  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 are both true/fake news labels, in DAL.
Then, DAL reversely optimizes news-aspect and evidence-aspect debiasing
discriminators to mitigate the impact of news and evidence content biases. At
the same time, DAL also optimizes the main fake news predictor, so that the
news-evidence interaction module can be learned. This process allows us to
teach evidence-aware fake news detection models to better conduct news-evidence
reasoning, and minimize the impact of content biases. To be noted, our proposed
DAL approach is a plug-and-play module that works well with existing backbones.
We conduct comprehensive experiments under two OOD settings, and plug DAL in
four evidence-aware fake news detection backbones. Results demonstrate that,
DAL significantly and stably outperforms the original backbones and some
competitive debiasing methods.
