Argument Structure Prediction in Online Conversations: A Comparative Study of Modeling Paradigms and Task Architectures

2026-09-30

AI Summary

This study addresses the unclear trade-offs among modeling paradigms and task architectures for online conversational argument structure prediction. We present the first multi-paradigm comparative analysis under strict constraints, developing a dedicated data processing pipeline that transforms Informational Argumentation Trees (IAT) into bipolar argument structures. Furthermore, this work systematically evaluates the end-to-end performance and efficiency of supervised fine-tuning and large language models within both single-step and multi-step architectures. Our findings reveal that relation identification constitutes the primary bottleneck and delineate the respective advantages and limitations of each paradigm. To facilitate future research, we release the complete framework as open source.

Abstract

Argument structure prediction (ASP) constructs complete argument structures from discourse by identifying argumentative units and their relations. While recent work has explored diverse approaches---including unified neural models, multi-step pipelines, and prompt-based large language models (LLMs)---their relative trade-offs remain under-explored, particularly in dialogical settings. We present a systematic evaluation of ASP under strict schema constraints, comparing supervised fine-tuning and prompt-based LLMs across single- and multi-step task architectures, generating complete argument structures from dialogical input end-to-end. We benchmark them on three diverse dialogical corpora adapted from Inference Anchoring Theory into bipolar argument structures. Under a shared evaluation framework, we assess predictive performance, cross-domain generalization, schema compliance, and computational efficiency. Our results show that ASP remains a challenging task, with identifying argumentative relations emerging as the primary bottleneck, largely due to the implicit and context-dependent nature of dialogical argumentation. To facilitate future research, we release our data processing pipeline and end-to-end modeling framework for computational ASP on dialogical corpora.

Problem

• Argument Structure Prediction
• Online Conversations
• Dialogical Argumentation
• Large Language Models
• Argumentative Relations

Innovation

• Argument Structure Prediction
• Large Language Models
• Dialogical Corpora
• Task Architectures
• Schema Compliance