Abstract
Few-shot Continual Relation Extraction is a crucial challenge for enabling AI systems to identify and adapt to evolving relationships in dynamic real-world domains. Traditional memory-based approaches often overfit to limited samples, failing to reinforce old knowledge, with the scarcity of data in few-shot scenarios further exacerbating these issues by hindering effective data augmentation in the latent space. In this paper, we propose a novel retrieval-based solution, starting with a large language model to generate descriptions for each relation. From these descriptions, we introduce a bi-encoder retrieval training paradigm to enrich both sample and class representation learning. Leveraging these enhanced representations, we design a retrieval-based prediction method where each sample "retrieves" the best fitting relation via a reciprocal rank fusion score that integrates both relation description vectors and class prototypes. Extensive experiments on multiple datasets demonstrate that our method significantly advances the state-of-the-art by maintaining robust performance across sequential tasks, effectively addressing catastrophic forgetting.
Abstract (translated)
少量样本下的持续关系抽取（Few-shot Continual Relation Extraction）是使AI系统能够识别并适应动态现实世界领域中不断变化的关系的关键挑战。传统的基于记忆的方法通常会过度拟合于有限的样本，无法强化旧知识，在少量样本场景中数据稀疏性进一步加剧了这些问题，阻碍了潜在空间中的有效数据增强。 在本文中，我们提出了一种新颖的检索解决方案，首先使用大型语言模型生成每个关系的描述。从这些描述出发，我们引入了一个双向编码器检索训练范式，以丰富样例和类表示学习。利用这些增强后的表示形式，我们设计了一种基于检索的预测方法，其中每个样本通过整合关系描述向量和类原型的互反秩融合分数来“检索”最合适的关联。 在多个数据集上的广泛实验表明，我们的方法显著提升了现有技术，在连续任务中保持了强大的性能，并有效解决了灾难性遗忘问题。
URL
https://arxiv.org/abs/2502.20596
