Adversarial Learning for Zero-Shot Stance Detection on Social Media
TL
TL;DR
This work proposes a new model for zero-shot stance detection on Twitter that uses adversarial learning to generalize across topics and achieves state-of-the-art performance on a number of unseen test topics with minimal computational costs.
Abstract
Stance detection on social media can help to identify and understand slanted news or commentary in everyday life. In this work, we propose a new model for zero-shot stance detection on Twitter that uses adversarial learning to generalize across topics. Our model achieves state-of-the-art performance on a number of unseen test topics with minimal computational costs. In addition, we extend zero-shot stance detection to topics not previously considered, highlighting future directions for zero-shot transfer.
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Zero-Shot Stance Detection: A Dataset and Model Using Generalized Topic Representations
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Multilingual stance detection in social media political debates
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