NNOSE: Nearest Neighbor Occupational Skill Extraction
This addresses the need for robust skill extraction in the labor market, though it is incremental as it builds on existing retrieval-augmented language models.
The paper tackled the problem of automatically extracting occupational skills from diverse job description datasets by proposing a retrieval-augmented method that leverages multiple datasets without fine-tuning, resulting in up to 30% span-F1 gain for infrequent skills in cross-dataset settings.
The labor market is changing rapidly, prompting increased interest in the automatic extraction of occupational skills from text. With the advent of English benchmark job description datasets, there is a need for systems that handle their diversity well. We tackle the complexity in occupational skill datasets tasks -- combining and leveraging multiple datasets for skill extraction, to identify rarely observed skills within a dataset, and overcoming the scarcity of skills across datasets. In particular, we investigate the retrieval-augmentation of language models, employing an external datastore for retrieving similar skills in a dataset-unifying manner. Our proposed method, \textbf{N}earest \textbf{N}eighbor \textbf{O}ccupational \textbf{S}kill \textbf{E}xtraction (NNOSE) effectively leverages multiple datasets by retrieving neighboring skills from other datasets in the datastore. This improves skill extraction \emph{without} additional fine-tuning. Crucially, we observe a performance gain in predicting infrequent patterns, with substantial gains of up to 30\% span-F1 in cross-dataset settings.