# Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes

arXiv: https://arxiv.org/abs/1705.05040 · HTML: https://mlsanity.com/paper/1705.05040/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2017.

## Built on by (most cited first, 8 of 8)

- [AMERICANO: Argument Generation with Discourse-driven Decomposition and Agent Interaction](https://mlsanity.com/paper/2310.20352/) (2023)
- [Argument Quality Assessment in the Age of Instruction-Following Large Language Models](https://mlsanity.com/paper/2403.16084/) (2024)
- [Dynamic Knowledge Integration for Evidence-Driven Counter-Argument Generation with Large Language Models](https://mlsanity.com/paper/2503.05328/) (2025)
- [Modeling the Quality of Dialogical Explanations](https://mlsanity.com/paper/2403.00662/) (2024)
- [From Argumentation to Deliberation: Perspectivized Stance Vectors for Fine-grained (Dis)agreement Analysis](https://mlsanity.com/paper/2502.09644/) (2025)
- [DEFINED: A Data-Efficient Computational Framework for Fine-Grained Creativity Assessment in Debate Scenarios](https://mlsanity.com/paper/2606.07226/) (2026)
- [Persuasion Index: A Theory-Guided Framework for Persuasion Analysis](https://mlsanity.com/paper/2606.14580/) (2026)
- [ARGUS: Theory-of-Mind Guided Argument Generation with Strategy-Aware Planning and Knowledge Grounding](https://mlsanity.com/paper/2608.20405/) (2026)

## Abstract (arXiv)

Debate and deliberation play essential roles in politics and government, but most models presume that debates are won mainly via superior style or agenda control. Ideally, however, debates would be won on the merits, as a function of which side has the stronger arguments. We propose a predictive model of debate that estimates the effects of linguistic features and the latent persuasive strengths of different topics, as well as the interactions between the two. Using a dataset of 118 Oxford-style debates, our model's combination of content (as latent topics) and style (as linguistic features) allows us to predict audience-adjudicated winners with 74% accuracy, significantly outperforming linguistic features alone (66%). Our model finds that winning sides employ stronger arguments, and allows us to identify the linguistic features associated with strong or weak arguments.