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

arXiv: https://arxiv.org/abs/2409.17480 · HTML: https://mlsanity.com/paper/2409.17480/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 42 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Survey on Dialogue Systems: Recent Advances and New Frontiers](https://mlsanity.com/paper/1711.01731/) (2017)
- [Is ChatGPT a Good Causal Reasoner? A Comprehensive Evaluation](https://mlsanity.com/paper/2305.07375/) (2023)
- [MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction](https://mlsanity.com/paper/2211.07342/) (2022)
- [The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction](https://mlsanity.com/paper/2104.06344/) (2021)
- [Constructing Narrative Event Evolutionary Graph for Script Event Prediction](https://mlsanity.com/paper/1805.05081/) (2018)
- [Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting](https://mlsanity.com/paper/2307.01709/) (2023)
- [Integrating Deep Event-Level and Script-Level Information for Script Event Prediction](https://mlsanity.com/paper/2110.15706/) (2021)
- [RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph](https://mlsanity.com/paper/2202.06129/) (2022)
- [A Generative Approach for Script Event Prediction via Contrastive Fine-tuning](https://mlsanity.com/paper/2212.03496/) (2022)
- [A Graph Enhanced BERT Model for Event Prediction](https://mlsanity.com/paper/2205.10822/) (2022)
- [TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2305.10866/) (2023)
- [Inference of Shape Expression Schemas Typed RDF Graphs](https://mlsanity.com/paper/2107.04891/) (2021)
- [DyGCL: Dynamic Graph Contrastive Learning For Event Prediction](https://mlsanity.com/paper/2404.15612/) (2024)

## Abstract (arXiv)

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.