Incorporating target-aware knowledge into prompt-tuning for few-shot stance detection

Stance detection, a fundamental task in natural language processing, identifies user stances in texts towards specific targets. The diverse targets and ever-changing expressions make it challenging to attain comprehensive knowledge from limited data. Existing methods focus on incorporating supplementary knowledge, neglecting fusion consistency during training, which is critical for preserving the rationality of the inference. In this paper, we introduce TAP, a novel approach for few-shot stance detection. TAP extends the verbalizer hierarchically, a mapping function in prompt-tuning. Constructed using a log-odds ratio of topics and targets, the verbalizer refines candidates with prior knowledge, forming the foundation for subsequent hierarchical distillation. The hierarchical distillation, a technique based on pilot experiments on the hierarchical verbalizer, ensures the fusion of diverse knowledge during prompt-tuning, maintaining consistency throughout the training process. Notably, TAP constructs verbalizers without external knowledge augmentation. The hierarchical distillation involves a joint loss function, contributing to the model’s robustness and training consistency. Extensive experiments are conducted on SemEval2016t6 and ArgMin datasets with 13 different targets. The proposed method is evaluated on various few-shot and full-data settings with F1-Macro and F1-Micro scores. On average, TAP achieves overall improvements of 4.71% and 3.76% over state-of-the-art baselines on Semeval2016t6 and ArgMin datasets, respectively, in few-shot scenarios.