用于跨语言职位名称匹配的多语言JobBERT
Multilingual JobBERT for Cross-Lingual Job Title Matching
AI总结：
本研究提出基于对比学习的多语言职位匹配模型JobBERT-V3，以JobBERT-V2为基础支持4种语言，在TalentCLEF 2025基准上优于基线，还可用于技能排序，已开源。
摘要：
我们提出JobBERT-V3，这是一种基于对比学习的跨语言职位名称匹配模型。本方法以当前最优的单语言JobBERT-V2为基础，借助合成翻译数据和包含超2100万条职位名称的均衡多语言数据集，将支持范围扩展至英语、德语、西班牙语和中文。该模型保留了前代模型侧重效率的架构，同时无需任务特定监督即可实现跨语言的稳健对齐。在TalentCLEF 2025基准上的大量评估表明，JobBERT-V3性能优于强多语言基线，且在单语言和跨语言场景下均能实现稳定表现。虽非研究重点，我们也证实该模型可有效用于为给定职位名称排序相关技能，展现了其在多语言劳动力市场智能领域更广泛的适用性。该模型已公开发布：https://huggingface.co/TechWolf/JobBERT-v3。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
