  Stance detection is an important component of understanding hidden influences
in everyday life. Since there are thousands of potential topics to take a
stance on, most with little to no training data, we focus on zero-shot stance
detection: classifying stance from no training examples. In this paper, we
present a new dataset for zero-shot stance detection that captures a wider
range of topics and lexical variation than in previous datasets. Additionally,
we propose a new model for stance detection that implicitly captures
relationships between topics using generalized topic representations and show
that this model improves performance on a number of challenging linguistic
phenomena.
