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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 F₁-score on the Zero-shot VAST dataset). We release our code and data at https://github.com/hanshanley/tata.</abstract>
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%0 Conference Proceedings
%T TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings
%A Hanley, Hans
%A Durumeric, Zakir
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F hanley-durumeric-2023-tata
%X 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 F₁-score on the Zero-shot VAST dataset). We release our code and data at https://github.com/hanshanley/tata.
%R 10.18653/v1/2023.emnlp-main.694
%U https://aclanthology.org/2023.emnlp-main.694
%U https://doi.org/10.18653/v1/2023.emnlp-main.694
%P 11280-11294

Markdown (Informal)

[TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings](https://aclanthology.org/2023.emnlp-main.694) (Hanley & Durumeric, EMNLP 2023)

• TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings (Hanley & Durumeric, EMNLP 2023)

ACL

• Hans Hanley and Zakir Durumeric. 2023. TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 11280–11294, Singapore. Association for Computational Linguistics.