  Recent Deep Learning (DL) models have succeeded in achieving human-level
accuracy on various natural language tasks such as question-answering, natural
language inference (NLI), and textual entailment. These tasks not only require
the contextual knowledge but also the reasoning abilities to be solved
efficiently. In this paper, we propose an unsupervised question-answering based
approach for a similar task, fact-checking. We transform the FEVER dataset into
a Cloze-task by masking named entities provided in the claims. To predict the
answer token, we utilize pre-trained Bidirectional Encoder Representations from
Transformers (BERT). The classifier computes label based on the correctly
answered questions and a threshold. Currently, the classifier is able to
classify the claims as "SUPPORTS" and "MANUAL_REVIEW". This approach achieves a
label accuracy of 80.2% on the development set and 80.25% on the test set of
the transformed dataset.
