@inproceedings{zheng2022knowledge,
author = {Zheng, Kai and Sun, Qingfeng and Yang, Yaming and Xu, Fei},
title = {Knowledge Stimulated Contrastive Prompting for Low-Resource Stance Detection},
organization = {Microsoft},
booktitle = {Association for Computational Linguistics},
year = {2022},
month = {June},
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.},
publisher = {Association for Computational Linguistics},
url = {http://approjects.co.za/?big=en-us/research/publication/knowledge-stimulated-contrastive-prompting-for-low-resource-stance-detection/},
}