CIARAM: Class Imbalance Aware Generative Framework for Relational Argument Mining

Abstract

Relational Argument Mining (RAM) is a key task of computational argumentation, which aims to classify the relationships such as Support or Attack between argument component (AC) pairs. Traditional approaches primarily rely on graph-based modelling with external knowledge sources, which are complex in nature. Also, these approaches struggle with RAM datasets when relation classes are imbalanced, as they are not designed for class-imbalanced scenarios. In this work, we propose CIARAM framework to reformulate RAM as a text-to-text generation problem to generate relational labels in a flattened text format. To address the class imbalance, we employ a data augmentation strategy using a decoder-only Large Language Model (LLM) to balance the underrepresented relation classes. Across five standard RAM benchmarks, CIARAM produces strong results, specifically with the billion-parameter model, with a substantial gain in performance compared to the latest baseline, demonstrating the strong potential of our approach.

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