Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models (2020)

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 a 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.

## introduction an ongoing challenge for human resource (hr) is the process used to screen and match applicants to a target job description with a goal of minimizing recruiting time while maximizing proper matches. the use of generic job descriptions not clearly stratified by the level of competence or skill sets often leads many candidates to apply every possible job, resulting in misuse of recruiter and applicant's time. a more challenging aspect is the evaluation of unstructured data such as resumes and cvs, which represents about 80% of the data processed daily, a task that is typically not an employer's priority given the manual effort involved @xcite . the current practice for screening applications involves reviewing individual resumes via traditional approaches, that rely on string/regex matching. the scope of posted job positions varies by the hiring organization type, job level, focus area, and more. the latest advent in natural language processing (nlp) enables the large-scale analysis of resumes @xcite . nlp models also allow for a comprehensive analyses on resumes and identification of latent concepts that may easily go unnoticed using a general manual process. this model's ability to infer core skills and qualifications from resumes can be used to normalize necessary content into standard concepts for matching with stated position requirements @xcite . however, the task of resume classification has been under-explored due to the lack of resources for individual research labs and the heterogeneous nature of job solicitations. this paper presents new research that aims to help applicants identify the level of job(s) they are qualified for and to provide recruiters with a rapid way to filter and match the best applicants. for this study, resumes submitted to four levels of clinical research coordinator (crc) positions are used. to the best of our knowledge, this is the first time that resume classification is explored with levels of competence, not categories. the contributions of this work are summarized as follows: ## approach this section introduces transformer-based neural approaches to address the following two tasks: t1 given a resume, decide which level of crc positions that the corresponding applicant is suitable for (section 4.1). ## conclusion this paper proposes two novel tasks, competencelevel classification (t1) and resume-description matching (t2), and provides a high-quality dataset as well as robust models using several transformerbased approaches. the accuracies achieved by our best models, 73.3 for t1 and 79.2 for t2, show a good promise for these models to be deployed in real hr systems. to the best of our knowledge, this is the first time that those two tasks are thoroughly studies, especially with the latest transformer architectures. we will continuously explore to improve these models by integrating expert's knowledge.