@inproceedings{hanley-durumeric-2023-tata,
    title = "{TATA}: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings",
    author = "Hanley, Hans  and
      Durumeric, Zakir",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.emnlp-main.694/",
    doi = "10.18653/v1/2023.emnlp-main.694",
    pages = "11280--11294",
    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."
}