A Variational Approach for Mitigating Entity Bias in Relation Extraction
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915340783452160 |
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| author | Mensah, Samuel Kochkina, Elena Magomere, Jabez Sain, Joy Prakash Kaur, Simerjot Smiley, Charese |
| author_facet | Mensah, Samuel Kochkina, Elena Magomere, Jabez Sain, Joy Prakash Kaur, Simerjot Smiley, Charese |
| contents | 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 relation extraction datasets across general, financial, and biomedical domains, in both indomain (original test sets) and out-of-domain (modified test sets with type-constrained entity replacements) settings. Our approach offers a robust, interpretable, and theoretically grounded methodology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11381 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Variational Approach for Mitigating Entity Bias in Relation Extraction Mensah, Samuel Kochkina, Elena Magomere, Jabez Sain, Joy Prakash Kaur, Simerjot Smiley, Charese Computation and Language Artificial Intelligence 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 relation extraction datasets across general, financial, and biomedical domains, in both indomain (original test sets) and out-of-domain (modified test sets with type-constrained entity replacements) settings. Our approach offers a robust, interpretable, and theoretically grounded methodology. |
| title | A Variational Approach for Mitigating Entity Bias in Relation Extraction |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2506.11381 |