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

arXiv: https://arxiv.org/abs/2011.02998 · HTML: https://mlsanity.com/paper/2011.02998/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

## Built on by (most cited first, 11 of 11)

- [Natural Language Processing for Human Resources: A Survey](https://mlsanity.com/paper/2410.16498/) (2024)
- [Top K Ranking for Multi-Armed Bandit with Noisy Evaluations](https://mlsanity.com/paper/2112.06517/) (2021)
- [From Noise to Signal to Selbstzweck: Reframing Human Label Variation in the Era of Post-training in NLP](https://mlsanity.com/paper/2510.12817/) (2025)
- [AI Security Beyond Core Domains: Resume Screening as a Case Study of Adversarial Vulnerabilities in Specialized LLM Applications](https://mlsanity.com/paper/2512.20164/) (2025)
- [Challenges and Opportunities of NLP for HR Applications: A Discussion Paper](https://mlsanity.com/paper/2405.07766/) (2024)
- [Career-Aware Resume Tailoring via Multi-Source Retrieval-Augmented Generation with Provenance Tracking: A Case Study](https://mlsanity.com/paper/2605.05257/) (2026)
- [JobHam-place with smart recommend job options and candidate filtering options](https://mlsanity.com/paper/2303.17930/) (2023)
- [Beyond the Resum'e: A Rubric-Aware Automatic Interview System for Information Elicitation](https://mlsanity.com/paper/2603.01775/) (2026)
- [How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applications](https://mlsanity.com/paper/2604.22679/) (2026)
- [Evaluating Modeling Approaches for Experience-Level Classification in Job Description](https://mlsanity.com/paper/2610.04304/) (2026)
- [EHR-Bench: A Chinese Multi-Task Benchmark for Recruitment AI](https://mlsanity.com/paper/2610.16689/) (2026)

## Abstract (arXiv)

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.