CIARAM: Class Imbalance Aware Generative Framework for Relational Argument Mining

ACL ARR 2025 February Submission2832 Authors

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 achieves State-of-the-Art (SoTA) results, with Macro-F1 score gains ranging from 5.05% to 12.88%, demonstrating the strong potential of our approach.

Paper Type: Short

Research Area: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Research Area Keywords: Relational Argument Mining, Class-imbalance, Argument mining, LLM

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 2832