基于异构图对比迁移学习的零样本跨语言文档级事件因果关系识别
Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning
AI总结：
本文提出GIMC框架，通过异构图交互网络建模文档级事件长距离依赖，并利用多粒度对比迁移学习对齐跨语言因果表示，实现零样本跨语言文档级事件因果关系识别，性能显著优于现有最优模型及GPT-3.5。
摘要：
事件因果关系识别（ECI）指检测文本中事件之间的因果关系。然而，现有研究多关注高资源语言的句子级ECI，对更具挑战性的低资源语言文档级ECI（DECI）探索不足。本文提出一种结合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。随后，为提升从源语言学到的因果知识的跨语言可迁移性，我们提出多粒度对比迁移学习模块以对齐跨语言的因果表示。大量实验表明，我们的框架在单语和双语场景下分别以9.4%和8.2%的平均F1分数超越先前最优模型。值得注意的是，在多语言场景中，我们的零样本框架在整体性能上甚至以24.3%的优势超越了采用少样本学习的GPT-3.5。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
