无需标注数据，用LLM生成技能匹配训练集，实现零样本精准提取工作技能。
Contrastive Bi-Encoder Models for Multi-Label Skill Extraction: Enhancing ESCO Ontology Matching with BERT and Attention Mechanisms
- 用LLM从技能定义生成训练数据，结合层级约束提升多技能生成质量。
- 在中文招聘广告上零样本测试，F1@5达0.72，优于传统方法。
- 适合劳动力分析、职业匹配等需自动标注技能的场景。
细粒度劳动力市场分析越来越依赖将非结构化招聘广告映射到标准化技能分类体系（如ESCO）。这一任务可自然建模为极端多标签分类（XMLC）问题，但监督方法受限于大规模、对齐分类标签数据的稀缺性和高成本，尤其在非英语语境下，招聘信息语言与正式技能定义差异显著。本文提出一种零样本技能提取框架，无需人工标注的招聘广告训练数据。该框架利用大语言模型（LLM）从ESCO定义中合成训练样本，并基于ESCO Level-2类别引入层级约束的多技能生成机制，以提升多标签上下文中的语义连贯性。在此合成语料基础上，训练一个对比双编码器，将招聘句子与ESCO技能描述映射到共享嵌入空间；编码器在BERT主干上融合BiLSTM和注意力池化，以更好建模长而信息密集的要求语句。上游使用RoBERTa的二分类过滤器剔除非技能句，提升端到端精度。实验表明：(i) 层级约束生成相比无约束配对，在流畅性和可区分性上均有提升；(ii) 所得多标签模型在真实中文招聘广告上具有良好零样本迁移能力，取得F1@5 = 0.72的优异检索性能，超越TF-IDF与标准BERT基线。整体而言，该流程为劳动力经济学与人才分析中的自动化技能标注提供了可扩展、数据高效的路径。
原文摘要 · Abstract (English)
Fine-grained labor market analysis increasingly relies on mapping unstructured job advertisements to standardized skill taxonomies such as ESCO. This mapping is naturally formulated as an Extreme Multi-Label Classification (XMLC) problem, but supervised solutions are constrained by the scarcity and cost of large-scale, taxonomy-aligned annotations--especially in non-English settings where job-ad language diverges substantially from formal skill definitions. We propose a zero-shot skill extraction framework that eliminates the need for manually labeled job-ad training data. The framework uses a Large Language Model (LLM) to synthesize training instances from ESCO definitions, and introduces hierarchically constrained multi-skill generation based on ESCO Level-2 categories to improve semantic coherence in multi-label contexts. On top of the synthetic corpus, we train a contrastive bi-encoder that aligns job-ad sentences with ESCO skill descriptions in a shared embedding space; the encoder augments a BERT backbone with BiLSTM and attention pooling to better model long, information-dense requirement statements. An upstream RoBERTa-based binary filter removes non-skill sentences to improve end-to-end precision. Experiments show that (i) hierarchy-conditioned generation improves both fluency and discriminability relative to unconstrained pairing, and (ii) the resulting multi-label model transfers effectively to real-world Chinese job advertisements, achieving strong zero-shot retrieval performance (F1@5 = 0.72) and outperforming TF--IDF and standard BERT baselines. Overall, the proposed pipeline provides a scalable, data-efficient pathway for automated skill coding in labor economics and workforce analytics.
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