Multilingual JobBERT for Cross-Lingual Job Title Matching
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915416776900608 |
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| author | Decorte, Jens-Joris De Lange, Matthias Van Hautte, Jeroen |
| author_facet | Decorte, Jens-Joris De Lange, Matthias Van Hautte, Jeroen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21609 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multilingual JobBERT for Cross-Lingual Job Title Matching Decorte, Jens-Joris De Lange, Matthias Van Hautte, Jeroen Computation and Language 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. |
| title | Multilingual JobBERT for Cross-Lingual Job Title Matching |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.21609 |