TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings
Submission Type: Regular Long Paper
Submission Track: Sentiment Analysis, Stylistic Analysis, and Argument Mining
Submission Track 2: Computational Social Science and Cultural Analytics
Keywords: Zero-shot, Few-shot, Stance Detection, Topic-Agnostic, Topic-Aware
TL;DR: We utilize topic-agnostic and topic-aware embeddings to perform zero-shot stance detection.
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.
Submission Number: 4248
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