Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models
This work addresses the labor-intensive task of candidate selection in HR, but it is incremental as it applies existing transformer methods to a new domain-specific dataset.
The paper tackles the problem of automating resume screening for Clinical Research Coordinator positions by developing transformer-based models that classify resumes into experience levels and match them to job descriptions, achieving 73.3% accuracy for classification and 79.2% for matching.
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.