Exploration of Marker-Based Approaches in Argument Mining through Augmented Natural Language
This work addresses the problem of automating argument analysis for researchers and applications in natural language processing, presenting an incremental improvement over existing methods.
The paper tackles argument mining by proposing argTANL, a generative end-to-end framework that jointly extracts argumentative components and relations using augmented natural language, achieving superior performance on three benchmarks with ME-argTANL.
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.