Knowledge Stimulated Contrastive Prompting for Low-Resource Stance Detection
Kai Zheng, Qingfeng Sun, Yaming Yang, Fei Xu · 2022
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.