JobSkape: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching

Antoine Magron, Anna Dai, Mike Zhang, Syrielle Montariol, Antoine Bosselut

Publikation: Konference artikel i Proceeding eller bog/rapport kapitelKonferencebidrag i proceedingsForskningpeer review

Abstract

Recent approaches in skill-to-surface-form matching, employing synthetic training data for classification or similarity model training, have shown promising results, eliminating the need for time-consuming and expensive annotation. However, previous datasets have limitations, such as featuring only one skill per sentence and generally comprising short sentences. This paper introduces JobSkape, a framework to generate synthetic data that resembles real-world job postings, specifically designed to enhance skill-to-taxonomy matching. Within this framework, we create SkillSkape, a comprehensive open-source synthetic dataset of job postings tailored for skill-matching tasks. We introduce several offline metrics that show our dataset is more diverse, realistic, and follows a higher quality based on similarities. Additionally, we present a multi-step pipeline utilizing large language models (LLMs), benchmarking against supervised methodologies. We outline that the performances are comparable and that each method can be used for different use cases.
OriginalsprogEngelsk
Titel1st Workshop on Natural Language Processing for Human Resources
Antal sider16
ForlagAssociation for Computational Linguistics
Publikationsdatomar. 2024
Sider43–58
StatusUdgivet - mar. 2024
BegivenhedNLP4HR WORKSHOP 2024: Workshop on Natural Language Processing for Human Resources - St. Julians, Malta
Varighed: 22 mar. 2024 → …
https://megagon.ai/nlp4hr-2024/

Workshop

WorkshopNLP4HR WORKSHOP 2024
Land/OmrådeMalta
BySt. Julians
Periode22/03/2024 → …
Internetadresse

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