  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.
