Summary of What Would Happen Next? Predicting Consequences From An Event Causality Graph, by Chuanhong Zhan and Wei Xiang and Chao Liang and Bang Wang
What Would Happen Next? Predicting Consequences from An Event Causality Graph
by Chuanhong Zhan, Wei Xiang, Chao Liang, Bang Wang
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to script event prediction, introducing the Causality Graph Event Prediction (CGEP) task that forecasts consequential events based on an Event Causality Graph (ECG). The authors design a Semantic Enhanced Distance-sensitive Graph Prompt Learning (SeDGPL) Model for this task. This model consists of three modules: Distance-sensitive Graph Linearization (DsGL), Event-Enriched Causality Encoding (EeCE), and Semantic Contrast Event Prediction (ScEP). These modules reformulate the ECG into a graph prompt template, integrate event contextual semantic and graph schema information, and enhance event representation among candidate events. The authors construct two CGEP datasets based on existing corpora and experimentally validate their proposed SeDGPL model, showing it outperforms advanced competitors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about predicting what might happen next in a story or historical event. Right now, computers can only look at the events that have happened so far to make predictions. But real life is more complicated and there’s not always enough information. This paper introduces a new way of thinking called Causality Graph Event Prediction (CGEP) that looks at the relationships between events. The authors designed a special model to do this, which they tested on two big datasets. They found that their model was better than other models at predicting what might happen next. |
Keywords
» Artificial intelligence » Prompt
