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--- |
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license: apache-2.0 |
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tags: |
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- xlm-roberta |
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- esco |
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- hr |
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- 2023.acl-long.662 |
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- job postings |
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pipeline_tag: fill-mask |
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--- |
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This model accompanies the following paper: |
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__ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain__ |
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Mike Zhang, Rob van der Goot, and Barbara Plank. In ACL (2023). |
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If you use this work please cite the following: |
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``` |
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@inproceedings{zhang-etal-2023-escoxlm, |
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title = "{ESCOXLM}-{R}: Multilingual Taxonomy-driven Pre-training for the Job Market Domain", |
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author = "Zhang, Mike and |
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van der Goot, Rob and |
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Plank, Barbara", |
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booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", |
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month = jul, |
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year = "2023", |
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address = "Toronto, Canada", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2023.acl-long.662", |
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pages = "11871--11890", |
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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.", |
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} |
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``` |
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Find more information in the Github repository: https://github.com/jjzha/escoxlmr |
