@inproceedings{kavas-etal-2024-enhancing,
    title = "Enhancing Job Posting Classification with Multilingual Embeddings and Large Language Models",
    author = "Kavas, Hamit  and
      Serra-Vidal, Marc  and
      Wanner, Leo",
    editor = "Dell'Orletta, Felice  and
      Lenci, Alessandro  and
      Montemagni, Simonetta  and
      Sprugnoli, Rachele",
    booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
    month = dec,
    year = "2024",
    address = "Pisa, Italy",
    publisher = "CEUR Workshop Proceedings",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.clicit-1.53/",
    pages = "440--450",
    ISBN = "979-12-210-7060-6",
    abstract = "In the modern labour market, taxonomies such the European Skills, Competences, Qualifications and Occupations (ESCO) classification are used as an interlingua to match job postings with job seeker profiles. Both are classified with respect to ESCO occupations, and match if they align with the same occupation and the same skills assigned to the occupation. However, matching models usually struggle with the classification because of overlapping skills and similar definitions of occupations defined in the ESCO taxonomy. This often leads to imprecise classification outcomes. In this paper, we focus on the challenge of the classification of job postings written in Italian or Spanish against ESCO occupations written in English. We experiment with multilingual embeddings, zero-shot classification, and use of a large language model (LLM) and show that the use of an LLM leads to best results."
}