Event Causality Identification (ECI) refers to detect causal relations between events in texts. However, most existing studies focus on sentence-level ECI with high-resource language, 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 document. Then, to improve cross-lingual transferability of causal knowledge learned from source language, we propose a multi-granularity contrastive transfer learning module to align the causal representations across languages. Extensive experiments show our framework outperforms previous state-of-the-art model by 9.4% and 8.2% of average F1 score on monolingual and multilingual scenarios respectively. Notably, in multilingual scenario, our zero-shot framework even exceeds GPT-3.5 with few-shot learning by 24.3% in overall performance.

翻译：事件因果关系识别（ECI）旨在检测文本中事件之间的因果关联。然而，现有研究多聚焦于高资源语言的句子级ECI，而更具挑战性的低资源语言文档级ECI（DECI）仍待深入探索。本文提出一种基于多粒度对比迁移学习的异构图交互模型（GIMC），用于零样本跨语言文档级ECI。具体而言，我们引入异构图交互网络来建模文档中分散事件间的长距离依赖关系；随后，为提升源语言学习到的因果知识的跨语言迁移能力，设计多粒度对比迁移学习模块以对齐跨语言因果表征。大量实验表明，在单语言和多语言场景下，本框架平均F1值分别超出此前最优模型9.4%和8.2%。值得注意的是，在多语言场景中，本零样本框架的整体性能甚至超过采用少样本学习的GPT-3.5达24.3%。