利用说服性对话对抗仇恨言论的策略整合
Consolidating Strategies for Countering Hate Speech Using Persuasive Dialogues
AI总结：
本文提出利用Walton论证方案、反驳言语行为和人格特质等可控特征生成反驳仇恨言论的说服性对话策略，并通过自动与人工评估确定最佳特征组合。
摘要：
仇恨性评论在社交媒体平台上普遍存在。尽管用于自动检测、标记和屏蔽此类虚假、冒犯性和有害在线内容的工具近来已经成熟，但仅靠这种被动且粗暴的方法只能提供短期和表面化的补救，而施害者依然持续存在。随着能够大规模生成表达清晰、合成且引人入胜内容的大语言模型的公开可用，人们担忧此类恶意内容在网络上的传播会迅速增长。现在需要关注更深层次、长期的解决方案，即与内容源头背后的人类施害者进行接触，以改变其观点，或至少通过说服性手段降低其言论的激烈程度。为此，我们提出定义并实验可控策略，用于在在线对话中生成针对仇恨评论的反驳论点。我们实验利用基于以下方面的特征来控制回复生成：（i）论证结构和基于推理的Walton论证方案，（ii）反驳言语行为，以及（iii）基于人类特征的特质，如Big-5人格特质和人类价值观。通过自动评估和人工评估，我们确定了能够生成流畅、具有论证性且逻辑合理的对抗仇恨论点的最佳特征组合。我们进一步分享了所开发的计算模型，用于自动为文本标注这些特征，并分享了一个现有仇恨言语对话语料库的银标准标注版本。
英文摘要：
Hateful comments are prevalent on social media platforms. Although tools for automatically detecting, flagging, and blocking such false, offensive, and harmful content online have lately matured, such reactive and brute force methods alone provide short-term and superficial remedies while the perpetrators persist. With the public availability of large language models which can generate articulate synthetic and engaging content at scale, there are concerns about the rapid growth of dissemination of such malicious content on the web. There is now a need to focus on deeper, long-term solutions that involve engaging with the human perpetrator behind the source of the content to change their viewpoint or at least bring down the rhetoric using persuasive means. To do that, we propose defining and experimenting with controllable strategies for generating counter-arguments to hateful comments in online conversations. We experiment with controlling response generation using features based on (i) argument structure and reasoning-based Walton argument schemes, (ii) counter-argument speech acts, and (iii) human characteristics-based qualities such as Big-5 personality traits and human values. Using automatic and human evaluations, we determine the best combination of features that generate fluent, argumentative, and logically sound arguments for countering hate. We further share the developed computational models for automatically annotating text with such features, and a silver-standard annotated version of an existing hate speech dialog corpora.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
