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        <title>ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain
%A Zhang, Mike
%A van der Goot, Rob
%A Plank, Barbara
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhang-etal-2023-escoxlm
%X 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.
%R 10.18653/v1/2023.acl-long.662
%U https://aclanthology.org/2023.acl-long.662
%U https://doi.org/10.18653/v1/2023.acl-long.662
%P 11871-11890

Markdown (Informal)

[ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain](https://aclanthology.org/2023.acl-long.662) (Zhang et al., ACL 2023)

• ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain (Zhang et al., ACL 2023)

ACL

• Mike Zhang, Rob van der Goot, and Barbara Plank. 2023. ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11871–11890, Toronto, Canada. Association for Computational Linguistics.