  This paper studies the problem of stance detection which aims to predict the
perspective (or stance) of a given document with respect to a given claim.
Stance detection is a major component of automated fact checking. As annotating
stances in different domains is a tedious and costly task, automatic methods
based on machine learning are viable alternatives. In this paper, we focus on
adversarial domain adaptation for stance detection where we assume there exists
sufficient labeled data in the source domain and limited labeled data in the
target domain. Extensive experiments on publicly available datasets show the
effectiveness of our domain adaption model in transferring knowledge for
accurate stance detection across domains.
