@inproceedings{mensah-etal-2025-variational,
    title = "A Variational Approach for Mitigating Entity Bias in Relation Extraction",
    author = "Mensah, Samuel  and
      Kochkina, Elena  and
      Magomere, Jabez  and
      Sain, Joy Prakash  and
      Kaur, Simerjot  and
      Smiley, Charese",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.acl-short.53/",
    pages = "676--684",
    ISBN = "979-8-89176-252-7",
    abstract = "Mitigating entity bias is a critical challenge in Relation Extraction (RE), where models often rely excessively on entities, resulting in poor generalization. This paper presents a novel approach to address this issue by adapting a Variational Information Bottleneck (VIB) framework. Our method compresses entity-specific information while preserving task-relevant features. It achieves state-of-the-art performance on both general and financial domain RE datasets, excelling in in-domain settings (original test sets) and out-of-domain (modified test sets with type-constrained entity replacements). Our approach offers a robust, interpretable, and theoretically grounded methodology."
}