  Stance detection concerns the classification of a writer's viewpoint towards
a target. There are different task variants, e.g., stance of a tweet vs. a full
article, or stance with respect to a claim vs. an (implicit) topic. Moreover,
task definitions vary, which includes the label inventory, the data collection,
and the annotation protocol. All these aspects hinder cross-domain studies, as
they require changes to standard domain adaptation approaches. In this paper,
we perform an in-depth analysis of 16 stance detection datasets, and we explore
the possibility for cross-domain learning from them. Moreover, we propose an
end-to-end unsupervised framework for out-of-domain prediction of unseen,
user-defined labels. In particular, we combine domain adaptation techniques
such as mixture of experts and domain-adversarial training with label
embeddings, and we demonstrate sizable performance gains over strong baselines
-- both (i) in-domain, i.e., for seen targets, and (ii) out-of-domain, i.e.,
for unseen targets. Finally, we perform an exhaustive analysis of the
cross-domain results, and we highlight the important factors influencing the
model performance.
