A Novel Prompt-tuning Method: Incorporating Scenario-specific Concepts
  into a Verbalizer
Yong Ma Senlin Luo Yu-Ming Shang Zhengjun Li Yong Liu
The verbalizer, which serves to map label words to class labels, is an
essential component of prompt-tuning. In this paper, we present a novel
approach to constructing verbalizers. While existing methods for verbalizer
construction mainly rely on augmenting and refining sets of synonyms or related
words based on class names, this paradigm suffers from a narrow perspective and
lack of abstraction, resulting in limited coverage and high bias in the
label-word space. To address this issue, we propose a label-word construction
process that incorporates scenario-specific concepts. Specifically, we extract
rich concepts from task-specific scenarios as label-word candidates and then
develop a novel cascade calibration module to refine the candidates into a set
of label words for each class. We evaluate the effectiveness of our proposed
approach through extensive experiments on {five} widely used datasets for
zero-shot text classification. The results demonstrate that our method
outperforms existing methods and achieves state-of-the-art results.