Problem
Research questions and friction points this paper is trying to address.
enthymemes
implicit premises
logical reconstruction
argument graphs
Identifying Implicit Premises for Logical Reconstruction of Argument Graphs
๐ค AI Summary
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๐ Abstract
The logical reconstruction of argument graphs from natural language text is challenging because of the prevalence of enthymemes (i.e., arguments with implicit premises). There are natural language processing methods for identifying enthymemes in text, and there are symbolic methods based on abduction for identifying missing premises in a logical representation of enthymemes. However, there is a need for methods to generate implicit premises to logically show a known entailment or contradiction relationship between a pair of statements. To address this, we propose a neuro-symbolic pipeline that uses large language models (LLMs) to generate intermediate implicit premises that are translated into logical formulae and used with logical formulae representing explicit premises and explicit claims to show the logical relationships between them (entailment, contradiction, or neutrality). Our approach is evaluated on the Microtext Argumentative Corpus.
Innovation
Methods, ideas, or system contributions that make the work stand out.
neuro-symbolic pipeline
large language models (LLMs)
implicit premises
logical reconstruction
argument graphs
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