End-to-End Argument Mining through Autoregressive Argumentative Structure Prediction

• URL: http://arxiv.org/abs/2510.16363v1
• Date: Sat, 18 Oct 2025 06:10:57 GMT
• Title: End-to-End Argument Mining through Autoregressive Argumentative Structure Prediction
• Abstract summary: This study jointly formulates the key tasks of Argument Mining (AM) in an end-to-end fashion using Autoregressive Argumentative Structure Prediction (AASP) framework.<n>AASP framework models the argumentative structures as constrained pre-defined sets of actions with the help of a conditional pre-trained language model.<n>Experiments conducted on three standard AM benchmarks demonstrate that AASP achieves state-of-theart (SoTA) results across all AM tasks in two benchmarks and delivers strong results in one benchmark.
• Score: 2.0421986354783432
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Argument Mining (AM) helps in automating the extraction of complex argumentative structures such as Argument Components (ACs) like Premise, Claim etc. and Argumentative Relations (ARs) like Support, Attack etc. in an argumentative text. Due to the inherent complexity of reasoning involved with this task, modelling dependencies between ACs and ARs is challenging. Most of the recent approaches formulate this task through a generative paradigm by flattening the argumentative structures. In contrast to that, this study jointly formulates the key tasks of AM in an end-to-end fashion using Autoregressive Argumentative Structure Prediction (AASP) framework. The proposed AASP framework is based on the autoregressive structure prediction framework that has given good performance for several NLP tasks. AASP framework models the argumentative structures as constrained pre-defined sets of actions with the help of a conditional pre-trained language model. These actions build the argumentative structures step-by-step in an autoregressive manner to capture the flow of argumentative reasoning in an efficient way. Extensive experiments conducted on three standard AM benchmarks demonstrate that AASP achieves state-of-theart (SoTA) results across all AM tasks in two benchmarks and delivers strong results in one benchmark.

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