  The effectiveness of prompt learning has been demonstrated in different
pre-trained language models. By formulating suitable template and choosing
representative label mapping, prompt learning can be used as an efficient
knowledge probe. However, finding suitable prompt in existing methods requires
multiple experimental attempts or appropriate vector initialization on
formulating suitable template and choosing representative label mapping, which
it is more common in few-shot learning tasks. Motivating by PLM working
process, we try to construct the prompt from task semantic perspective and thus
propose the STPrompt -Semantic-guided and Task-driven Prompt model.
Specifically, two novel prompts generated from the semantic dependency tree
(Dep-prompt) and task-specific metadata description (Meta-prompt), are firstly
constructed in a prompt augmented pool, and the proposed model would
automatically select a suitable semantic prompt to motivating the prompt
learning process. Our results show that the proposed model achieves the
state-of-the-art performance in five different datasets of few-shot text
classification tasks, which prove that more semantic and significant prompts
could assume as a better knowledge proving tool.
