Automatic fact verification has received significant attention recently. Contemporary automatic fact-checking systems focus on estimating truthfulness using numerical scores which are not human-interpretable. A human fact-checker generally follows several logical steps to verify a verisimilitude claim and conclude whether its truthful or a mere masquerade. Popular fact-checking websites follow a common structure for fact categorization such as half true, half false, false, pants on fire, etc. Therefore, it is necessary to have an aspect-based (delineating which part(s) are true and which are false) explainable system that can assist human fact-checkers in asking relevant questions related to a fact, which can then be validated separately to reach a final verdict. In this paper, we propose a 5W framework (who, what, when, where, and why) for question-answer-based fact explainability. To that end, we present a semi-automatically generated dataset called FACTIFY-5WQA, which consists of 391, 041 facts along with relevant 5W QAs - underscoring our major contribution to this paper. A semantic role labeling system has been utilized to locate 5Ws, which generates QA pairs for claims using a masked language model. Finally, we report a baseline QA system to automatically locate those answers from evidence documents, which can serve as a baseline for future research in the field. Lastly, we propose a robust fact verification system that takes paraphrased claims and automatically validates them. The dataset and the baseline model are available at https: //github.com/ankuranii/acl-5W-QA

翻译：自动事实验证近年来受到广泛关注。当代自动事实核查系统侧重于使用数值分数评估真实性，但这些分数缺乏人类可解释性。人工事实核查员通常遵循若干逻辑步骤验证一则看似真实的主张，并判断其是否属实或仅为伪装。主流事实核查网站采用通用结构进行事实分类，如半真、半假、虚假、烈火谎言等。因此，亟需一种基于方面（区分哪些部分为真、哪些为假）的可解释系统，能够辅助人工事实核查员提出与事实相关的恰当问题，并分别验证各问题以得出最终结论。本文提出一种基于问答的事实可解释性5W框架（何人、何事、何时、何地、为何）。为此，我们构建了一个半自动生成的数据集FACTIFY-5WQA，包含391,041条事实及其对应的5W问答对——这是本文的主要贡献。我们采用语义角色标注系统定位5W要素，并利用掩码语言模型为声明生成问答对。最后，我们报告了一个基线问答系统，可自动从证据文档中定位这些答案，为未来该领域研究提供基准。此外，我们提出一个鲁棒的事实验证系统，能够接收释义后的声明并自动验证。数据集和基线模型可通过https://github.com/ankuranii/acl-5W-QA获取。