Compact Models for Structured Argument and Stance Analysis: A Framing-Aware, Retrieval-Augmented Pipeline

Abstract

We propose a modular, retrieval-augmented pipeline for computational argumentation that integrates two complementary components: ArgStance, a multi-task model for argument and stance reasoning, and TargetMatch, a contrastive retrieval model that treats target identification as a first-class retrieval task. We further formulate stance detection as a framing-aware problem, recognizing that the polarity of a stance toward a target depends on how the proposition is framed. To support broad generalization, we construct a large dataset spanning Kialo discussions, Wikipedia, and curated news articles, and introduce a cross-source injection strategy that mitigates domain and style biases. Our compact models achieve F1 scores of 0.94 for argument detection (ModernBERT-base) and 0.84 for same-side stance detection (ModernBERT-large), while TargetMatch attains a top-10 retrieval accuracy of 0.75. Under controlled zero-shot comparisons with large language models, our models remain competitive while offering advantages in reproducibility, deployment cost, and controllable intermediate predictions.