What Would Happen Next? Predicting Consequences from An Event Causality Graph
This work addresses the challenge of event prediction in AI/NLP by moving beyond simple script chains to handle more complicated event evolutions, though it appears incremental as it builds on existing graph and prompt learning methods.
The paper tackles the problem of predicting subsequent events in complex real-world scenarios by introducing the Causality Graph Event Prediction (CGEP) task, which uses an Event Causality Graph instead of a script chain, and proposes the SeDGPL model that outperforms advanced competitors on constructed datasets.
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.