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An Argumentative Dialogue System for COVID-19 Vaccine Information

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Logic and Argumentation (CLAR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13040))

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Abstract

Dialogue systems are widely used in AI to support timely and interactive communication with users. We propose a general-purpose dialogue system architecture that leverages computational argumentation to perform reasoning and provide consistent and explainable answers. We illustrate the system using a COVID-19 vaccine information case study.

B. Fazzinga, A. Galassi, and P. Torroni—Equal contribution.

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Notes

  1. 1.

    https://www.gartner.com/smarterwithgartner/chatbots-will-appeal-to-modern-workers/.

  2. 2.

    https://www.mordorintelligence.com/industry-reports/chatbot-market.

  3. 3.

    https://www.canada.ca/en/employment-social-development/services/my-account/terms-use-chatbot.html.

  4. 4.

    https://government.economictimes.indiatimes.com/news/digital-india/covid-19-govt-launches-facebook-and-messenger-chatbot/74843125.

  5. 5.

    https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai.

  6. 6.

    We point out that our concept of support is a new notion linking status nodes to reply nodes, and its semantics is different from the standard one [3, 9].

  7. 7.

    The implicit assumption here is that the user does not enter conflicting information, and that the language model correctly interprets the user input. Clearly, if this is not the case, the system’s output becomes unreliable. But that wouldn’t depend on the underlying reasoning framework. The definition of fall-back strategies able to handle such exceptions would be an important extension to the system.

  8. 8.

    Italian medicines agency, https://www.aifa.gov.it/en/vaccini-covid-19.

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Acknowledgments

The research reported in this work was partially supported by the EU H2020 ICT48 project “Humane AI Net" under contract #952026.

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Authors and Affiliations

  1. ICAR-CNR, Rende, Italy

    Bettina Fazzinga

  2. DICES, University of Calabria, Rende, Italy

    Bettina Fazzinga

  3. DISI, University of Bologna, Bologna, Italy

    Andrea Galassi & Paolo Torroni

Authors
  1. Bettina Fazzinga
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  2. Andrea Galassi
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  3. Paolo Torroni
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Corresponding author

Correspondence to Andrea Galassi .

Editor information

Editors and Affiliations

  1. Department of Information Engineering, University of Brescia, Brescia, Italy

    Pietro Baroni

  2. Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany

    Christoph Benzmüller

  3. Department of Philosophy (Zhuhai), Sun Yat-sen University, Zhuhai, China

    Yὶ N. Wáng

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Fazzinga, B., Galassi, A., Torroni, P. (2021). An Argumentative Dialogue System for COVID-19 Vaccine Information. In: Baroni, P., Benzmüller, C., Wáng, Y.N. (eds) Logic and Argumentation. CLAR 2021. Lecture Notes in Computer Science(), vol 13040. Springer, Cham. https://doi.org/10.1007/978-3-030-89391-0_27

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  • DOI: https://doi.org/10.1007/978-3-030-89391-0_27

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  • Print ISBN: 978-3-030-89390-3

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