The Candide model: How narratives emerge where observations meet beliefs

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Abstract

This paper presents the Candide model as a computational architecture for modelling human-like, narrative-based language understanding. The model starts from the idea that narratives emerge through the process of interpreting novel linguistic observations, such as utterances, paragraphs and texts, with respect to previously acquired knowledge and beliefs. Narratives are personal, as they are rooted in past experiences, and constitute perspectives on the world that might motivate different interpretations of the same observations. Concretely, the Candide model operationalises this idea by dynamically modelling the belief systems and background knowledge of individual agents, updating these as new linguistic observations come in, and exposing them to a logic reasoning engine that reveals the possible sources of divergent interpretations. Apart from introducing the foundational ideas, we also present a proof-of-concept implementation that demonstrates the approach through a number of illustrative examples.
Original languageEnglish
Title of host publicationProceedings of the The 5th Workshop on Narrative Understanding
EditorsNader Akoury, Elizabeth Clark, Mohit Iyyer, Snigdha Chaturvedi, Faeze Brahman, Khyathi Raghavi Chandu
PublisherAssociation for Computational Linguistics
Pages48-57
Number of pages10
ISBN (Electronic)9781959429920
Publication statusPublished - Jul 2023
Event5th Workshop on Narrative Understanding - Toronto, Canada
Duration: 14 Jul 202314 Jul 2023
https://sites.google.com/umass.edu/wnu2023

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Workshop

Workshop5th Workshop on Narrative Understanding
Abbreviated titleWNU
Country/TerritoryCanada
CityToronto
Period14/07/2314/07/23
Internet address

Bibliographical note

Funding Information:
The research reported on in this paper received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements no. 951846 (MUHAI - Meaning and Understanding in Human-centric AI) and no. 101094752 (SoMe4Dem - Social Media for Democracy – understanding the causal mechanisms of digital citizenship), from the Research Foundation Flanders (FWO) through a postdoctoral grant awarded to Paul Van Eecke (grant no. 75929) and from the Flemish Government under the ‘Flanders AI Research Program’.

Publisher Copyright:
© 2023 Association for Computational Linguistics.

Keywords

  • Fluid Construction Grammar
  • Construction Grammar
  • Computational Construction Grammar

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