基于因果图预测事件后续影响，提升真实场景下事件预测准确性。
What Would Happen Next? Predicting Consequences from An Event Causality Graph
- 构建事件因果图，用图提示学习捕捉复杂演化关系
- 在MAVEN-ERE和ESC数据集上显著优于现有方法
- 适合需要理解事件深层因果的智能决策系统研究者
现有剧本事件预测任务基于事件链序列预测后续事件。然而现实场景中历史事件演化更为复杂，且事件链提供的信息有限，难以准确预测后续事件。本文提出一种基于事件因果图（ECG）的因果图事件预测（CGEP）任务，并设计了语义增强的距离敏感图提示学习（SeDGPL）模型。该模型包含：(1) 距离敏感图线性化（DsGL）模块，将ECG重构为PLM可处理的图提示模板；(2) 事件增强因果编码（EeCE）模块，融合事件上下文语义与图结构信息；(3) 语义对比事件预测（ScEP）模块，通过提示学习增强候选事件表征并预测后续事件。基于MAVEN-ERE和ESC语料库构建两个CGEP数据集进行实验，结果验证了所提模型在CGEP任务上的优越性。
原文摘要 · Abstract (English)
Existing script event prediction task forcasts the subsequent event based on an event script chain. However, the evolution of historical events are more complicated in real world scenarios and the limited information provided by the event script chain also make it difficult to accurately predict subsequent events. This paper introduces a Causality Graph Event Prediction(CGEP) task that forecasting consequential event based on an Event Causality Graph (ECG). We propose a Semantic Enhanced Distance-sensitive Graph Prompt Learning (SeDGPL) Model for the CGEP task. In SeDGPL, (1) we design a Distance-sensitive Graph Linearization (DsGL) module to reformulate the ECG into a graph prompt template as the input of a PLM; (2) propose an Event-Enriched Causality Encoding (EeCE) module to integrate both event contextual semantic and graph schema information; (3) propose a Semantic Contrast Event Prediction (ScEP) module to enhance the event representation among numerous candidate events and predict consequential event following prompt learning paradigm. %We construct two CGEP datasets based on existing MAVEN-ERE and ESC corpus for experiments. Experiment results validate our argument our proposed SeDGPL model outperforms the advanced competitors for the CGEP task.
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