Claim Verification using a Multi-GAN based Model

TL;DR

This paper introduces a novel multi-GAN based model for claim verification that generates synthetic data to improve accuracy, validated on the FEVER dataset with superior results.

Contribution

It proposes a multi-GAN framework with three generator-discriminator pairs for claim verification, enhancing performance over existing models.

Findings

Outperforms state-of-the-art claim verification models.

Uses synthetic data to improve classification accuracy.

Validates the model's equilibrium through theoretical analysis.

Abstract

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.