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.

本文提出了一种基于双视图适应网络的领域自适应方法，通过识别领域间语言表达不同的立场表达类型，并将其分别学习适应，进而结合两个视图的部分最佳适应部分，以提高不同域中的立场数据匹配，进而提升单一视图状态下的方法。