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.

翻译：证据感知虚假新闻检测旨在对基于新闻内容检索得到的新闻与证据进行推理，以发现一致性或不一致性。然而，我们发现证据感知检测模型存在偏差问题，即新闻/证据内容与真假新闻标签之间的虚假相关性，且难以泛化至分布外场景。为解决此问题，我们提出了一种新型双对抗学习（DAL）方法。在DAL中，我们分别引入以真假新闻标签为目标的新闻层面去偏判别器和证据层面去偏判别器。随后，DAL通过反向优化这两类去偏判别器来减轻新闻与证据内容偏差的影响。同时，DAL还优化主虚假新闻预测器，使新闻-证据交互模块得以学习。该过程使证据感知虚假新闻检测模型能更好地进行新闻-证据推理，并最小化内容偏差的影响。值得注意的是，DAL方法作为即插即用模块，可与现有主干网络良好配合。我们在两种分布外设置下开展综合实验，将DAL集成至四个证据感知虚假新闻检测主干网络中。结果表明，DAL显著且稳定地优于原始主干网络及多种竞争性去偏方法。