  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.
