Adversarial Learning for Zero-Shot Stance Detection on Social Media

Emily Allaway, Malavika Srikanth, Kathleen McKeown


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.
Anthology ID:
2021.naacl-main.379
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4756–4767
Language:
URL:
https://aclanthology.org/2021.naacl-main.379
DOI:
10.18653/v1/2021.naacl-main.379
Bibkey:
Cite (ACL):
Emily Allaway, Malavika Srikanth, and Kathleen McKeown. 2021. Adversarial Learning for Zero-Shot Stance Detection on Social Media. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4756–4767, Online. Association for Computational Linguistics.
Cite (Informal):
Adversarial Learning for Zero-Shot Stance Detection on Social Media (Allaway et al., NAACL 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.naacl-main.379.pdf
Video:
 https://preview.aclanthology.org/ingestion-script-update/2021.naacl-main.379.mp4
Code
 MalavikaSrikanth16/adversarial-learning-for-stance