PTR: Prompt Tuning with Rules for Text Classification

Recently, prompt tuning has been widely applied to stimulate the rich knowledge in pre-trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved promising results on some few-class classification tasks, such as sentiment classification and natural language inference, manually designing prompts is cumbersome. Meanwhile, generating prompts automatically is also difficult and time-consuming. Therefore, obtaining effective prompts for complex many-class classification tasks still remains a challenge. In this paper, we propose to encode the prior knowledge of a classification task into rules, then design sub-prompts according to the rules, and finally combine the sub-prompts to handle…

• NN
  National Natural Science Foundation of China

  Award: 62236004
• TU
  Tsinghua University
• NK
  National Key Research and Development Program of China

  Award: 2020AAA0106500