# Multilingual JobBERT for Cross-Lingual Job Title Matching

arXiv: https://arxiv.org/abs/2507.21609 · HTML: https://mlsanity.com/paper/2507.21609/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [GPT-4 vs. Human Translators: A Comprehensive Evaluation of Translation Quality Across Languages, Domains, and Expertise Levels](https://mlsanity.com/paper/2407.03658/) (2024)
- [Learning Job Titles Similarity from Noisy Skill Labels](https://mlsanity.com/paper/2207.00494/) (2022)
- [VacancySBERT: the approach for representation of titles and skills for semantic similarity search in the recruitment domain](https://mlsanity.com/paper/2307.16638/) (2023)
- [Learning Job Title Representation from Job Description Aggregation Network](https://mlsanity.com/paper/2406.08055/) (2024)
- [Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management](https://mlsanity.com/paper/2507.13275/) (2025)

## Built on by (most cited first, 4 of 4)

- [WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain](https://mlsanity.com/paper/2604.13055/) (2026)
- [Unified Work Embeddings: Contrastive Learning of a Bidirectional Multi-task Ranker](https://mlsanity.com/paper/2511.07969/) (2025)
- [UniSkill: A Dataset for Matching University Curricula to Professional Competencies](https://mlsanity.com/paper/2603.03134/) (2026)
- [Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Management](https://mlsanity.com/paper/2606.31692/) (2026)

## Abstract (arXiv)

We introduce JobBERT-V3, a contrastive learning-based model for cross-lingual job title matching. Building on the state-of-the-art monolingual JobBERT-V2, our approach extends support to English, German, Spanish, and Chinese by leveraging synthetic translations and a balanced multilingual dataset of over 21 million job titles. The model retains the efficiency-focused architecture of its predecessor while enabling robust alignment across languages without requiring task-specific supervision. Extensive evaluations on the TalentCLEF 2025 benchmark demonstrate that JobBERT-V3 outperforms strong multilingual baselines and achieves consistent performance across both monolingual and cross-lingual settings. While not the primary focus, we also show that the model can be effectively used to rank relevant skills for a given job title, demonstrating its broader applicability in multilingual labor market intelligence. The model is publicly available: https://huggingface.co/TechWolf/JobBERT-v3.