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        <title>EZ-STANCE: A Large Dataset for English Zero-Shot Stance Detection</title>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2024.acl-long.838</identifier>
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%0 Conference Proceedings
%T EZ-STANCE: A Large Dataset for English Zero-Shot Stance Detection
%A Zhao, Chenye
%A Caragea, Cornelia
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F zhao-caragea-2024-ez
%X 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.
%R 10.18653/v1/2024.acl-long.838
%U https://aclanthology.org/2024.luhme-long.838/
%U https://doi.org/10.18653/v1/2024.acl-long.838
%P 15697-15714

Markdown (Informal)

[EZ-STANCE: A Large Dataset for English Zero-Shot Stance Detection](https://aclanthology.org/2024.luhme-long.838/) (Zhao & Caragea, ACL 2024)

• EZ-STANCE: A Large Dataset for English Zero-Shot Stance Detection (Zhao & Caragea, ACL 2024)

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.