# Improving Continual Relation Extraction through Prototypical Contrastive Learning

arXiv: https://arxiv.org/abs/2210.04513 · HTML: https://mlsanity.com/paper/2210.04513/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2022.

## Built on by (most cited first, 14 of 14)

- [Towards Lifelong Learning of Large Language Models: A Survey](https://mlsanity.com/paper/2406.06391/) (2024)
- [Rationale-Enhanced Language Models are Better Continual Relation Learners](https://mlsanity.com/paper/2310.06547/) (2023)
- [Improving Continual Relation Extraction by Distinguishing Analogous Semantics](https://mlsanity.com/paper/2305.06620/) (2023)
- [LW2G: Learning Whether to Grow for Prompt-based Continual Learning](https://mlsanity.com/paper/2409.18860/) (2024)
- [Enhancing Continual Relation Extraction via Classifier Decomposition](https://mlsanity.com/paper/2305.04636/) (2023)
- [InfoCL: Alleviating Catastrophic Forgetting in Continual Text Classification from An Information Theoretic Perspective](https://mlsanity.com/paper/2310.06362/) (2023)
- [Serial Contrastive Knowledge Distillation for Continual Few-shot Relation Extraction](https://mlsanity.com/paper/2305.06616/) (2023)
- [Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors](https://mlsanity.com/paper/2402.15713/) (2024)
- [Learning Wisdom from Errors: Promoting LLM's Continual Relation Learning through Exploiting Error Cases](https://mlsanity.com/paper/2508.12031/) (2025)
- [DP-CRE: Continual Relation Extraction via Decoupled Contrastive Learning and Memory Structure Preservation](https://mlsanity.com/paper/2403.02718/) (2024)
- [RepCL: Exploring Effective Representation for Continual Text Classification](https://mlsanity.com/paper/2305.07289/) (2023)
- [Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting](https://mlsanity.com/paper/2305.13533/) (2023)
- [DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit](https://mlsanity.com/paper/2310.09501/) (2023)
- [Few-shot Continual Relation Extraction via Open Information Extraction](https://mlsanity.com/paper/2502.16648/) (2025)

## Abstract (arXiv)

Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks. In order to alleviate this critical problem for enhanced CRE performance, we propose a novel Continual Relation Extraction framework with Contrastive Learning, namely CRECL, which is built with a classification network and a prototypical contrastive network to achieve the incremental-class learning of CRE. Specifically, in the contrastive network a given instance is contrasted with the prototype of each candidate relations stored in the memory module. Such contrastive learning scheme ensures the data distributions of all tasks more distinguishable, so as to alleviate the catastrophic forgetting further. Our experiment results not only demonstrate our CRECL's advantage over the state-of-the-art baselines on two public datasets, but also verify the effectiveness of CRECL's contrastive learning on improving CRE performance.