  This article describes research on claim verification carried out using a
multiple GAN-based model. The proposed model consists of three pairs of
generators and discriminators. The generator and discriminator pairs are
responsible for generating synthetic data for supported and refuted claims and
claim labels. A theoretical discussion about the proposed model is provided to
validate the equilibrium state of the model. The proposed model is applied to
the FEVER dataset, and a pre-trained language model is used for the input text
data. The synthetically generated data helps to gain information which helps
the model to perform better than state of the art models and other standard
classifiers.
