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--- |
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language: |
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- en |
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tags: |
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- JobBERTa |
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- job postings |
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--- |
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# JobBERTa |
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This is the JobBERTa model from: |
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**NNOSE: Nearest Neighbor Occupational Skill Extraction**. Mike Zhang, Rob van der Goot, Min-Yen Kan, and Barbara Plank. To appear at EACL 2024. |
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This model is continuously pre-trained from a `roberta-base` checkpoint on ~3.2M sentences from job postings. More information can be found in the paper. |
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If you use this model, please cite the following paper: |
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``` |
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@inproceedings{zhang-etal-2024-nnose, |
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title = "{NNOSE}: Nearest Neighbor Occupational Skill Extraction", |
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author = "Zhang, Mike and |
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Goot, Rob and |
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Kan, Min-Yen and |
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Plank, Barbara", |
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editor = "Graham, Yvette and |
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Purver, Matthew", |
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booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", |
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month = mar, |
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year = "2024", |
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address = "St. Julian{'}s, Malta", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2024.eacl-long.35", |
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pages = "589--608", |
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abstract = "The labor market is changing rapidly, prompting increased interest in the automatic extraction of occupational skills from text. With the advent of English benchmark job description datasets, there is a need for systems that handle their diversity well. We tackle the complexity in occupational skill datasets tasks{---}combining and leveraging multiple datasets for skill extraction, to identify rarely observed skills within a dataset, and overcoming the scarcity of skills across datasets. In particular, we investigate the retrieval-augmentation of language models, employing an external datastore for retrieving similar skills in a dataset-unifying manner. Our proposed method, \textbf{N}earest \textbf{N}eighbor \textbf{O}ccupational \textbf{S}kill \textbf{E}xtraction (NNOSE) effectively leverages multiple datasets by retrieving neighboring skills from other datasets in the datastore. This improves skill extraction \textit{without} additional fine-tuning. Crucially, we observe a performance gain in predicting infrequent patterns, with substantial gains of up to 30{\%} span-F1 in cross-dataset settings.", |
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} |
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``` |
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