Improving Continual Few-shot Relation Extraction through Relational Knowledge Distillation and Prototype Augmentation (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to Continual Relation Extraction (CRE) are limited in handling the rapid emergence of new relations in real-world scenarios. |
| Approach: | They propose a framework that integrates prototype-based data augmentation and relational knowledge distillation to solve the problem of Continual Few-shot Relation Extraction (CFRE). |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on the FewRel and TACRED datasets. |
Similar Papers
Serial Contrastive Knowledge Distillation for Continual Few-shot Relation Extraction (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing models for few-shot relation extraction (RE) are not suitable for continual few-sshot RE. |
| Approach: | They propose a new model to train a model for new relations with few labeled training data. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets. |
Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation (2022.acl-long)
Copied to clipboard
| Challenge: | Existing continual relation learning methods rely on labeled training data for learning new tasks, which can be expensive and time-consuming. |
| Approach: | They propose a method that embeds space regularization and data augmentation to learn relational patterns with very few labeled data while avoiding catastrophic forgetting of previous task knowledge. |
| Outcome: | The proposed method outperforms existing state-of-the-art methods in CFRL task settings. |
Mutual-pairing Data Augmentation for Fewshot Continual Relation Extraction (2025.naacl-long)
Copied to clipboard
Nguyen Hoang Anh, Quyen Tran, Thanh Xuan Nguyen, Nguyen Thi Ngoc Diep, Linh Ngo Van, Thien Huu Nguyen, Trung Le
| Challenge: | Existing methods for Few-shot Continual Relation Extraction struggle with catastrophic forgetting and overfitting. |
| Approach: | They propose a method that transforms single input sentences into complex texts by integrating old and new data. |
| Outcome: | The proposed method sharpens model focus and improves model performance . it also uncovers fascinating behaviors of Sharpness-Aware Minimization (SAM) in Few-shot Continual Learning. |
Dynamic-prototype Contrastive Fine-tuning for Continual Few-shot Relation Extraction with Unseen Relation Detection (2025.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to learn relations from labeled data overlook task interference in continual learning and memory requirements for different relations. |
| Approach: | They propose a framework to learn new relations from limited labeled data while preserving knowledge about previously learned relations. |
| Outcome: | The proposed framework is more practical and comprehensive for real-world scenarios. |
Continual Few-shot Relation Extraction via Adaptive Gradient Correction and Knowledge Decomposition (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to learn new relations with limited samples neglect the instability of embeddings in the process of different task training, which leads to catastrophic forgetting. |
| Approach: | They propose a method to analyze catastrophic forgetting by limiting embedding instability . they propose to decompose knowledge into general and task-related knowledge . |
| Outcome: | The proposed method outperforms the state-of-the-art model and improves the following degree of embeddings. |
Consistent Prototype Learning for Few-Shot Continual Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for few-shot continual relation extraction are overfitting memory samples, resulting in insufficient activation of old relations and limited ability to handle confusion of similar classes. |
| Approach: | They propose a few-shot continual relation extraction task that uses memory-enhanced modules to train a model on incrementally few-shot data to avoid forgetting old relations. |
| Outcome: | The proposed method outperforms existing methods on two commonly-used datasets. |
Mitigating Non-Representative Prototypes and Representation Bias in Few-Shot Continual Relation Extraction (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for few-shot continual relation extraction (FCRE) face two main challenges: non-representative prototypes and representation bias. |
| Approach: | They propose to use General Orthogonal Frame to create robust class prototypes . they also utilize label description representations as global class representatives . |
| Outcome: | The proposed method outperforms state-of-the-art methods on well-known benchmarks on well known FCRE benchmarks. |
Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods to learn new relations with limited labeled data are prone to catastrophic forgetting and overfitting. |
| Approach: | They propose a framework that uses prompts to acquire more generalized knowledge . they propose CFRE to continuously learn new relations while retaining knowledge of old ones . |
| Outcome: | The proposed method outperforms state-of-the-art methods by a large margin and significantly mitigates catastrophic forgetting and overfitting in low-resource scenarios. |
Preserving Generalization of Language models in Few-shot Continual Relation Extraction (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for Few-shot Continual Relations Extraction (FCRE) are limited in labeled training data and models must learn from a few new samples to solve new tasks. |
| Approach: | They propose a method that leverages often-discarded language model heads to integrate knowledge from new relations with limited labeled data while avoiding catastrophic forgetting. |
| Outcome: | The proposed method circumvents catastrophic forgetting and preserves prior knowledge from pre-trained backbones while maintaining accuracy of existing classifications. |
Distilling Causal Effect of Data in Continual Few-shot Relation Learning (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for learning relational patterns from data are prone to catastrophic forgetting issues due to limited number of samples and continual training mode. |
| Approach: | They propose a unified causal framework for CFRL to restore causal effects from old data . they establish two additional causal paths from old to predictions by colliding with old data separately in the old feature space. |
| Outcome: | The proposed method is superior to existing state-of-the-art methods in CFRL task settings. |