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Dialogical Argumentation and Textual Entailment

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Natural Language Processing in Artificial Intelligence—NLPinAI 2020

Part of the book series: Studies in Computational Intelligence ((SCI,volume 939))

Abstract

In this chapter, we introduce a new dialogical system for first order classical logic which is close to natural language argumentation, and we prove its completeness with respect to usual classical validity. We combine our dialogical system with the Grail syntactic and semantic parser developed by the second author in order to address automated textual entailment, that is, we use it for deciding whether or not a sentence is a consequence of a short text. This work—which connects natural language semantics and argumentation with dialogical logic—can be viewed as a step towards an inferentialist view of natural language semantics.

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Notes

  1. 1.

    Our system is able to deal with metaphoric use, like the The cash machine ate my credit card. see e.g., [37].

  2. 2.

    The patterns in the well-known corpus for testing Textual Entailment recognition called FraCaS [11] greatly vary in difficulty. We expect only some of them (monotonicity, syllogisms) to be handled easily, whereas we expect others (plurals, temporal inference for aspectual classes) to be much more difficult for systems based on automated theorem provers, or, indeed, for any automated system.

  3. 3.

    The words “question” and “answer” are called “attack” and “defence” by Felscher in [16]; we deviate from this terminology because we will rather use the terms “attack” and “defence” exclusively for the moves in a game, avoiding possible confusion.

  4. 4.

    We ignore the product connectives ‘\(\bullet \)’ here, since it has somewhat more complicated natural deduction rules and it is not used in the examples.

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Authors and Affiliations

  1. LIRMM, Université de Montpellier, CNRS, 860 rue Saint Priest, 34095, Montpellier, France

    Davide Catta, Richard Moot & Christian Retoré

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  1. Davide Catta
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  2. Richard Moot
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Correspondence to Christian Retoré .

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  1. Department of Algebra and Logic, Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Roussanka Loukanova

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Catta, D., Moot, R., Retoré, C. (2021). Dialogical Argumentation and Textual Entailment. In: Loukanova, R. (eds) Natural Language Processing in Artificial Intelligence—NLPinAI 2020. Studies in Computational Intelligence, vol 939. Springer, Cham. https://doi.org/10.1007/978-3-030-63787-3_7

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