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        <title>Enhancing Job Posting Classification with Multilingual Embeddings and Large Language Models</title>
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        <namePart type="given">Hamit</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Enhancing Job Posting Classification with Multilingual Embeddings and Large Language Models
%A Kavas, Hamit
%A Serra-Vidal, Marc
%A Wanner, Leo
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F kavas-etal-2024-enhancing
%X 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.
%U https://aclanthology.org/2024.clicit-1.53/
%P 440-450

Markdown (Informal)

[Enhancing Job Posting Classification with Multilingual Embeddings and Large Language Models](https://aclanthology.org/2024.clicit-1.53/) (Kavas et al., CLiC-it 2024)

• Enhancing Job Posting Classification with Multilingual Embeddings and Large Language Models (Kavas et al., CLiC-it 2024)

ACL

• Hamit Kavas, Marc Serra-Vidal, and Leo Wanner. 2024. Enhancing Job Posting Classification with Multilingual Embeddings and Large Language Models. In Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024), pages 440–450, Pisa, Italy. CEUR Workshop Proceedings.