论文标题
ERGO：文档级事件事件因果关系识别的事件关系图形变压器
ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification
论文作者
论文摘要
文档级事件因果关系识别（DECI）旨在确定文档中事件对之间的因果关系。它在没有明确的因果指标的情况下构成了跨句子推理的巨大挑战。在本文中，我们提出了一个新颖的事件关系图形变压器（ERGO）的DECI框架，该框架改善了两个方面的现有最新方法（SOTA）方法。首先，我们通过构建事件关系图来将DECI作为节点分类问题，而无需先验知识或工具。其次，ERGO无缝整合事件对关系分类和全局推断，该分类利用关系图变压器（RGT）捕获潜在的因果链。此外，我们介绍了边缘建设策略和自适应焦点损失，以应对由普通虚假相关性引起的巨大误报。在两个基准数据集上进行的广泛实验表明，ERGO明显优于先前的SOTA方法（平均13.1％的F1增长）。我们已经进行了广泛的定量分析和案例研究，为未来的研究方向提供了见解（第4.8节）。
Document-level Event Causality Identification (DECI) aims to identify causal relations between event pairs in a document. It poses a great challenge of across-sentence reasoning without clear causal indicators. In this paper, we propose a novel Event Relational Graph TransfOrmer (ERGO) framework for DECI, which improves existing state-of-the-art (SOTA) methods upon two aspects. First, we formulate DECI as a node classification problem by constructing an event relational graph, without the needs of prior knowledge or tools. Second, ERGO seamlessly integrates event-pair relation classification and global inference, which leverages a Relational Graph Transformer (RGT) to capture the potential causal chain. Besides, we introduce edge-building strategies and adaptive focal loss to deal with the massive false positives caused by common spurious correlation. Extensive experiments on two benchmark datasets show that ERGO significantly outperforms previous SOTA methods (13.1% F1 gains on average). We have conducted extensive quantitative analysis and case studies to provide insights for future research directions (Section 4.8).
