  Stance detection is important for understanding different attitudes and
beliefs on the Internet. However, given that a passage's stance toward a given
topic is often highly dependent on that topic, building a stance detection
model that generalizes to unseen topics is difficult. In this work, we propose
using contrastive learning as well as an unlabeled dataset of news articles
that cover a variety of different topics to train topic-agnostic/TAG and
topic-aware/TAW embeddings for use in downstream stance detection. Combining
these embeddings in our full TATA model, we achieve state-of-the-art
performance across several public stance detection datasets (0.771 $F_1$-score
on the Zero-shot VAST dataset). We release our code and data at
https://github.com/hanshanley/tata.
