Abstract
Stance Detection Task (SDT) aims at identifying the stance of the sentence towards a specific target and is usually modeled as a classification problem. Backgound knowledge is often necessary for stance detection with respect to a specific target, especially when there is no target explicitly mentioned in text. This paper focuses on the knowledge stimulation for low-resource stance detection tasks. We firstly explore to formalize stance detection as a prompt based contrastive learning task. At the same time, to make prompt learning suit to stance detection, we design a template mechanism to incorporate corresponding target into instance representation. Furthermore, we propose a masked language prompt joint contrastive learning approach to stimulate the knowledge inherit from the pre-trained model. The experimental results on three benchmarks show that knowledge stimulation is effective in stance detection accompanied with our proposed mechanism.- Anthology ID:
- 2022.findings-emnlp.83
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1168–1178
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.83
- DOI:
- 10.18653/v1/2022.findings-emnlp.83
- Cite (ACL):
- Kai Zheng, Qingfeng Sun, Yaming Yang, and Fei Xu. 2022. Knowledge Stimulated Contrastive Prompting for Low-Resource Stance Detection. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1168–1178, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Knowledge Stimulated Contrastive Prompting for Low-Resource Stance Detection (Zheng et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2022.findings-emnlp.83.pdf