Mutual-pairing Data Augmentation for Fewshot Continual Relation Extraction (2025)

Abstract: Data scarcity is a major challenge in Few-shot Continual Relation Extraction (FCRE), where models must learn new relations from limited data while retaining past knowledge. Current methods, restricted by minimal data streams, struggle with catastrophic forgetting and overfitting. To overcome this, we introduce a novel *data augmentation strategy* that transforms single input sentences into complex texts by integrating both old and new data. Our approach sharpens model focus, enabling precise identification of word relationships based on specified relation types. By embedding adversarial training effects and leveraging new training perspectives through special objective functions, our method enhances model performance significantly. Additionally, we explore Sharpness-Aware Minimization (SAM) in Few-shot Continual Learning. Our extensive experiments uncover fascinating behaviors of SAM across tasks and offer valuable insights for future research in this dynamic field.

## introduction relation extraction (re) is a problem in information extraction that seeks to extract semantic relationships between pairs of entities in a sentence. for example, given the sentence "kamala harris and donald trump were political opponents in the us presidential election", the relation to be extracted between "kamala harris" and "donald trump" is "political opponents". in more in-depth research on real-world scenarios for re, few-shot continual relation extraction (fcre) @xcite @xcite is a challenging setting that has recently attracted a lot of attention. in this setting, the models need to continuously capture semantic information of new emerging relations from a small and limited amount data, while avoiding forgetting knowledge of previously learned ones. therefore, the two main concerns in this scenario involve dealing with catastrophic forgetting @xcite @xcite @xcite and overfitting of fcre models. recent work @xcite @xcite tackles these problems by leveraging memory-based approaches inspired by conventional continual learning methods @xcite @xcite . these methods intentionally save a small amount of data samples from old tasks and propose various strategies to enhance the models' abilities to distinguish relation representations. however, a significant issue is that previous models are often fine-tuned with only one sample from each old class and a few samples from new classes, which leads to forgetting and overfitting in the challenging fcre scenario. to address these challenges, this work explores a new approach to generate more diverse and meaningful learning samples for fcre. by leveraging strong guidance, our method relies solely on the available datasets, avoiding the need for external resources. specifically, we introduce a data augmentation method that combines both old and new data to create new training samples, transforming single input sentences into complex texts and weaving together knowledge from all tasks so far. starting with an original sentence containing a pair of entities, we append an arbitrary sentence, either before or after it. this augmentation does not change the relation between the two entities in each original sentence. thus, our approach not only expands the training data but also guides models to focus on identifying word relationships based on the specified relation type, rather than on grammatical or other semantic associations. more specifically, our method encourages the attention mechanism to pri-oritize words in the original sentence related to the entity pair, while minimizing attention to the added noisy sentences. especially, we propose special objective functions to inherently deliver adversarial training effects and novel perspectives during training, significantly boosting model performance-particularly in preventing forgetting and reducing overfitting. furthermore, in the effort to explore solutions for the overfitting problem in few-shot continual learning models, we conducted extensive experiments on the application of sharpness-aware minimization (sam) @xcite . specifically, we assessed the effectiveness of sam in improving the latest state-of-the-art methods for both @xcite and few-shot continual event detection (fced) @xcite @xcite . the results yielded surprising and intriguing insights, showing that sam is not always a suitable solution for few-shot continual scenarios, which we believe will be valuable for future research. in summary, the key contributions of this work are as follows: ## proposed method in this section, we introduce our novel data augmentation technique and two special objective functions in section 3.1. we then discuss interesting findings related to applying sharpness aware minimization (sam) -a method for enhancing model generalizability, in the challenging scenario of fcre, where models are prone to overfitting (section 3.2). ## experiments in this section, we present experimental results demonstrating the effectiveness of our proposed data augmentation strategy.