本中提取因果关系事件与事件结构信息之间的因果关系。欧洲经委会的任务更具挑战性,因为每个事件都可以包含多个事件参数,在事件之间形成细微的关联,以决定因果关系事件对等。因此,我们提出了一个具有双重电网标记计划的方法,以捕捉欧洲经委会内部和事件间参数的相关性。此外,我们设计了一个事件类型强化模型结构,以实现双重电网标记计划。实验显示了我们的方法的有效性,并广泛分析指出了欧洲经委会的未来方向。