# Constructing Narrative Event Evolutionary Graph for Script Event Prediction

arXiv: https://arxiv.org/abs/1805.05081 · HTML: https://mlsanity.com/paper/1805.05081/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2018.

## Built on by (most cited first, 30 of 33)

- [ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification](https://mlsanity.com/paper/2203.02225/) (2022)
- [The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction](https://mlsanity.com/paper/2104.06344/) (2021)
- [EventBERT: A Pre-Trained Model for Event Correlation Reasoning](https://mlsanity.com/paper/2110.06533/) (2021)
- [Guided Generation of Cause and Effect](https://mlsanity.com/paper/2107.09846/) (2021)
- [ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities](https://mlsanity.com/paper/2104.02137/) (2021)
- [What is Event Knowledge Graph: A Survey](https://mlsanity.com/paper/2112.15280/) (2021)
- [Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification](https://mlsanity.com/paper/2307.01972/) (2023)
- [EventPlus: A Temporal Event Understanding Pipeline](https://mlsanity.com/paper/2101.04922/) (2021)
- [Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization](https://mlsanity.com/paper/2206.01532/) (2022)
- [Scene-Driven Multimodal Knowledge Graph Construction for Embodied AI](https://mlsanity.com/paper/2311.03783/) (2023)
- [GraphFM: Graph Factorization Machines for Feature Interaction Modeling](https://mlsanity.com/paper/2105.11866/) (2021)
- [Towards Fine-grained Causal Reasoning and QA](https://mlsanity.com/paper/2204.07408/) (2022)
- [Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning](https://mlsanity.com/paper/2305.14970/) (2023)
- [CausalBERT: Injecting Causal Knowledge Into Pre-trained Models with Minimal Supervision](https://mlsanity.com/paper/2107.09852/) (2021)
- [Integrating Deep Event-Level and Script-Level Information for Script Event Prediction](https://mlsanity.com/paper/2110.15706/) (2021)
- [Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering](https://mlsanity.com/paper/2203.07633/) (2022)
- [SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller](https://mlsanity.com/paper/2210.06694/) (2022)
- [A Generative Approach for Script Event Prediction via Contrastive Fine-tuning](https://mlsanity.com/paper/2212.03496/) (2022)
- [EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs](https://mlsanity.com/paper/2404.00209/) (2024)
- [MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation](https://mlsanity.com/paper/2311.09105/) (2023)
- [A Graph Enhanced BERT Model for Event Prediction](https://mlsanity.com/paper/2205.10822/) (2022)
- [Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report](https://mlsanity.com/paper/2109.00287/) (2021)
- [Probing Script Knowledge from Pre-Trained Models](https://mlsanity.com/paper/2204.10176/) (2022)
- [Video-CoE: Reinforcing Video Event Prediction via Chain of Events](https://mlsanity.com/paper/2603.14935/) (2026)
- [Rich Event Modeling for Script Event Prediction](https://mlsanity.com/paper/2212.08287/) (2022)
- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [Modeling Human Mental States with an Entity-based Narrative Graph](https://mlsanity.com/paper/2104.07079/) (2021)
- [Context-aware short-term interest first model for session-based recommendation](https://mlsanity.com/paper/2103.15514/) (2021)
- [Symbolic Register Automata for Complex Event Recognition and Forecasting](https://mlsanity.com/paper/2110.04032/) (2021)
- [Prompt2Gaussia: Uncertain Prompt-learning for Script Event Prediction](https://mlsanity.com/paper/2308.02103/) (2023)

## Abstract (arXiv)

Script event prediction requires a model to predict the subsequent event given an existing event context. Previous models based on event pairs or event chains cannot make full use of dense event connections, which may limit their capability of event prediction. To remedy this, we propose constructing an event graph to better utilize the event network information for script event prediction. In particular, we first extract narrative event chains from large quantities of news corpus, and then construct a narrative event evolutionary graph (NEEG) based on the extracted chains. NEEG can be seen as a knowledge base that describes event evolutionary principles and patterns. To solve the inference problem on NEEG, we present a scaled graph neural network (SGNN) to model event interactions and learn better event representations. Instead of computing the representations on the whole graph, SGNN processes only the concerned nodes each time, which makes our model feasible to large-scale graphs. By comparing the similarity between input context event representations and candidate event representations, we can choose the most reasonable subsequent event. Experimental results on widely used New York Times corpus demonstrate that our model significantly outperforms state-of-the-art baseline methods, by using standard multiple choice narrative cloze evaluation.