Opening up Minds with Argumentative Dialogues
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Abstract (arXiv)
Recent research on argumentative dialogues has focused on persuading people to take some action, changing their stance on the topic of discussion, or winning debates. In this work, we focus on argumentative dialogues that aim to open up (rather than change) people's minds to help them become more understanding to views that are unfamiliar or in opposition to their own convictions. To this end, we present a dataset of 183 argumentative dialogues about 3 controversial topics: veganism, Brexit and COVID-19 vaccination. The dialogues were collected using the Wizard of Oz approach, where wizards leverage a knowledge-base of arguments to converse with participants. Open-mindedness is measured before and after engaging in the dialogue using a questionnaire from the psychology literature, and success of the dialogue is measured as the change in the participant's stance towards those who hold opinions different to theirs. We evaluate two dialogue models: a Wikipedia-based and an argument-based model. We show that while both models perform closely in terms of opening up minds, the argument-based model is significantly better on other dialogue properties such as engagement and clarity.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 20 of its 34 references that are in the map. First posted on arXiv 16 Jan 2023.
Builds on
20 of 34 references.
References · 20
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
- 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
- 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
- 2018Wizard of Wikipedia: Knowledge-Powered Conversational agentscited by 530
- 2019Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Goodcited by 207
- 2019Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoringcited by 164
- 2020Knowledge-Grounded Dialogue Generation with Pre-trained Language Modelscited by 107
- 2016Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussionscited by 106
- 2013A Computational Approach to Politeness with Application to Social Factorscited by 104
- 2018A Dataset for Document Grounded Conversationscited by 104
8 more
- 2019Learning to Select Knowledge for Response Generation in Dialog Systemscited by 80
- 2020ConvoKit: A Toolkit for the Analysis of Conversationscited by 71
- 2018Towards Exploiting Background Knowledge for Building Conversation Systemscited by 57
- 2020Low-Resource Knowledge-Grounded Dialogue Generationcited by 53
- 2019Incremental Transformer with Deliberation Decoder for Document Grounded Conversationscited by 35
- 2020Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategiescited by 29
- 2016Conversational flow in Oxford-style debatescited by 25
- 2021I Beg to Differ: A study of constructive disagreement in online conversationscited by 18
Built on by
Citing papers per year: 2023: 1; 2024: 2; 2025: 3; 2026: 2.
8 papers.
- 2023The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Whocited by 19
- 2024Evaluating Moral Beliefs across LLMs through a Pluralistic Frameworkcited by 11
- 2024ORCHID: A Chinese Debate Corpus for Target-Independent Stance Detection and Argumentative Dialogue Summarizationcited by 6
- 2025Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialoguecited by 3
- 2025AutoQual: An LLM Agent for Automated Discovery of Interpretable Features for Review Quality Assessmentcited by 2
- 2025Can NLP Tackle Hate Speech in the Real World? Stakeholder-Informed Feedback and Survey on Counterspeechcited by 0
- 2026AI and Collective Decisions: Strengthening Legitimacy and Losers' Consentcited by 0
- 2026The Failure Happens Before the Drift: The Social Dynamics of Values in LLM Agent Societiescited by 0
- 2026The Failure Happens Before the Drift: The Social Dynamics of Values in LLM Agent Societies31 Aug 2026
- 2026AI and Collective Decisions: Strengthening Legitimacy and Losers' Consent7 Apr 2026
- 2025AutoQual: An LLM Agent for Automated Discovery of Interpretable Features for Review Quality Assessment9 Oct 2025
- 2025Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialogue7 Aug 2025
- 2025Can NLP Tackle Hate Speech in the Real World? Stakeholder-Informed Feedback and Survey on Counterspeech6 Aug 2025
- 2024Evaluating Moral Beliefs across LLMs through a Pluralistic Framework6 Nov 2024
- 2024ORCHID: A Chinese Debate Corpus for Target-Independent Stance Detection and Argumentative Dialogue Summarization17 Oct 2024
- 2023The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who27 Apr 2023