Assessing the Minimal Dialectical Quality in Argumentation: A Neuro-Symbolic Approach Integrating Argument Mining, Quality Assessment, and Probabilistic Reasoning

Abstract

This work introduces a new dimension of argumentative quality, termed Minimal Dialectical Quality (MDQ), which requires an argumentation to coherently support its main claims through adequate justifications and, when necessary, explicit rebuttals. MDQ provides a less subjective notion of argumentation quality than many existing approaches, as it relies exclusively on information contained in the text itself and does not require external knowledge or fact-checking. To investigate the feasibility of MDQ as an approximation of human judgments of argumentative quality, we propose the Minimal Dialectical Quality Evaluator (MiDiQE), a neuro-symbolic system for assessing the quality of argumentative essays. MiDiQE operationalizes MDQ by computing the probability that a given text satisfies the new dimension and comparing it against a predefined threshold. The system integrates neural Argumentation Mining, sub-symbolic Single Argument Quality assessment, and symbolic Argumentation Reasoning modules to extract argument structures, evaluate their components, and perform formal probabilistic reasoning over them. Experimental results on argumentative essay datasets show that MDQ aligns well with human quality assessments, supporting its validity as a meaningful quality dimension. They also demonstrate that MiDiQE produces interpretable evaluations of argumentative quality while highlighting both the strengths and limitations of the proposed approach. Potential applications and directions for future work are discussed.