Modelling Suspense in Short Stories as Uncertainty Reduction over Neural Representation

David Wilmot, Frank Keller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Suspense is a crucial ingredient of narrative fiction, engaging readers and making stories compelling. While there is a vast theoretical literature on suspense, it is computationally not well understood. We compare two ways for modelling suspense: surprise, a backward-looking measure of how unexpected the current state is given the story so far; and uncertainty reduction, a forward-looking measure of how unexpected the continuation of the story is. Both can be computed either directly over story representations or over their probability distributions. We propose a hierarchical language model that encodes stories and computes surprise and uncertainty reduction. Evaluating against short stories annotated with human suspense judgements, we find that uncertainty reduction over representations is the best predictor, resulting in near human accuracy. We also show that uncertainty reduction can be used to predict suspenseful events in movie synopses.
Original languageEnglish
Title of host publicationProceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Place of PublicationOnline
PublisherAssociation for Computational Linguistics
Pages1763-1788
Number of pages26
ISBN (Electronic)978-1-952148-25-5
Publication statusPublished - 10 Jul 2020
Event2020 Annual Conference of the Association for Computational Linguistics - Hyatt Regency Seattle, Virtual conference, United States
Duration: 5 Jul 202010 Jul 2020
Conference number: 58
https://acl2020.org/

Conference

Conference2020 Annual Conference of the Association for Computational Linguistics
Abbreviated titleACL 2020
CountryUnited States
CityVirtual conference
Period5/07/2010/07/20
Internet address

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