@inproceedings{moslemi-zouaq-2024-tagdebias,
    title = "{T}ag{D}ebias: Entity and Concept Tagging for Social Bias Mitigation in Pretrained Language Models",
    author = "Moslemi, Mehrnaz  and
      Zouaq, Amal",
    editor = "Duh, Kevin  and
      Gomez, Helena  and
      Bethard, Steven",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.findings-naacl.101/",
    doi = "10.18653/v1/2024.findings-naacl.101",
    pages = "1553--1567",
    abstract = "Pre-trained language models (PLMs) play a crucial role in various applications, including sensitive domains such as the hiring process. However, extensive research has unveiled that these models tend to replicate social biases present in their pre-training data, raising ethical concerns. In this study, we propose the TagDebias method, which proposes debiasing a dataset using type tags. It then proceeds to fine-tune PLMs on this debiased dataset. Experiments show that our proposed TagDebias model, when applied to a ranking task, exhibits significant improvements in bias scores."
}