SAD: A Large-Scale Strategic Argumentative Dialogue Dataset

2026-01-12

AI Summary

This work addresses the limitation of existing argumentative corpora, which are predominantly confined to single-turn, non-interactive settings and thus insufficient for modeling strategic multi-turn argumentation. To bridge this gap, we introduce SAD, the first large-scale dataset for strategic argumentative dialogue, comprising 392,822 multi-turn exchanges. Each utterance is annotated with up to five argumentation strategies grounded in theoretical frameworks, and models are tasked with generating goal-oriented arguments conditioned on both dialogue history and speaker stance. By incorporating a multi-strategy joint annotation scheme, SAD emphasizes the co-modeling of strategy selection with contextual and positional dynamics. We fine-tune and evaluate several pretrained generative models on this dataset, uncovering complex patterns in real-world strategic argumentation and advancing argument generation toward greater interactivity and strategic sophistication.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Knowledge Representation and Reasoning: Argumentation
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Argumentation generation has attracted substantial research interest due to its central role in human reasoning and decision-making. However, most existing argumentative corpora focus on non-interactive, single-turn settings, either generating arguments from a given topic or refuting an existing argument. In practice, however, argumentation is often realized as multi-turn dialogue, where speakers defend their stances and employ diverse argumentative strategies to strengthen persuasiveness. To support deeper modeling of argumentation dialogue, we present the first large-scale \textbf{S}trategic \textbf{A}rgumentative \textbf{D}ialogue dataset, SAD, consisting of 392,822 examples. Grounded in argumentation theories, we annotate each utterance with five strategy types, allowing multiple strategies per utterance. Unlike prior datasets, SAD requires models to generate contextually appropriate arguments conditioned on the dialogue history, a specified stance on the topic, and targeted argumentation strategies. We further benchmark a range of pretrained generative models on SAD and present in-depth analysis of strategy usage patterns in argumentation.

Problem

• argumentation generation
• multi-turn dialogue
• argumentative strategies
• dialogue dataset
• strategic argumentation

Innovation

• strategic argumentation
• argumentative dialogue
• multi-turn dialogue
• argumentation strategies
• large-scale dataset