Efficient Text Encoders for Labor Market Analysis

Labor market analysis relies on extracting insights from job advertisements, which provide valuable yet unstructured information on job titles and corresponding skill requirements. While state-of-the-art methods for skill extraction achieve strong performance, they depend on large language models (LLMs), which are computationally expensive and slow. In this paper, we propose $\mathbf{\text{ConTeXT-match}}$, a novel contrastive learning approach with token-level attention that is well-suited for the extreme multi-label classification task of skill classification. $\mathbf{\text{ConTeXT-match}}$ significantly improves skill extraction efficiency and performance, achieving state-of-the-art results with a lightweight bi-encoder model. To support robust evaluation, we introduce $\mathbf{\text{Skill-XL}}$, a new benchmark with exhaustive, sentence-level skill annotations that explicitly address the redundancy in the large label space. Finally, we present $\mathbf{\text{JobBERT}}\mathbf{\text{V2}}$, an improved job title normalization model that leverages extracted skills to produce high-quality job title representations. Experiments demonstrate that our models are efficient, accurate, and scalable, making them ideal for large-scale, real-time labor market analysis.