Identification of Entailment and Contradiction Relations for Natural Language Argumentation: A Neurosymbolic Approach

Abstract:Argumentation is an important cognitive activity that involves the scrutiny of arguments and counterarguments. When given an argument, it is desirable that its premises entail its claim, and that the premises and claim are consistent, and when given a pair of arguments, it is desirable to check whether one argument supports or attacks another. If the arguments are in natural language, then we could use methods for natural language inference, also known as recognizing textual entailment (RTE). The state-of-the-art RTE methods use machine learning and deep learning, but these are not explainable. An alternative approach which we investigate in this paper is to translate text into an abstract meaning representation (AMR) graph. For this we use a pre-trained AMR parser. We then translate the AMR graph into propositional logic and use a SAT solver for automated reasoning. In text, often common sense suggests that an entailment (or contradiction) relationship holds between a premise and a claim, but because different wordings are used, this is not identified from their logical representations. To address this, we introduce relaxation methods to allow replacement or forgetting of some propositions. Our experimental results show our approach performs well on three RTE datasets. The benefit of our approach is that we have a logical representation of premises and claims, and of the relaxations that are used to identify the entailment and contradiction relations.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2405.01259 [cs.AI]

(or arXiv:2405.01259v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2405.01259 Focus to learn more arXiv-issued DOI via DataCite

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