Claim Verification using a Multi-GAN based Model
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 46 references that are in the map. First posted on arXiv 14 Mar 2021.
Builds on
12 of 46 references.
References · 12
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
- 2018AllenNLP: A Deep Semantic Natural Language Processing Platformcited by 347
- 2017Automatic Detection of Fake Newscited by 145
- 2018The Fact Extraction and VERification (FEVER) Shared Taskcited by 108
- 2017Long Text Generation via Adversarial Training with Leaked Informationcited by 100
- 2018DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learningcited by 100
- 2018Predicting Factuality of Reporting and Bias of News Media Sourcescited by 71
- 2018TI-CNN: Convolutional Neural Networks for Fake News Detectioncited by 22
- 2019Adversarial attacks against Fact Extraction and VERificationcited by 11
- 2018Adversarial Text Generation Without Reinforcement Learningcited by 7
- 2015Fusing Data with Correlationscited by 2
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Citing papers per year: 2022: 1.
1 paper.