mlsanityData to 14 Oct 2026

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

arXiv 2310.14450 ↗First posted 22 Oct 2023cs.CL cs.CY cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 33 of its 50 references that are in the map. First posted on arXiv 22 Oct 2023.

Builds on

33 of 50 references.

References · 33

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21 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024CoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learningcited by 1
  2. 2025Tracking the Takes and Trajectories of English-Language News Narratives across Trustworthy and Worrisome Websitescited by 1
  3. 2025Emotion-aware Dual Cross-Attentive Neural Network with Label Fusion for Stance Detection in Misinformative Social Media Contentcited by 1
  4. 2026Zero-Shot Stance Detection in the Wild: Dynamic Target Generation and Multi-Target Adaptationcited by 1