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

large-language-models › model-architecture

📄 Abstract

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.

Authors (5)

Nilmadhab Das

Vishal Vaibhav

Yash Sunil Choudhary

V. Vijaya Saradhi

Ashish Anand

Submitted

October 18, 2025

arXiv Category

cs.CL

arXiv PDF

Key Contributions

Proposes the Autoregressive Argumentative Structure Prediction (AASP) framework for end-to-end argument mining. Unlike previous methods that flatten structures, AASP jointly models Argument Components (ACs) and Argumentative Relations (ARs) using an autoregressive approach with a conditional pre-trained language model, treating structures as constrained actions.

Business Value

Enables automated analysis of persuasive texts, aiding in areas like legal document review, opinion mining, and fact-checking by identifying claims and supporting evidence.

Paper Metadata

Innovation Type

Algorithmic Framework

Deployment Feasibility

Medium; requires integration into NLP pipelines and access to pre-trained language models.

Limitations Addressed

Difficulty in modeling dependencies between Argument Components (ACs) and Argumentative Relations (ARs),Limitations of generative approaches that flatten argumentative structures,Need for end-to-end joint modeling of AM tasks

Technical Tags

argument miningautoregressive predictionargumentative structurenatural language processingconditional language modelsend-to-end learningpremiseclaimsupportattack

Research Topics

Argument MiningNLP for Text AnalysisGenerative Models for Structured PredictionComputational Linguistics

Methods & Architectures

Autoregressive Structure PredictionConditional Pre-trained Language ModelEnd-to-End Learning Autoregressive ModelsConditional Pre-trained Language Models

Applications & Tasks

Natural Language Processing Text Analysis Argumentation Mining Modeling dependencies between Argument Components (ACs) and Argumentative Relations (ARs)Extracting complex argumentative structuresOvercoming limitations of flattening argumentative structures Argument Component (AC) identificationArgumentative Relation (AR) predictionJoint extraction of argumentative structures

Related Fields

Natural Language ProcessingComputational LinguisticsArgumentation TheoryMachine LearningText Mining

Keywords

argument miningNLPautoregressivestructure predictionargument componentsargumentative relationslanguage modelsend-to-endtext analysispremiseclaim

Academic Context

#Argument Mining#NLP for Text Analysis#Generative Models for Structured Prediction#Computational Linguistics

Commercial Potential

Potential Products

Automated argument analysis toolsSoftware for identifying claims and evidence in textArgument summarization systems

Target Industries

LegalMediaAcademiaCustomer Service (feedback analysis)

Use Case Examples

Identifying the main claims and supporting premises in legal briefsAnalyzing user reviews to understand arguments for/against a productDetecting misinformation by analyzing argumentative structures

Competitive Edge

Offers a novel end-to-end autoregressive approach that jointly models argumentative structures, overcoming limitations of prior sequential or flattened methods.

Market Opportunity

Growing market for NLP tools and text analytics.

Revenue Models

SaaS for text analysis platformslicensing to enterprises.

Resource Requirements

Compute Needs

Requires computational resources for training and inference with large language models.

Data Requirements

Annotated argumentative texts.

Deployment Constraints

Integration into existing NLP pipelines, performance on diverse text types.

Scalability

Scalable with computational resources and larger datasets.

Production Readiness

Maturity Level

Research / Development

Time to Market

Medium term for integration into NLP applications.

Patent Potential

Medium, for the specific autoregressive structure prediction framework for argument mining.

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