Search Results for author:
Found 4 papers, 3 papers with code
Great Expectations: Unsupervised Inference of Suspense, Surprise and Salience in Storytelling
Narrative theory methods (rules and procedures) are applied to the knowledge built into deep learning models to directly infer salience, surprise, and salience in stories.
A Temporal Variational Model for Story Generation
Recent language models can generate interesting and grammatically correct text in story generation but often lack plot development and long-term coherence.
Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories
Measuring event salience is essential in the understanding of stories.
Modelling Suspense in Short Stories as Uncertainty Reduction over Neural Representation
Suspense is a crucial ingredient of narrative fiction, engaging readers and making stories compelling.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
