Adversarial Learning for Zero-Shot Stance Detection on Social Media

• URL: http://arxiv.org/abs/2105.06603v1
• Date: Fri, 14 May 2021 01:08:48 GMT
• Title: Adversarial Learning for Zero-Shot Stance Detection on Social Media
• Authors: Emily Allaway, Malavika Srikanth, and Kathleen McKeown
• Abstract summary: 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.
• Score: 1.7702142798241087
• License: http://creativecommons.org/licenses/by/4.0/
• 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 new topics, highlighting future directions for zero-shot transfer.

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