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 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.
Abstract (translated)
缓解实体偏差是关系抽取（RE）中的一个关键挑战，因为在关系抽取中，模型往往会过度依赖于实体信息，导致泛化能力较差。本文提出了一种通过调整变分信息瓶颈（VIB）框架来解决这一问题的新型方法。我们的方法在压缩特定于实体的信息的同时保留任务相关的特征。在通用、金融和生物医学领域的关系抽取数据集上，无论是在原测试集（in-domain）还是修改后的测试集（out-of-domain，包含类型约束下的实体替换）设置下，本方法均达到了最先进的性能水平。我们的方法提供了一种稳健、可解释且理论基础扎实的方法论。
URL
https://arxiv.org/abs/2506.11381
