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（Event Causality Identification, ECI）旨在检测两个给定文本事件之间是否存在因果关系，是理解事件因果关系的重要任务。然而，ECI任务忽略了关键的事件结构与因果效应成分信息，使其难以支撑下游应用。本文探索了一个新任务——事件因果关系抽取（Event Causality Extraction, ECE），旨在从纯文本中提取具有结构化事件信息的因果事件对。ECE任务更具挑战性，因为每个事件可能包含多个事件参数，从而在事件之间施加细粒度关联以确定因果事件对。为此，我们提出了一种基于双网格标注方案的方法，用于捕获ECE中事件内与事件间的参数关联。进一步，我们设计了一种事件类型增强的模型架构来实现双网格标注方案。实验证明了我们方法的有效性，广泛的分析为ECE指出了若干未来研究方向。