  This paper proposes a novel prompt-based finetuning method (called DLM-SCS)
for few-shot text classification by utilizing the discriminative language model
ELECTRA that is pretrained to distinguish whether a token is original or
generated. The underlying idea is that the prompt instantiated with the true
label should have higher semantic consistency score than other prompts with
false labels. Since a prompt usually consists of several components (or parts),
its semantic consistency can be decomposed accordingly. The semantic
consistency of each component is then computed by making use of the pretrained
ELECTRA model, without introducing extra parameters. Extensive experiments have
shown that our model outperforms several state-of-the-art prompt-based few-shot
methods.
