Unsupervised Question Answering for Fact-Checking

Understand

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.