Adversarial Learning for Zero-Shot Stance Detection on Social Media
Computation and Language
2021-05-17 v1
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.
Keywords
Cite
@article{arxiv.2105.06603,
title = {Adversarial Learning for Zero-Shot Stance Detection on Social Media},
author = {Emily Allaway and Malavika Srikanth and Kathleen McKeown},
journal= {arXiv preprint arXiv:2105.06603},
year = {2021}
}
Comments
To appear in NAACL 2021