Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Abstract

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.
Original languageEnglish
Title of host publication13th International Conference on Language Resources and Evaluation
Number of pages11
PublisherEuropean Language Resources Association (ELRA)
Publication date16 Jun 2022
Pages436-447
Publication statusPublished - 16 Jun 2022

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