Out-of-distribution Evidence-aware Fake News Detection via Dual Adversarial Debiasing

arXiv:2304.12888v2 Announce Type: replace
Abstract: Evidence-aware fake news detection aims to conduct reasoning between news and evidence, which is retrieved based on news content, to find uniformity or inconsistency. However, we find evidence-aware detection models suffer from biases, i.e., spurious correlations between news/evidence contents and true/fake news labels, and are hard to be generalized to Out-Of-Distribution (OOD) situations. To deal with this, we propose a novel Dual Adversarial Learning (DAL) approach. We incorporate news-aspect and evidence-aspect debiasing discriminators, whose targets …

abstract adversarial arxiv biases contents correlations cs.ai cs.cl detection distribution evidence fake fake news however labels reasoning true type via