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

arXiv: https://arxiv.org/abs/2308.12531 · HTML: https://mlsanity.com/paper/2308.12531/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 24 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019)
- [Entity-Relation Extraction as Multi-Turn Question Answering](https://mlsanity.com/paper/1905.05529/) (2019)
- [A General Framework for Information Extraction using Dynamic Span Graphs](https://mlsanity.com/paper/1904.03296/) (2019)
- [Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders](https://mlsanity.com/paper/2010.03851/) (2020)
- [TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking](https://mlsanity.com/paper/2010.13415/) (2020)
- [Joint entity recognition and relation extraction as a multi-head selection problem](https://mlsanity.com/paper/1804.07847/) (2018)
- [A Partition Filter Network for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2108.12202/) (2021)
- [UniRE: A Unified Label Space for Entity Relation Extraction](https://mlsanity.com/paper/2107.04292/) (2021)

## Built on by (most cited first, 1 of 1)

- [Knowledge Augmented Entity and Relation Extraction for Legal Documents with Hypergraph Neural Network](https://mlsanity.com/paper/2602.08289/) (2026)

## Abstract (arXiv)

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.