Exploration of Marker-Based Approaches in Argument Mining through Augmented Natural Language

TL;DR

This paper introduces argTANL, a generative end-to-end framework for Argument Mining that uses augmented natural language to jointly extract argumentative components and relations, enhanced by marker-based techniques.

Contribution

The study presents a novel generative paradigm for Argument Mining that incorporates marker-based augmentation and fine-tuning to improve extraction accuracy.

Findings

ME-argTANL outperforms existing models on standard benchmarks.

Marker-based techniques significantly enhance model performance.

Joint extraction of ACs and ARs is effectively achieved with the proposed framework.

Abstract

Argument Mining (AM) involves identifying and extracting Argumentative Components (ACs) and their corresponding Argumentative Relations (ARs). Most of the prior works have broken down these tasks into multiple sub-tasks. Existing end-to-end setups primarily use the dependency parsing approach. This work introduces a generative paradigm-based end-to-end framework argTANL. argTANL frames the argumentative structures into label-augmented text, called Augmented Natural Language (ANL). This framework jointly extracts both ACs and ARs from a given argumentative text. Additionally, this study explores the impact of Argumentative and Discourse markers on enhancing the model's performance within the proposed framework. Two distinct frameworks, Marker-Enhanced argTANL (ME-argTANL) and argTANL with specialized Marker-Based Fine-Tuning, are proposed to achieve this. Extensive experiments are conducted on three standard AM benchmarks to demonstrate the superior performance of the ME-argTANL.