The Candide model: How narratives emerge where observations meet beliefs

Paul Van Eecke; Lara Verheyen; Tom Willaert; Katrien Beuls

doi:10.18653/v1/2023.wnu-1.7

The Candide model: How narratives emerge where observations meet beliefs

Paul Van Eecke, Lara Verheyen, Tom Willaert, Katrien Beuls

Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution

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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 language	English
Title of host publication	5th Workshop on Narrative Understanding, WNU 2023 - Proceedings of the Workshop
Editors	Nader Akoury, Elizabeth Clark, Mohit Iyyer, Snigdha Chaturvedi, Faeze Brahman, Khyathi Raghavi Chandu
Publisher	Association for Computational Linguistics
Pages	48-57
Number of pages	10
ISBN (Electronic)	9781959429920
DOIs	https://doi.org/10.18653/v1/2023.wnu-1.7
Publication status	Published - 2023
Event	The 61st Annual Meeting of the Association for Computational Linguistics - Toronto, Canada Duration: 9 Jul 2023 → 14 Jul 2023 Conference number: 61 https://2023.aclweb.org

Publication series

Name	Proceedings of the The 5th Workshop on Narrative Understanding

Conference

Conference	The 61st Annual Meeting of the Association for Computational Linguistics
Abbreviated title	ACL 2023
Country/Territory	Canada
City	Toronto
Period	9/07/23 → 14/07/23
Internet address	https://2023.aclweb.org

Funding

We are especially grateful to Remi van Trijp for his important contributions to the insightful discussions that led up to the development of the Candide model, as well as for his constructive feedback on a first version of this manuscript. 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’. 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’.

Funders	Funder number
Flanders AI Research Program
Vlaamse regering
European Commission
Social Media for Democracy
Horizon 2020 Framework Programme	951846, 101094752
Fonds Wetenschappelijk Onderzoek	75929

Keywords

language understanding
reasoning
personal dynamic memory

Access to Document

10.18653/v1/2023.wnu-1.7

2023.wnu-1.7Final published version, 251 KBLicence: CC BY

https://aclanthology.org/2023.wnu-1.7/Licence: CC BY

Cite this

Van Eecke, P., Verheyen, L., Willaert, T., & Beuls, K. (2023). The Candide model: How narratives emerge where observations meet beliefs. In N. Akoury, E. Clark, M. Iyyer, S. Chaturvedi, F. Brahman, & K. R. Chandu (Eds.), 5th Workshop on Narrative Understanding, WNU 2023 - Proceedings of the Workshop (pp. 48-57). (Proceedings of the The 5th Workshop on Narrative Understanding). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.wnu-1.7

Van Eecke, Paul ; Verheyen, Lara ; Willaert, Tom et al. / The Candide model: How narratives emerge where observations meet beliefs. 5th Workshop on Narrative Understanding, WNU 2023 - Proceedings of the Workshop. editor / Nader Akoury ; Elizabeth Clark ; Mohit Iyyer ; Snigdha Chaturvedi ; Faeze Brahman ; Khyathi Raghavi Chandu. Association for Computational Linguistics, 2023. pp. 48-57 (Proceedings of the The 5th Workshop on Narrative Understanding).

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title = "The Candide model: How narratives emerge where observations meet beliefs",

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.",

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author = "\{Van Eecke\}, Paul and Lara Verheyen and Tom Willaert and Katrien Beuls",

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doi = "10.18653/v1/2023.wnu-1.7",

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Van Eecke, P, Verheyen, L, Willaert, T & Beuls, K 2023, The Candide model: How narratives emerge where observations meet beliefs. in N Akoury, E Clark, M Iyyer, S Chaturvedi, F Brahman & KR Chandu (eds), 5th Workshop on Narrative Understanding, WNU 2023 - Proceedings of the Workshop. Proceedings of the The 5th Workshop on Narrative Understanding, Association for Computational Linguistics, pp. 48-57, The 61st Annual Meeting of the Association for Computational Linguistics, Toronto, Canada, 9/07/23. https://doi.org/10.18653/v1/2023.wnu-1.7

The Candide model: How narratives emerge where observations meet beliefs. / Van Eecke, Paul; Verheyen, Lara; Willaert, Tom et al.
5th Workshop on Narrative Understanding, WNU 2023 - Proceedings of the Workshop. ed. / Nader Akoury; Elizabeth Clark; Mohit Iyyer; Snigdha Chaturvedi; Faeze Brahman; Khyathi Raghavi Chandu. Association for Computational Linguistics, 2023. p. 48-57 (Proceedings of the The 5th Workshop on Narrative Understanding).

Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - The Candide model: How narratives emerge where observations meet beliefs

AU - Van Eecke, Paul

AU - Verheyen, Lara

AU - Willaert, Tom

AU - Beuls, Katrien

N1 - Conference code: 61

PY - 2023

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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BT - 5th Workshop on Narrative Understanding, WNU 2023 - Proceedings of the Workshop

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A2 - Clark, Elizabeth

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A2 - Chaturvedi, Snigdha

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ER -

Van Eecke P, Verheyen L, Willaert T, Beuls K. The Candide model: How narratives emerge where observations meet beliefs. In Akoury N, Clark E, Iyyer M, Chaturvedi S, Brahman F, Chandu KR, editors, 5th Workshop on Narrative Understanding, WNU 2023 - Proceedings of the Workshop. Association for Computational Linguistics. 2023. p. 48-57. (Proceedings of the The 5th Workshop on Narrative Understanding). doi: 10.18653/v1/2023.wnu-1.7

The Candide model: How narratives emerge where observations meet beliefs

Abstract

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Meaning and understanding in human-centric AI

Cite this

The Candide model: How narratives emerge where observations meet beliefs

Abstract

Publication series

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Meaning and understanding in human-centric AI

Cite this