What Would Happen Next? Predicting Consequences from An Event Causality Graph

TL;DR

This paper introduces a new task called Causality Graph Event Prediction (CGEP) that predicts future events based on an Event Causality Graph, and proposes a novel model SeDGPL that outperforms existing methods.

Contribution

The paper presents a new CGEP task and a Semantic Enhanced Distance-sensitive Graph Prompt Learning model with modules for graph linearization, causality encoding, and semantic contrast, advancing event prediction methods.

Findings

SeDGPL outperforms existing models on CGEP datasets.

The Distance-sensitive Graph Linearization effectively reformulates ECGs for PLMs.

Event-Enriched Causality Encoding improves semantic understanding in predictions.

Abstract

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.