The ease and the speed of spreading misinformation and propaganda on the Web motivate the need to develop trustworthy technology for detecting fallacies in natural language arguments. However, state-of-the-art language modeling methods exhibit a lack of robustness on tasks like logical fallacy classification that require complex reasoning. In this paper, we propose a Case-Based Reasoning method that classifies new cases of logical fallacy by language-modeling-driven retrieval and adaptation of historical cases. We design four complementary strategies to enrich the input representation for our model, based on external information about goals, explanations, counterarguments, and argument structure. Our experiments in in-domain and out-of-domain settings indicate that Case-Based Reasoning improves the accuracy and generalizability of language models. Our ablation studies confirm that the representations of similar cases have a strong impact on the model performance, that models perform well with fewer retrieved cases, and that the size of the case database has a negligible effect on the performance. Finally, we dive deeper into the relationship between the properties of the retrieved cases and the model performance.

翻译：互联网上错误信息和宣传的传播之便捷与迅速，促使人们需要开发可靠的技术来检测自然语言论证中的谬误。然而，最先进的语言建模方法在需要复杂推理的任务（如逻辑谬误分类）中表现出鲁棒性不足。本文提出一种基于案例推理的方法，该方法通过语言模型驱动的检索与历史案例适配来对新的逻辑谬误案例进行分类。我们设计了四种互补策略，基于目标、解释、反驳和论证结构的外部信息来丰富模型的输入表示。在领域内和跨领域设置下的实验表明，基于案例推理能提升语言模型的准确性和泛化能力。消融研究证实，相似案例的表示对模型性能有显著影响，模型在使用较少检索案例时表现良好，且案例数据库大小对性能的影响可忽略不计。最后，我们深入探讨了检索案例属性与模型性能之间的关系。