Claim Verification using a Multi-GAN based Model
This addresses the problem of automated fact-checking for users needing reliable information, but it appears incremental as it builds on existing GAN and language model techniques.
The researchers tackled claim verification by developing a multi-GAN model with three generator-discriminator pairs to generate synthetic data for claims and labels, achieving better performance than state-of-the-art models on the FEVER dataset.
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.