ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain

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


The increasing number of benchmarks for Natural Language Processing (NLP) tasks in the computational job market domain highlights the demand for methods that can handle job-related tasks such as skill extraction, skill classification, job title classification, and de-identification. While some approaches have been developed that are specific to the job market domain, there is a lack of generalized, multilingual models and benchmarks for these tasks. In this study, we introduce a language model called ESCOXLM-R, based on XLM-R-large, which uses domain-adaptive pre-training on the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy, covering 27 languages. The pre-training objectives for ESCOXLM-R include dynamic masked language modeling and a novel additional objective for inducing multilingual taxonomical ESCO relations. We comprehensively evaluate the performance of ESCOXLM-R on 6 sequence labeling and 3 classification tasks in 4 languages and find that it achieves state-of-the-art results on 6 out of 9 datasets. Our analysis reveals that ESCOXLM-R performs better on short spans and outperforms XLM-R-large on entity-level and surface-level span-F1, likely due to ESCO containing short skill and occupation titles, and encoding information on the entity-level.
Original languageEnglish
Title of host publicationThe 61st Annual Meeting of the Association for Computational Linguistics
Number of pages20
VolumeProceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Place of PublicationToronto, Canada
PublisherAssociation for Computational Linguistics
Publication dateJul 2023
Publication statusPublished - Jul 2023


  • Job market understanding
  • Natural Language Processing


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