Question Answering for Fact-Checking
Mayank Jobanputra · 2019
Recent Deep Learning (DL) models have succeeded in achieving human-level accuracy on various natural languages 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 Clozetask 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.