TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings
Hans William Alexander Hanley, Zakir Durumeric
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.
BibTeX
@inproceedings{
hanley2023tata,
title={{TATA}: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings},
author={Hans William Alexander Hanley and Zakir Durumeric},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=J9Vx7eTuWb}
}