Learnable Multi-Attribute Gradual Semantics for Predicting Persuasion in Argumentative Debates

Gradual semantics for weighted bipolar argumentation provide a principled framework for modelling argumentative reasoning, yet existing approaches remain mostly scalar, fixed, and weakly grounded in empirical data. We introduce learnable multi-attribute gradual semantics for persuasion prediction in argumentative debates. Our approach builds a dataset of 600 textual debates converted into multi-attribute argumentation graphs enriched with multi-dimensional features on nodes and relations. Building on this representation, we propose learnable aggregation operators that distinguish intrinsic quality from persuasive strategy dimensions. Experiments show that the learned semantics achieve competitive performance with neural and LLM-based baselines while preserving interpretability.