A Generative Marker Enhanced End-to-End Framework for Argument Mining

• URL: http://arxiv.org/abs/2406.08606v2
• Date: Sun, 8 Sep 2024 12:24:11 GMT
• Title: A Generative Marker Enhanced End-to-End Framework for Argument Mining
• Authors: Nilmadhab Das, Vishal Choudhary, V. Vijaya Saradhi, Ashish Anand,
• Abstract summary: Argument Mining (AM) involves identifying and extracting Argumentative Components (ACs) and their corresponding Argumentative Relations (ARs) This work introduces a generative paradigm-based end-to-end framework argTANL. It frames the argumentative structures into label-augmented text, called Augmented Natural Language (ANL)
• Score: 0.8213829427624407
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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