Abstract
Stance detection is an important component of understanding hidden influences in everyday life. Since there are thousands of potential topics to take a stance on, most with little to no training data, we focus on zero-shot stance detection: classifying stance from no training examples. In this paper, we present a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets. Additionally, we propose a new model for stance detection that implicitly captures relationships between topics using generalized topic representations and show that this model improves performance on a number of challenging linguistic phenomena.- Anthology ID:
- 2020.emnlp-main.717
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8913–8931
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.717
- DOI:
- 10.18653/v1/2020.emnlp-main.717
- Cite (ACL):
- Emily Allaway and Kathleen McKeown. 2020. Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8913–8931, Online. Association for Computational Linguistics.
- Cite (Informal):
- Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations (Allaway & McKeown, EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2020.emnlp-main.717.pdf
- Code
- emilyallaway/zero-shot-stance
- Data
- VAST