NGEP: A Graph-based Event Planning Framework for Story Generation
Chen Tang, Zhihao Zhang, Tyler Loakman, Chenghua Lin, Frank Guerin

TL;DR
NGEP is a novel graph-based framework for story event planning that improves narrative coherence and controllability in long text generation by inferring event sequences from an event graph and using a neural event advisor.
Contribution
The paper introduces NGEP, a new graph-based event planning framework that outperforms existing methods in story generation tasks by enhancing coherence and control.
Findings
Outperforms state-of-the-art event planning methods
Improves story coherence and controllability
Enhances downstream story generation quality
Abstract
To improve the performance of long text generation, recent studies have leveraged automatically planned event structures (i.e. storylines) to guide story generation. Such prior works mostly employ end-to-end neural generation models to predict event sequences for a story. However, such generation models struggle to guarantee the narrative coherence of separate events due to the hallucination problem, and additionally the generated event sequences are often hard to control due to the end-to-end nature of the models. To address these challenges, we propose NGEP, an novel event planning framework which generates an event sequence by performing inference on an automatically constructed event graph and enhances generalisation ability through a neural event advisor. We conduct a range of experiments on multiple criteria, and the results demonstrate that our graph-based neural framework…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Artificial Intelligence in Games
