  We address the issue of having a limited number of annotations for stance
classification in a new domain, by adapting out-of-domain classifiers with
domain adaptation. Existing approaches often align different domains in a
single, global feature space (or view), which may fail to fully capture the
richness of the languages used for expressing stances, leading to reduced
adaptability on stance data. In this paper, we identify two major types of
stance expressions that are linguistically distinct, and we propose a tailored
dual-view adaptation network (DAN) to adapt these expressions across domains.
The proposed model first learns a separate view for domain transfer in each
expression channel and then selects the best adapted parts of both views for
optimal transfer. We find that the learned view features can be more easily
aligned and more stance-discriminative in either or both views, leading to more
transferable overall features after combining the views. Results from extensive
experiments show that our method can enhance the state-of-the-art single-view
methods in matching stance data across different domains, and that it
consistently improves those methods on various adaptation tasks.
