CARE: Co-Attention Network for Joint Entity and Relation Extraction

TL;DR

This paper introduces CARE, a co-attention network that improves joint entity and relation extraction by enabling mutual enhancement between subtasks through a co-attention mechanism, outperforming existing models on benchmark datasets.

Contribution

The paper proposes a novel co-attention network with parallel encoding for joint entity and relation extraction, addressing feature confusion and subtask interaction issues.

Findings

Outperforms baseline models on NYT, WebNLG, SciERC datasets.

Effective mutual enhancement between entity and relation extraction.

Co-attention module captures two-way interactions, improving accuracy.

Abstract

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.