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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.naacl-main.379.pdf
- Code
- MalavikaSrikanth16/adversarial-learning-for-stance