TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings
This work addresses the challenge of building stance detection models that generalize across topics, which is important for analyzing attitudes on the Internet, but it is incremental as it builds on existing contrastive learning and embedding methods.
The paper tackled the problem of stance detection generalization to unseen topics by proposing TATA, a model that combines topic-agnostic and topic-aware embeddings trained with contrastive learning on unlabeled news articles, achieving state-of-the-art performance with a 0.771 F1-score on the Zero-shot VAST dataset.
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.