Stance detection on social media can help to identify and understand slanted news or commentary in everyday life. In this work, we propose a new model for zero-shot stance detection on Twitter that uses adversarial learning to generalize across topics. Our model achieves state-of-the-art performance on a number of unseen test topics with minimal computational costs. In addition, we extend zero-shot stance detection to new topics, highlighting future directions for zero-shot transfer.

翻译：社交媒体的发现有助于识别和理解日常生活中的倾斜新闻或评论。 在这项工作中,我们提出在Twitter上采用对抗性学习的零弹定位探测新模式,以对各主题进行概括。我们的模式在一些以最低计算成本的隐性测试专题上取得了最先进的表现。此外,我们把零弹定位探测扩大到新的专题,突出零弹转移的未来方向。