Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning
arxiv(2024)
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
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
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