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.

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，现有研究大多聚焦于高资源语言的句子级ECI，对低资源语言下更具挑战性的文档级ECI（DECI）探索不足。本文提出一种基于多粒度对比迁移学习的异构图文交互模型（GIMC），用于零样本跨语言文档级ECI。具体而言，我们引入异构图文交互网络建模文档中散布事件间的长距离依赖关系。为提升从源语言习得的因果知识的跨语言迁移能力，我们提出多粒度对比迁移学习模块以对齐跨语言因果表示。大量实验表明，在单语言与多语言场景下，本框架较先前最优模型平均F1值分别提升9.4%和8.2%。值得注意的是，在多语言场景中，本零样本框架的总体性能甚至超越采用少样本学习的GPT-3.5达24.3%。