What Would Happen Next? Predicting Consequences from An Event Causality Graph (2024)

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. Experiment results validate our argument our proposed SeDGPL model outperforms the advanced competitors for the CGEP task.

## introduction event prediction aims to forecast the consequential event that are most likely to happen next, based on historical events and their relationships. it has significant applications in many scenarios and industries, such as dialogue systems @xcite , discourse understanding @xcite , and story generation @xcite . existing script event prediction task @xcite predicts the subsequent event given a sequence of events, named event script chain. however, we argue that the evolution of historical events are more complicated than a script event chain in real world scenarios. besides, the limited information provided by event chains also make it difficult to accurately predict subsequent events. motivated from such considerations, this paper introduces a causality graph event prediction(cgep) task that forecasting consequential event based on an event causality graph (ecg). as illustrated in fig. 1 , the cgep task is to select the most likely consequential event from candidate set based on an input ecg and an selected anchor event. instead of using event script chain for subsequent event prediction, we model the connection between events by an ecg, which can better reveal the evolution of historical events. besides, an ecg may have more than one consequential event that are likely to happen next. as such, we predict a consequential event for each tail node event (i.e. the anchor event) in an ecg, to achieve a more comprehensive understanding of events' evolution. traditional event prediction methods either encode the contextual semantic of events @xcite or model the information of graph structure @xcite for event forcasting. the recently emerged prompt learning paradigm, based on pre-trained language model (plm), exhibits outstanding ability in logical reasoning and has been applied in many natural language processing tasks @xcite . however, most plms take text sequences as input and struggle to process graph-structured inputs. in this paper, we use graph prompt learning paradigm to linearize the input ecg, so as to utilize the encyclopedia-like knowledge embedded in a plm for prediction. besides, some studies obtain common sense knowledge from external knowledge bases to augment event prediction @xcite . this again validates our argument that the event chain input contains insufficiency information of historical event. by contrast our ecg input itself has included abundant historical events and causalities information. to this end, we enrich the event representation by integrating event contextual semantic and graph schema information from the input ecgs. furthermore, we select the consequential event from a significantly larger candidate set than that of event script prediction task. and a semantic contrastive learning is used to enhance the event representation among numerous candidate events. ## methodology we propose a semantic enhanced distancesensitive graph prompt learning model (sedgpl) for causality graph event prediction. as illustrated in figure 3 , the sedgpl contains three modules: (1 ## concluding remarks in this paper, we argue that predicting consequential events based on the event causality graph is more effective than predicting based on the event script chain. to validate our argument, we propose the sedgpl model, a distance-sensitive graph prompt model that integrate both event contextual semantic and graph schema information, and conduct abundant experiments on both cgep and sep task. experiment results validate our argument, and our proposed sedgpl model outperforms the advanced competitors for the cgep task. in our future work, we shall attempt to integrate other types of event relationships, e.g. temporal relations, to assist in event prediction.