Opening up Minds with Argumentative Dialogues

Youmna Farag, C. Brand, Jacopo Amidei, P. Piwek, T. Stafford, Svetlana Stoyanchev, Andreas Vlachos
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引用次数: 1

Abstract

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.
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用辩论式对话打开思维
最近关于辩论对话的研究集中在说服人们采取一些行动,改变他们在讨论话题上的立场,或者赢得辩论。在这项工作中,我们专注于辩论性对话,旨在打开(而不是改变)人们的思想,帮助他们更加理解不熟悉或与自己信念相反的观点。为此,我们提出了一个关于3个有争议话题的183个辩论对话的数据集:素食主义、英国脱欧和COVID-19疫苗接种。对话是用《绿野仙踪》的方法收集的,向导利用知识库中的论点与参与者交谈。在参与对话之前和之后,使用心理学文献中的问卷来衡量思想的开放程度,而对话的成功是通过参与者对持不同意见的人的立场变化来衡量的。我们评估了两种对话模型:基于维基百科的模型和基于论证的模型。我们表明,虽然两种模型在开放思维方面表现密切,但基于论点的模型在其他对话属性(如参与度和清晰度)上明显更好。
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