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.

我们提出了一种新颖的对抗性学习方法，称为 “Dual Adversarial Learning”，以解决在新闻内容和证据内容之间进行推理时遇到的偏差问题，并减少内容偏差的影响。

通过双重对抗去偏置的域外证据感知假新闻检测

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

In open-domain dialogue systems, generative approaches have attracted much
attention for response generation. However, existing methods are heavily
plagued by generating safe responses and unnatural responses. To alleviate
these two problems, we propose a novel framework named Dual Adversarial
Learning (DAL) for high-quality response generation. DAL is the first work to
innovatively utilizes the duality between query generation and response
generation to avoid safe responses and increase the diversity of the generated
responses. Additionally, DAL uses adversarial learning to mimic human judges
and guides the system to generate natural responses. Experimental results
demonstrate that DAL effectively improves both diversity and overall quality of
the generated responses. DAL outperforms the state-of-the-art methods regarding
automatic metrics and human evaluations.

通过引入对抗生成学习，利用查询生成与回复生成二者之间的对偶，提高回复的多样化，通过模仿人类评委来引导系统生成自然回复的新颖框架 Dual Adversarial Learning 在高质量回复的生成方面取得了很好的效果。