With the success of the prompt-tuning paradigm in Natural Language Processing
(NLP), various prompt templates have been proposed to further stimulate
specific knowledge for serving downstream tasks, e.g., machine translation,
text generation, relation extraction, and so on. Existing prompt templates are
mainly shared among all training samples with the information of task
description. However, training samples are quite diverse. The sharing task
description is unable to stimulate the unique task-related information in each
training sample, especially for tasks with the finite-label space. To exploit
the unique task-related information, we imitate the human decision process
which aims to find the contrastive attributes between the objective factual and
their potential counterfactuals. Thus, we propose the \textbf{C}ounterfactual
\textbf{C}ontrastive \textbf{Prompt}-Tuning (CCPrompt) approach for many-class
classification, e.g., relation classification, topic classification, and entity
typing. Compared with simple classification tasks, these tasks have more
complex finite-label spaces and are more rigorous for prompts. First of all, we
prune the finite label space to construct fact-counterfactual pairs. Then, we
exploit the contrastive attributes by projecting training instances onto every
fact-counterfactual pair. We further set up global prototypes corresponding
with all contrastive attributes for selecting valid contrastive attributes as
additional tokens in the prompt template. Finally, a simple Siamese
representation learning is employed to enhance the robustness of the model. We
conduct experiments on relation classification, topic classification, and
entity typing tasks in both fully supervised setting and few-shot setting. The
results indicate that our model outperforms former baselines.

本研究提出了一种基于对比意义和对立特性的反事实对比提示调优（CCPrompt）方法，可以提高多类别分类任务，例如关系分类、主题分类和实体打标任务的准确性和鲁棒性，并采用简单的暹罗猫（Siamese）表示学习来增强模型的鲁棒性。