mlsanityData to 7 Oct 2026

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

arXiv 1907.08971 ↗First posted 21 Jul 2019cs.LG cs.CL stat.MLcited by 29

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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.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jul 2019.

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Citing papers per year: 2021: 3; 2022: 4; 2023: 1; 2024: 10; 2025: 6; 2026: 5.

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  1. 2024What Evidence Do Language Models Find Convincing?cited by 74
  2. 2021ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoningcited by 58
  3. 2024Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statementscited by 33
  4. 2024Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Languagecited by 31
  5. 2024Debatrix: Multi-dimensional Debate Judge with Iterative Chronological Analysis Based on LLMcited by 21
  6. 2021Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentationcited by 17
  7. 2021Learning From Revisions: Quality Assessment of Claims in Argumentation at Scalecited by 14
  8. 2025Large Language Models in Argument Mining: A Surveycited by 14
  9. 2024Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluationcited by 13
  10. 2022Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networkscited by 10
  11. 2024OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Datasetcited by 8
  12. 2022Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussioncited by 7
17 more
  1. 2024AutoPersuade: A Framework for Evaluating and Explaining Persuasive Argumentscited by 7
  2. 2025Do Emotions Really Affect Argument Convincingness? A Dynamic Approach with LLM-based Manipulation Checkscited by 6
  3. 2022An Item Response Theory Framework for Persuasioncited by 5
  4. 2025Towards a Perspectivist Turn in Argument Quality Assessmentcited by 5
  5. 2024Argument Quality Assessment in the Age of Instruction-Following Large Language Modelscited by 4
  6. 2025ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentationcited by 4
  7. 2023Topic Ontologies for Argumentscited by 3
  8. 2026Detecting Winning Arguments with Large Language Models and Persuasion Strategiescited by 3
  9. 2022Towards a Holistic View on Argument Quality Predictioncited by 2
  10. 2025Investigating Subjective Factors of Argument Strength: Storytelling, Emotions, and Hedgingcited by 2
  11. 2025Trust Me, I Can Convince You: The Contextualized Argument Appraisal Frameworkcited by 2
  12. 2024TACO -- Twitter Arguments from COnversationscited by 1
  13. 2024Generating Attractive and Authentic Copywriting from Customer Reviewscited by 1
  14. 2026ArgBench: Benchmarking LLMs on Computational Argumentation Taskscited by 1
  15. 2026A dataset of rated conceptual argumentscited by 1
  16. 2026Can One-sided Arguments Lead to Response Change in Large Language Models?cited by 0
  17. 2026Persuasion Index: A Theory-Guided Framework for Persuasion Analysiscited by 0