Multilingual JobBERT for Cross-Lingual Job Title Matching
This addresses job matching across languages for multilingual labor market applications, but it is incremental as it builds on an existing model.
The paper tackles cross-lingual job title matching by introducing JobBERT-V3, which extends a monolingual model to support English, German, Spanish, and Chinese using synthetic translations and a dataset of over 21 million job titles, achieving consistent performance on the TalentCLEF 2025 benchmark.
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.