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This paper presents a comprehensive study on resume classification to reducethe time and labor needed to screen an overwhelming number of applicationssignificantly, while improving the selection of suitable candidates. A total of6,492 resumes are extracted from 24,933 job applications for 252 positionsdesignated into four levels of experience for Clinical Research Coordinators(CRC). Each resume is manually annotated to its most appropriate CRC positionby experts through several rounds of triple annotation to establish guidelines.As a result, a high Kappa score of 61% is achieved for inter-annotatoragreement. Given this dataset, novel transformer-based classification modelsare developed for two tasks: the first task takes a resume and classifies it toa CRC level (T1), and the second task takes both a resume and a job descriptionto apply and predicts if the application is suited to the job T2. Our bestmodels using section encoding and multi-head attention decoding give results of73.3% to T1 and 79.2% to T2. Our analysis shows that the prediction errors aremostly made among adjacent CRC levels, which are hard for even experts todistinguish, implying the practical value of our models in real HR platforms.

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