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

Changmao Li, Elaine Fisher, Rebecca Thomas, Steve Pittard, Vicki Hertzberg, Jinho D. Choi


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.
Anthology ID:
2020.emnlp-main.679
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8456–8466
Language:
URL:
https://aclanthology.org/2020.emnlp-main.679
DOI:
10.18653/v1/2020.emnlp-main.679
Bibkey:
Cite (ACL):
Changmao Li, Elaine Fisher, Rebecca Thomas, Steve Pittard, Vicki Hertzberg, and Jinho D. Choi. 2020. Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8456–8466, Online. Association for Computational Linguistics.
Cite (Informal):
Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models (Li et al., EMNLP 2020)
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PDF:
https://aclanthology.org/2020.emnlp-main.679.pdf
Video:
 https://slideslive.com/38938715