  Event Causality Identification (ECI) refers to the detection of causal
relations between events in texts. However, most existing studies focus on
sentence-level ECI with high-resource languages, leaving more challenging
document-level ECI (DECI) with low-resource languages under-explored. In this
paper, we propose a Heterogeneous Graph Interaction Model with
Multi-granularity Contrastive Transfer Learning (GIMC) for zero-shot
cross-lingual document-level ECI. Specifically, we introduce a heterogeneous
graph interaction network to model the long-distance dependencies between
events that are scattered over a document. Then, to improve cross-lingual
transferability of causal knowledge learned from the source language, we
propose a multi-granularity contrastive transfer learning module to align the
causal representations across languages. Extensive experiments show our
framework outperforms the previous state-of-the-art model by 9.4% and 8.2% of
average F1 score on monolingual and multilingual scenarios respectively.
Notably, in the multilingual scenario, our zero-shot framework even exceeds
GPT-3.5 with few-shot learning by 24.3% in overall performance.
