In this paper, we propose elucidating fact checking predictions using counterfactual explanations to help people understand why a specific piece of news was identified as fake. In this work, generating counterfactual explanations for fake news involves three steps: asking good questions, finding contradictions, and reasoning appropriately. We frame this research question as contradicted entailment reasoning through question answering (QA). We first ask questions towards the false claim and retrieve potential answers from the relevant evidence documents. Then, we identify the most contradictory answer to the false claim by use of an entailment classifier. Finally, a counterfactual explanation is created using a matched QA pair with three different counterfactual explanation forms. Experiments are conducted on the FEVER dataset for both system and human evaluations. Results suggest that the proposed approach generates the most helpful explanations compared to state-of-the-art methods.
翻译:在本文中,我们提议用反事实解释来澄清事实核查预测,以帮助人们理解为什么某一新闻被确定为假消息。在这项工作中,为假新闻提供反事实解释涉及三个步骤:提出好的问题,找出矛盾和恰当的推理。我们通过问答将研究问题定义为自相矛盾的必然推理。我们首先对虚假索赔提出问题,然后通过使用一个要求分类器从相关证据文件中获取可能的答案。然后,我们用一个要求分类器找出对错误索赔的最矛盾的答案。最后,利用一个匹配的QA对和三个不同的反事实解释表来创建反事实解释。在FEWER数据集上进行了实验,用于系统和人类评估。结果显示,与最新方法相比,拟议方法产生了最有用的解释。