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 it is 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 (which part is true and which part is 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 have gathered a
semi-automatically generated dataset called FACTIFY-5WQA, which consists of
395, 019 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 be served as the baseline for future research in this
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 this https URL

本文提出了一个基于 5W 问答的可解释性事实验证框架，使用语义角色标记系统定位 5W 并生成 QA 对，我们获得了一个半自动生成的 FACTIFY-5WQA 数据集，并提出了一个基线 QA 系统自动定位从证据文件中的答案。最后，我们提出了一个强大的事实验证系统，可以对改写的声明进行自动验证。