The spread of misinformation, propaganda, and flawed argumentation has been amplified in the Internet era. Given the volume of data and the subtlety of identifying violations of argumentation norms, supporting information analytics tasks, like content moderation, with trustworthy methods that can identify logical fallacies is essential. In this paper, we formalize prior theoretical work on logical fallacies into a comprehensive three-stage evaluation framework of detection, coarse-grained, and fine-grained classification. We adapt existing evaluation datasets for each stage of the evaluation. We employ three families of robust and explainable methods based on prototype reasoning, instance-based reasoning, and knowledge injection. The methods combine language models with background knowledge and explainable mechanisms. Moreover, we address data sparsity with strategies for data augmentation and curriculum learning. Our three-stage framework natively consolidates prior datasets and methods from existing tasks, like propaganda detection, serving as an overarching evaluation testbed. We extensively evaluate these methods on our datasets, focusing on their robustness and explainability. Our results provide insight into the strengths and weaknesses of the methods on different components and fallacy classes, indicating that fallacy identification is a challenging task that may require specialized forms of reasoning to capture various classes. We share our open-source code and data on GitHub to support further work on logical fallacy identification.

翻译：错误信息、宣传和缺陷论证的传播在互联网时代被放大。鉴于数据量巨大且识别违反论证规范行为的微妙性，支持内容审核等信息分析任务时，采用能够识别逻辑谬误的可信方法至关重要。本文将对逻辑谬误的先前理论工作进行形式化，构建一个包含检测、粗粒度分类和细粒度分类的综合三阶段评估框架。我们为每个评估阶段适配现有评估数据集，采用基于原型推理、实例推理和知识注入的三类鲁棒且可解释的方法。这些方法将语言模型与背景知识和可解释机制相结合。此外，我们通过数据增强和课程学习策略解决数据稀疏性问题。该三阶段框架自然整合了现有任务（如宣传检测）中的数据集和方法，作为统一的评估测试平台。我们在数据集上对这些方法进行广泛评估，重点关注其鲁棒性和可解释性。实验结果揭示了不同方法和谬误类别在各组件上的优势与不足，表明谬误识别是一项需要定制化推理形式来捕获各类别的挑战性任务。我们在GitHub上开源代码和数据集，以支持逻辑谬误识别领域的后续研究。