  Recently, prompt-based fine-tuning has garnered considerable interest as a
core technique for few-shot text classification task. This approach
reformulates the fine-tuning objective to align with the Masked Language
Modeling (MLM) objective. Leveraging unlabeled data, prompt-based self-training
has shown greater effectiveness in binary and three-class classification.
However, prompt-based self-training for multi-class classification has not been
adequately investigated, despite its significant applicability to real-world
scenarios. Moreover, extending current methods to multi-class classification
suffers from the verbalizer that extracts the predicted value of manually
pre-defined single label word for each class from MLM predictions.
Consequently, we introduce a novel, efficient verbalizer structure, named
Mapping-free Automatic Verbalizer (MAV). Comprising two fully connected layers,
MAV serves as a trainable verbalizer that automatically extracts the requisite
word features for classification by capitalizing on all available information
from MLM predictions. Experimental results on five multi-class classification
datasets indicate MAV's superior self-training efficacy.
