A Few-shot Approach to Resume Information Extraction via Prompts

TL;DR

This paper introduces a few-shot prompt learning approach for resume information extraction, emphasizing custom templates and verbalizers, notably the Manual Knowledgeable Verbalizer (MKV), to improve robustness and effectiveness.

Contribution

It proposes the MKV rule for designing verbalizers tailored to resume extraction, enhancing prompt learning performance over existing automatic methods.

Findings

MKV improves template robustness and effectiveness.

MKV surpasses automatic prompt methods in sample imbalance.

Tailored prompts significantly enhance resume information extraction.

Abstract

Prompt learning's fine-tune performance on text classification tasks has attracted the NLP community. This paper applies it to resume information extraction, improving existing methods for this task. We created manual templates and verbalizers tailored to resume texts and compared the performance of Masked Language Model (MLM) and Seq2Seq PLMs. Also, we enhanced the verbalizer design for Knowledgeable Prompt-tuning, contributing to prompt template design across NLP tasks. We present the Manual Knowledgeable Verbalizer (MKV), a rule for constructing verbalizers for specific applications. Our tests show that MKV rules yield more effective, robust templates and verbalizers than existing methods. Our MKV approach resolved sample imbalance, surpassing current automatic prompt methods. This study underscores the value of tailored prompt learning for resume extraction, stressing the importance of custom-designed templates and verbalizers.