The automatic detection of satire vs. regular news is relevant for downstream applications (for instance, knowledge base population) and to improve the understanding of linguistic characteristics of satire. Recent approaches build upon corpora which have been labeled automatically based on article sources. We hypothesize that this encourages the models to learn characteristics for different publication sources (e.g., "The Onion" vs. "The Guardian") rather than characteristics of satire, leading to poor generalization performance to unseen publication sources. We therefore propose a novel model for satire detection with an adversarial component to control for the confounding variable of publication source. On a large novel data set collected from German news (which we make available to the research community), we observe comparable satire classification performance and, as desired, a considerable drop in publication classification performance with adversarial training. Our analysis shows that the adversarial component is crucial for the model to learn to pay attention to linguistic properties of satire.

本文介绍了一种新的用于讽刺检测的模型，该模型包括对抗性部分以控制发布源的混杂变量，最终得到了可观的讽刺分类性能和显著下降的发布分类性能。通过分析得出，对抗性组件是该模型关注讽刺语言属性的关键。

对抗性训练在讽刺检测中的应用：对混淆变量的控制