# DP-CRE: Continual Relation Extraction via Decoupled Contrastive Learning and Memory Structure Preservation

arXiv: https://arxiv.org/abs/2403.02718 · HTML: https://mlsanity.com/paper/2403.02718/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 27 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [PathNet: Evolution Channels Gradient Descent in Super Neural Networks](https://mlsanity.com/paper/1701.08734/) (2017)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning](https://mlsanity.com/paper/2103.13885/) (2021)
- [Sentence Embedding Alignment for Lifelong Relation Extraction](https://mlsanity.com/paper/1903.02588/) (2019)
- [Curriculum-Meta Learning for Order-Robust Continual Relation Extraction](https://mlsanity.com/paper/2101.01926/) (2021)
- [Consistent Representation Learning for Continual Relation Extraction](https://mlsanity.com/paper/2203.02721/) (2022)
- [Learning Adaptive Embedding Considering Incremental Class](https://mlsanity.com/paper/2008.13351/) (2020)
- [Improving Continual Relation Extraction through Prototypical Contrastive Learning](https://mlsanity.com/paper/2210.04513/) (2022)
- [Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation](https://mlsanity.com/paper/2210.04497/) (2022)
- [Improving Continual Relation Extraction by Distinguishing Analogous Semantics](https://mlsanity.com/paper/2305.06620/) (2023)
- [Less is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approach](https://mlsanity.com/paper/2209.00243/) (2022)
- [Enhancing Continual Relation Extraction via Classifier Decomposition](https://mlsanity.com/paper/2305.04636/) (2023)

## Built on by (most cited first, 2 of 2)

- [Learning Wisdom from Errors: Promoting LLM's Continual Relation Learning through Exploiting Error Cases](https://mlsanity.com/paper/2508.12031/) (2025)
- [WAVE++: Capturing Within-Task Variance for Continual Relation Extraction with Adaptive Prompting](https://mlsanity.com/paper/2505.13944/) (2025)

## Abstract (arXiv)

Continuous Relation Extraction (CRE) aims to incrementally learn relation knowledge from a non-stationary stream of data. Since the introduction of new relational tasks can overshadow previously learned information, catastrophic forgetting becomes a significant challenge in this domain. Current replay-based training paradigms prioritize all data uniformly and train memory samples through multiple rounds, which would result in overfitting old tasks and pronounced bias towards new tasks because of the imbalances of the replay set. To handle the problem, we introduce the DecouPled CRE (DP-CRE) framework that decouples the process of prior information preservation and new knowledge acquisition. This framework examines alterations in the embedding space as new relation classes emerge, distinctly managing the preservation and acquisition of knowledge. Extensive experiments show that DP-CRE significantly outperforms other CRE baselines across two datasets.