jjzha/skillskape

From

@inproceedings{magron-etal-2024-jobskape,
    title = {{JobSkape: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching}},
    author = {{Magron, Antoine  and
      Dai, Anna  and
      Zhang, Mike  and
      Montariol, Syrielle  and
      Bosselut, Antoine}},
    editor = {{Hruschka, Estevam  and
      Lake, Thom  and
      Otani, Naoki  and
      Mitchell, Tom}},
    booktitle = {{Proceedings of the First Workshop on Natural Language Processing for Human Resources (NLP4HR 2024)}},
    month = {{mar}},
    year = {{2024}},
    address = {{St. Julian's, Malta}},
    publisher = {{Association for Computational Linguistics}},
    url = {{https://aclanthology.org/2024.nlp4hr-1.4/}},
    pages = {{43--58}},
    abstract = {{Recent approaches in skill matching, employing synthetic training data for classification or similarity model training, have shown promising results, reducing the need for time-consuming and expensive annotations. However, previous synthetic datasets have limitations, such as featuring only one skill per sentence and generally comprising short sentences. In this paper, we introduce JobSkape, a framework to generate synthetic data that tackles these limitations, 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 that our dataset resembles real-world data. Additionally, we present a multi-step pipeline for skill extraction and matching tasks using large language models (LLMs), benchmarking against known supervised methodologies. We outline that the downstream evaluation results on real-world data can beat baselines, underscoring its efficacy and adaptability.}}
}