Joint entity and relation extraction is the fundamental task of information extraction, consisting of two subtasks: named entity recognition and relation extraction. However, most existing joint extraction methods suffer from issues of feature confusion or inadequate interaction between the two subtasks. Addressing these challenges, in this work, we propose a Co-Attention network for joint entity and Relation Extraction (CARE). Our approach includes adopting a parallel encoding strategy to learn separate representations for each subtask, aiming to avoid feature overlap or confusion. At the core of our approach is the co-attention module that captures two-way interaction between the two subtasks, allowing the model to leverage entity information for relation prediction and vice versa, thus promoting mutual enhancement. Through extensive experiments on three benchmark datasets for joint entity and relation extraction (NYT, WebNLG, and SciERC), we demonstrate that our proposed model outperforms existing baseline models. Our code will be available at https://github.com/kwj0x7f/CARE.

翻译：联合实体与关系抽取是信息提取的基础任务，包含命名实体识别和关系抽取两个子任务。然而，现有大多数联合抽取方法存在特征混淆或两个子任务间交互不足的问题。针对这些挑战，本文提出一种面向联合实体与关系抽取的协同注意力网络（CARE）。我们的方法采用并行编码策略，为每个子任务学习独立的表示，旨在避免特征重叠或混淆。该方法的核心是捕捉两个子任务间双向交互的协同注意力模块，使模型能够利用实体信息进行关系预测，反之亦然，从而促进相互增强。通过在三个联合实体与关系抽取基准数据集（NYT、WebNLG和SciERC）上的广泛实验，我们证明所提模型优于现有基线模型。我们的代码将发布于https://github.com/kwj0x7f/CARE。