  Although stance detection has made great progress in the past few years, it
is still facing the problem of unseen targets. In this study, we investigate
the domain difference between targets and thus incorporate attention-based
conditional encoding with adversarial domain generalization to perform unseen
target stance detection. Experimental results show that our approach achieves
new state-of-the-art performance on the SemEval-2016 dataset, demonstrating the
importance of domain difference between targets in unseen target stance
detection.
