---
title: "SAD: A Large-Scale Strategic Argumentative Dialogue Dataset"
description: "On this resource they define a generation task: write the next argument given the topic, the prior dialogue, a target stance and… 44 references."
authors: ["YongKang Liu", "Jiayang Yu", "Mingyang Wang", "Yiqun Zhang", "Ercong Nie", "Shi Feng", "Daling Wang", "Kaisong Song", "Hinrich Schuetze"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1673/"
doi: "10.18653/v1/2026.acl-long.1673"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/liu-2026-sad-large-scale-strategic-argumentative/"
---
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# SAD: A Large-Scale Strategic Argumentative Dialogue Dataset

**Authors:** [YongKang Liu](https://md.research.elamur.ai/free/en/authors/yongkang-liu/), Jiayang Yu, [Mingyang Wang](https://md.research.elamur.ai/free/en/authors/mingyang-wang/), [Yiqun Zhang](https://md.research.elamur.ai/free/en/authors/yiqun-zhang/), [Ercong Nie](https://md.research.elamur.ai/free/en/authors/ercong-nie/), [Shi Feng](https://md.research.elamur.ai/free/en/authors/shi-feng/), [Daling Wang](https://md.research.elamur.ai/free/en/authors/daling-wang/), [Kaisong Song](https://md.research.elamur.ai/free/en/authors/kaisong-song/) and [Hinrich Schuetze](https://md.research.elamur.ai/free/en/authors/hinrich-schuetze/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1673/) · [PDF](https://aclanthology.org/2026.acl-long.1673.pdf) · [DOI 10.18653/v1/2026.acl-long.1673](https://doi.org/10.18653/v1/2026.acl-long.1673) · [OpenAlex](https://openalex.org/W7166869819)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Most argument-generation corpora treat argumentation as a one-shot task: produce an argument for a topic or rebut a single claim. Real debates, however, unfold over several turns, and participants deliberately choose rhetorical tactics. To address this, the authors built SAD from threads crawled from the ChangeMyView forum, reconstructing dialogues by following reply chains. Each utterance is labeled with its stance (for or against the topic) and with one or more of five strategy types derived from argumentation theory. On this resource they define a generation task: write the next argument given the topic, the prior dialogue, a target stance and the desired strategies. They also train an automatic persuasiveness judge, supervised by forum like counts, which agrees well with human ratings. Benchmarks on several LLMs show that supplying strategy cues generally improves fluency, coherence and relevance, while persuasiveness gains are mixed. Fine-tuning with DPO beats SFT. SAD provides a sizable, realistic testbed for controllable, strategy-aware debating systems.

## Key points
- **Scale:** SAD contains 392,822 dialogue examples built from 722,812 utterances across 20,619 ChangeMyView topics.
- **Strategy labels:** Linguists defined five multi-label strategy types: Question, Causality, Example, Analogy and Statement.
- **Task formulation:** The task is P(Argument | History, Stance, Topic, Strategy). The data also supports strategy identification, argument retrieval and general multi-turn argument generation.
- **Effect of strategy prompting:** Persuasiveness rose clearly for Claude-3-haiku (2.90 → 3.31) and barely for Llama3.1-8B (2.79 → 2.83). It dipped for Qwen3-8B (3.85 → 3.72). Closed-source models seem better at exploiting strategies.
- **Fine-tuning results:** DPO outperformed SFT for both Llama3.1-8B and Qwen3-8B. The best setup was Qwen3-8B with DPO and strategies, reaching Relevance 4.24 and a persuasion score of 0.6909.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: YongKang Liu, Jiayang Yu, Mingyang Wang, Yiqun Zhang, Ercong Nie, Shi Feng, Daling Wang, Kaisong Song and Hinrich Schuetze. 2026. SAD: A Large-Scale Strategic Argumentative Dialogue Dataset. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{liu2026sad,
  title = {{SAD: A Large-Scale Strategic Argumentative Dialogue Dataset}},
  author = {YongKang Liu, Jiayang Yu, Mingyang Wang, Yiqun Zhang, Ercong Nie, Shi Feng, Daling Wang, Kaisong Song and Hinrich Schuetze},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1673},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1673}
}
```

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*Updated 1 November 2026*