  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.
