Prompt learning has been shown to achieve near-Fine-tune performance in most
text classification tasks with very few training examples. It is advantageous
for NLP tasks where samples are scarce. In this paper, we attempt to apply it
to a practical scenario, i.e resume information extraction, and to enhance the
existing method to make it more applicable to the resume information extraction
task. In particular, we created multiple sets of manual templates and
verbalizers based on the textual characteristics of resumes. In addition, we
compared the performance of Masked Language Model (MLM) pre-training language
models (PLMs) and Seq2Seq PLMs on this task. Furthermore, we improve the design
method of verbalizer for Knowledgeable Prompt-tuning in order to provide a
example for the design of Prompt templates and verbalizer for other
application-based NLP tasks. In this case, we propose the concept of Manual
Knowledgeable Verbalizer(MKV). A rule for constructing the Knowledgeable
Verbalizer corresponding to the application scenario. Experiments demonstrate
that templates and verbalizers designed based on our rules are more effective
and robust than existing manual templates and automatically generated prompt
methods. It is established that the currently available automatic prompt
methods cannot compete with manually designed prompt templates for some
realistic task scenarios. The results of the final confusion matrix indicate
that our proposed MKV significantly resolved the sample imbalance issue.

本文介绍了一种应用 Prompt learning 到简历信息提取的方法，其中使用了 MLM 和 Seq2Seq PLMs，以及 Manual Knowledgeable Verbalizer (MKV)，并探讨了在应用场景下设计 Prompt templates 和 verbalizer 的规则。实验证明，基于这些规则设计出来的模板和 verbalizer 比现有的手动模板和自动生成的提示方法更有效和更具鲁棒性，同时 MKV 显著解决了样本不平衡问题。