  Skill Classification (SC) is the task of classifying job competences from job
postings. This work is the first in SC applied to Danish job vacancy data. We
release the first Danish job posting dataset: Kompetencer (en: competences),
annotated for nested spans of competences. To improve upon coarse-grained
annotations, we make use of The European Skills, Competences, Qualifications
and Occupations (ESCO; le Vrang et al., 2014) taxonomy API to obtain
fine-grained labels via distant supervision. We study two setups: The zero-shot
and few-shot classification setting. We fine-tune English-based models and
RemBERT (Chung et al., 2020) and compare them to in-language Danish models. Our
results show RemBERT significantly outperforms all other models in both the
zero-shot and the few-shot setting.
