Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning | |
Zhitao He1,2; Pengfei Cao1,2; Zhuoran Jin1,2; Yubo Chen1,2; Kang Liu1,2; Jun Zhao1,2 | |
2024-05 | |
会议日期 | 2024-5 |
会议地点 | Torino, Italia |
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. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/57558] |
专题 | 复杂系统认知与决策实验室 |
通讯作者 | Jun Zhao |
作者单位 | 1.The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Zhitao He,Pengfei Cao,Zhuoran Jin,et al. Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning[C]. 见:. Torino, Italia. 2024-5. |
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