TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings

Hans W. A. Hanley; Zakir Durumeric

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arXiv · 2310.14450

TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings

Hans W. A. Hanley·Zakir Durumeric

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Abstract

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.

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Hans W. A. Hanley, Zakir Durumeric. 2023-10-22. TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings. https://arxiv.org/abs/2310.14450

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