Abstract
Zero-shot stance detection (ZSSD) aims to determine whether the author of a text is in favor, against, or neutral toward a target that is unseen during training. In this paper, we present EZ-STANCE, a large English ZSSD dataset with 47,316 annotated text-target pairs. In contrast to VAST, which is the only other large existing ZSSD dataset for English, EZ-STANCE is 2.5 times larger, includes both noun-phrase targets and claim targets that cover a wide range of domains, provides two challenging subtasks for ZSSD: target-based ZSSD and domain-based ZSSD, and contains much harder examples for the neutral class. We evaluate EZ-STANCE using state-of-the-art deep learning models. Furthermore, we propose to transform ZSSD into the NLI task by applying simple yet effective prompts to noun-phrase targets. Our experimental results show that EZ-STANCE is a challenging new benchmark, which provides significant research opportunities on English ZSSD. We publicly release our dataset and code at https://github.com/chenyez/EZ-STANCE.- Anthology ID:
- 2024.acl-long.838
- Volume:
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 15697–15714
- Language:
- URL:
- https://aclanthology.org/2024.acl-long.838
- DOI:
- 10.18653/v1/2024.acl-long.838
- Cite (ACL):
- Chenye Zhao and Cornelia Caragea. 2024. EZ-STANCE: A Large Dataset for English Zero-Shot Stance Detection. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15697–15714, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- EZ-STANCE: A Large Dataset for English Zero-Shot Stance Detection (Zhao & Caragea, ACL 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.acl-long.838.pdf