Abstract
Zero-shot stance detection (ZSSD) aims to determine whether the author of a text is in favor of, against, or neutral toward a target that is unseen during training. In this paper, we present EZ-STANCE, a large English ZSSD dataset with 30,606 annotated text-target pairs. In contrast to VAST, the only other existing ZSSD dataset, EZ-STANCE includes both noun-phrase targets and claim targets, covering a wide range of domains. In addition, we introduce two challenging subtasks for ZSSD: target-based ZSSD and domain-based ZSSD. We provide an in-depth description and analysis of our dataset. We evaluate EZ-STANCE using state-of-the-art deep learning models. Furthermore, we propose to transform ZSSD into the NLI task by applying two 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 ZSSD. We will make our dataset and code available on GitHub.- Anthology ID:
- 2023.findings-emnlp.64
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 897–911
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.64
- DOI:
- 10.18653/v1/2023.findings-emnlp.64
- Cite (ACL):
- Chenye Zhao and Cornelia Caragea. 2023. EZ-STANCE: A Large Dataset for Zero-Shot Stance Detection. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 897–911, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- EZ-STANCE: A Large Dataset for Zero-Shot Stance Detection (Zhao & Caragea, Findings 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.findings-emnlp.64.pdf