Exploring an Effective Approach to Acquire Meta‐Knowledge for Low‐Resource Few‐Shot Relation Extraction

ABSTRACT Few‐shot relation extraction has attracted significant attention due to its potential to identify new relations when training samples are scarce. Previous studies demonstrate that meta‐learning techniques can significantly enhance models' adaptability in few‐shot scenarios. Most existing meta‐learning methods generalize to unseen relations by constructing effective prototype representations for each relation. However, meta‐learning techniques often require a large amount of labeled data during the meta‐training stage, which contradicts the initial purpose of addressing the issue of insufficient labeled data. Current methods usually fail to model both intra‐class similarity and inter‐class difference sufficiently, resulting in fuzzy class prototypes when distinguishing between similar but slightly different relations. Therefore, we propose a novel task, low‐resource few‐shot relation extraction (LR‐FSRE), that explores minimizing the usage of labeled data while maximizing the acquisition of task meta‐knowledge, and we design a novel graph‐based adaptive discrimination network that optimizes relation prototypes in both tasks. Specifically, we effectively represent class prototypes using the topological information between instance and relation‐level representation. Then, the relation discrimination network considers the difference between similar classes to further improve the recognition ability of relation classes. Finally, a debiased optimization strategy is employed to optimize the learning processes of task‐general and task‐specific knowledge. Experimental results show that our proposed framework outperforms the state‐of‐the‐art methods in FSRE and LR‐FSRE tasks, and achieves significant improvement in accuracy across challenging cross‐domain and similarity relation discrimination scenarios.