
This model accompanies the following paper:

__ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain__

Mike Zhang, Rob van der Goot, and Barbara Plank. In ACL (2023).

If you use this work please cite the following:

```
@inproceedings{zhang-etal-2023-escoxlm,
    title = "{ESCOXLM}-{R}: Multilingual Taxonomy-driven Pre-training for the Job Market Domain",
    author = "Zhang, Mike  and
      van der Goot, Rob  and
      Plank, Barbara",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.662",
    pages = "11871--11890",
    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.",
}

```

Find more information in the Github repository: https://github.com/jjzha/escoxlmr
{"license": "apache-2.0", "tags": ["xlm-roberta", "esco", "hr", "2023.acl-long.662", "job postings"], "pipeline_tag": "fill-mask"}
transformers pytorch xlm-roberta esco hr 2023.acl-long.662 job postings fill-mask license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #esco #hr #2023.acl-long.662 #job postings #fill-mask #license-apache-2.0 #endpoints_compatible #region-us 


This model accompanies the following paper:

__ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain__

Mike Zhang, Rob van der Goot, and Barbara Plank. In ACL (2023).

If you use this work please cite the following:



Find more information in the Github repository: URL
TAGS
#transformers #pytorch #xlm-roberta #esco #hr #2023.acl-long.662 #job postings #fill-mask #license-apache-2.0 #endpoints_compatible #region-us 
