@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://aclanthology.org/2025.acl-short.53/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T A Variational Approach for Mitigating Entity Bias in Relation Extraction
%A Mensah, Samuel
%A Kochkina, Elena
%A Magomere, Jabez
%A Sain, Joy Prakash
%A Kaur, Simerjot
%A Smiley, Charese
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-252-7
%F mensah-etal-2025-variational
%X 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.
%R 10.18653/v1/2025.acl-short.53
%U https://aclanthology.org/2025.acl-short.53/
%U https://doi.org/10.18653/v1/2025.acl-short.53
%P 676-684
Markdown (Informal)
[A Variational Approach for Mitigating Entity Bias in Relation Extraction](https://aclanthology.org/2025.acl-short.53/) (Mensah et al., ACL 2025)
ACL
- Samuel Mensah, Elena Kochkina, Jabez Magomere, Joy Prakash Sain, Simerjot Kaur, and Charese Smiley. 2025. A Variational Approach for Mitigating Entity Bias in Relation Extraction. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 676–684, Vienna, Austria. Association for Computational Linguistics.