# Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network

arXiv: https://arxiv.org/abs/1907.08971 · HTML: https://mlsanity.com/paper/1907.08971/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2019.

## Built on by (most cited first, 29 of 29)

- [What Evidence Do Language Models Find Convincing?](https://mlsanity.com/paper/2402.11782/) (2024)
- [ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning](https://mlsanity.com/paper/2104.07644/) (2021)
- [Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements](https://mlsanity.com/paper/2402.10614/) (2024)
- [Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Language](https://mlsanity.com/paper/2406.17753/) (2024)
- [Debatrix: Multi-dimensional Debate Judge with Iterative Chronological Analysis Based on LLM](https://mlsanity.com/paper/2403.08010/) (2024)
- [Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation](https://mlsanity.com/paper/2107.00281/) (2021)
- [Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale](https://mlsanity.com/paper/2101.10250/) (2021)
- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation](https://mlsanity.com/paper/2406.03151/) (2024)
- [Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks](https://mlsanity.com/paper/2203.14647/) (2022)
- [OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset](https://mlsanity.com/paper/2406.14657/) (2024)
- [Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion](https://mlsanity.com/paper/2204.07741/) (2022)
- [AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments](https://mlsanity.com/paper/2410.08917/) (2024)
- [Do Emotions Really Affect Argument Convincingness? A Dynamic Approach with LLM-based Manipulation Checks](https://mlsanity.com/paper/2503.00024/) (2025)
- [An Item Response Theory Framework for Persuasion](https://mlsanity.com/paper/2204.11337/) (2022)
- [Towards a Perspectivist Turn in Argument Quality Assessment](https://mlsanity.com/paper/2502.14501/) (2025)
- [Argument Quality Assessment in the Age of Instruction-Following Large Language Models](https://mlsanity.com/paper/2403.16084/) (2024)
- [ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation](https://mlsanity.com/paper/2505.22076/) (2025)
- [Topic Ontologies for Arguments](https://mlsanity.com/paper/2301.09759/) (2023)
- [Detecting Winning Arguments with Large Language Models and Persuasion Strategies](https://mlsanity.com/paper/2601.10660/) (2026)
- [Towards a Holistic View on Argument Quality Prediction](https://mlsanity.com/paper/2205.09803/) (2022)
- [Generating Attractive and Authentic Copywriting from Customer Reviews](https://mlsanity.com/paper/2404.13906/) (2024)
- [Investigating Subjective Factors of Argument Strength: Storytelling, Emotions, and Hedging](https://mlsanity.com/paper/2507.17409/) (2025)
- [Trust Me, I Can Convince You: The Contextualized Argument Appraisal Framework](https://mlsanity.com/paper/2509.17844/) (2025)
- [TACO -- Twitter Arguments from COnversations](https://mlsanity.com/paper/2404.00406/) (2024)
- [ArgBench: Benchmarking LLMs on Computational Argumentation Tasks](https://mlsanity.com/paper/2604.17366/) (2026)
- [A dataset of rated conceptual arguments](https://mlsanity.com/paper/2607.27499/) (2026)
- [Can One-sided Arguments Lead to Response Change in Large Language Models?](https://mlsanity.com/paper/2602.06260/) (2026)
- [Persuasion Index: A Theory-Guided Framework for Persuasion Analysis](https://mlsanity.com/paper/2606.14580/) (2026)

## Abstract (arXiv)

With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments. Machines capable of responding and interacting with humans in helpful ways have become ubiquitous. We now expect them to discuss with us the more delicate questions in our world, and they should do so armed with effective arguments. But what makes an argument more persuasive? What will convince you? In this paper, we present a new data set, IBM-EviConv, of pairs of evidence labeled for convincingness, designed to be more challenging than existing alternatives. We also propose a Siamese neural network architecture shown to outperform several baselines on both a prior convincingness data set and our own. Finally, we provide insights into our experimental results and the various kinds of argumentative value our method is capable of detecting.