Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models

TL;DR

This paper develops transformer-based models for classifying resumes into competence levels and matching them with job descriptions, significantly streamlining candidate screening with high accuracy.

Contribution

It introduces novel transformer models for resume classification and matching, utilizing section encoding and multi-head attention, with a new annotated dataset for CRC positions.

Findings

Achieved 73.3% accuracy in classifying CRC levels

Achieved 79.2% accuracy in resume-job matching

Errors mostly occur among adjacent CRC levels

Abstract

This paper presents a comprehensive study on resume classification to reduce the time and labor needed to screen an overwhelming number of applications significantly, while improving the selection of suitable candidates. A total of 6,492 resumes are extracted from 24,933 job applications for 252 positions designated into four levels of experience for Clinical Research Coordinators (CRC). Each resume is manually annotated to its most appropriate CRC position by experts through several rounds of triple annotation to establish guidelines. As a result, a high Kappa score of 61% is achieved for inter-annotator agreement. Given this dataset, novel transformer-based classification models are developed for two tasks: the first task takes a resume and classifies it to a CRC level (T1), and the second task takes both a resume and a job description to apply and predicts if the application is suited to the job T2. Our best models using section encoding and multi-head attention decoding give results of 73.3% to T1 and 79.2% to T2. Our analysis shows that the prediction errors are mostly made among adjacent CRC levels, which are hard for even experts to distinguish, implying the practical value of our models in real HR platforms.