GRADUAL : Granularity-aware Dual Prototype Learning for Better Few-Shot Relation Extraction (2024)

Abstract: Recent studies have shown that fusing text labels and context sentences is an effective method for learning prototype representations in few-shot relation extraction. However, the **inconsistency of prototype representations** across different few-shot tasks persists due to different context sentences for the same relation, even with the integration of text labels into prototype representations. Conversely, the text label for each relation is unique and consistent, 1)which prompts us to propose a **dual prototype learning method**. Unlike previous methods that only construct support-based prototypes, we additionally construct label-based prototypes. Furthermore, we introduce a graph-based prototype adjustment module to construct topological information between support-based and label-based prototypes, thereby generating a more effective similarity measure through a simple linear combination. In addition, relations of different granularities have different distribution widths in the same semantic space, the **imbalanced distribution in the semantic space** leads to a lack of comparability among relations. To create a more discriminative semantic space, 2)we propose a **granularity-aware prototype learning method** that unifies the distribution width of relations, making relations of different granularities have similar distribution widths. Experimental results on two public benchmark datasets show that our proposed methods achieve state-of-the-art performance in few-shot relation classification.

## introduction relation extraction (re) is a fundamental task in natural language processing (nlp) that aims to extract semantic relations between entities from * equal contribution. † correspondence author. water polo first appeared in england in the late 19th century. ## problem formulation we follow the typical few-shot task setting, namely the n -way-k-shot setup @xcite . in each few-shot task, each instance c includes context sentence tokens @xmath0, . our model follows meta-learning setup during training @xcite , which consists of two stages: the meta-training stage and the metatesting stage. the datasets for the meta-training and meta-testing stages are respectively referred to as the meta-training dataset d train and the metatesting dataset d test , with no overlapping relations between them. the meta-training dataset is divided into support instances s and query instances q to enable the model to gain transferable knowledge and the ability to "learn to learn". unlike traditional model training methods, fsl, after integrating the concept of meta-learning, treats the meta-task as the training unit. similarity measure by combining different inputs (similarity measures between the support-based prototype and query instances, and between the label-based prototype and query instances) and the corresponding topological information through a simple linear combination. (3) a granularity-aware layer in the granularity-aware prototype learning method. the granularity-aware layer uses the area size between the support-based prototype, the labelbased prototype, and the query instance to measure relations of different granularities in each few-shot task. ## conclusions in this work, we introduce gradual, an innovative method designed for few-shot relation extraction (fsre). gradual enhances the performance and generalization capability of the model by generating more consistent prototype representations for each relation and constructing a more discriminative semantic space for different relations. firstly, we first combine the metrics of supportbased prototypes and label-based prototypes into a new similarity metric by constructing topological information between prototypes, which helps the model to get high-quality prototype representations. secondly, to enhance the expressive power of the semantic space, we measure the granularity of different relations by the area size between the prototype and the query instance. by transforming the imbalanced distribution of different granularity relations in the same semantic space into a similar distribution, we construct a more discriminative semantic space. extensive experiments have demonstrated that our gradual significantly outperforms previous works. in future research, we plan to apply gradual to more information extraction tasks across various domains and examine its robustness.