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Tuning pre-trained language models (PLMs) with task-specific prompts has beena promising approach for text classification. Particularly, previous studiessuggest that prompt-tuning has remarkable superiority in the low-data scenarioover the generic fine-tuning methods with extra classifiers. The core idea ofprompt-tuning is to insert text pieces, i.e., template, to the input andtransform a classification problem into a masked language modeling problem,where a crucial step is to construct a projection, i.e., verbalizer, between alabel space and a label word space. A verbalizer is usually handcrafted orsearched by gradient descent, which may lack coverage and bring considerablebias and high variances to the results. In this work, we focus on incorporatingexternal knowledge into the verbalizer, forming a knowledgeable prompt-tuning(KPT), to improve and stabilize prompt-tuning. Specifically, we expand thelabel word space of the verbalizer using external knowledge bases (KBs) andrefine the expanded label word space with the PLM itself before predicting withthe expanded label word space. Extensive experiments on zero and few-shot textclassification tasks demonstrate the effectiveness of knowledgeableprompt-tuning.

Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification