Towards Robust Few-Shot Relation Classification: Incorporating Relation Description with Agreement (2025)

Abstract: Few-shot relation classification aims to recognize the relation between two mentioned entities, with the help of only a few support samples. However, a few samples tend to be limited for tackling unlimited queries. If a query cannot find references from the support samples, it is defined as none-of-the-above (NOTA). Previous works mainly focus on how to distinguish N+1 categories, including N known relations and one NOTA class, to accurately recognize relations. However, the robustness towards various NOTA rates, i.e. the proportion of NOTA among queries, is under investigation. In this paper, we target the robustness and propose a simple but effective framework. Specifically, we introduce relation descriptions as external knowledge to enhance the model’s comprehension of the relation semantics. Moreover, we further promote robustness by proposing a novel agreement loss. It is designed for seeking decision consistency between the instance-level decision, i.e. support samples, and relation-level decision, i.e. relation descriptions. Extensive experimental results demonstrate that the proposed framework outperforms strong baselines while being robust against various NOTA rates. The code is released on GitHub at https://github.com/Pisces-29/RoFRC.

## introduction few-shot relation classification (fsrc) is a popular task in the information extraction field. it aims to predict the relation between two entities in a sentence, by only referencing a few known samples. previous fsrc models are usually trained on a collection of meta-tasks sampled from the training corpus @xcite . a meta-task can also be called an "episode", which contains a support set and a query set. the support set comprises n non-overlapping categories, and each category consists of k instances. each query is classified * corresponding author. query set figure 1 : an example for a 2-way 1-shot episode with 50% nota rate. the support set involves 2 classes, each containing only 1 instance. the head entity and tail entity are marked in red and blue, respectively. the query set contains two instances that need to be predicted. with the help of the support samples. by learning meta-knowledge from these episodic training tasks, the model can quickly adapt to classifying new relations with only a few examples. as an example shown in figure 1 , the support set provides two known relations, i.e. "country" and "participant". the first query instance, which expresses a "country" relation, can be easily classified by referring to the support set. but this is an ideal situation. however, in real-world applications, the fsrc model often encounters query instances with relations that are not present in the support set. for the second query, its relation between "steve jobs" and "1955" is "born in". it is unable to find a reference from the support set. @xcite define such relation as none-of-the-above (nota) and first propose that the fsrc model should involve n + 1 categories, including n known relations and an additional nota category. by introducing the nota relation, the model can more robustly handle the open-ended nature of real-world relation extraction tasks, where novel relations are likely to be encountered. to address this problem, previous works mainly aim to distinguish n + 1 classes by introducing learnable vectors @xcite , formulating a multiple-choice problem @xcite , or gen-erating nota representations @xcite . while these approaches have shown promising results, their robustness towards different nota rates remains an open question. most existing works evaluate their models using fixed nota rates, such as 30% or 50% @xcite . this means that the proportion of query instances with the nota relation is kept constant during training and testing. however, this assumption may not hold in practical applications, where the nota rate can vary significantly across different groups of queries. the sensitivity of fsrc models to nota rates has important implications for their real-world applicability. to deal with this issue, we propose a simple but effective architecture, named robust few-shot relation classification (rofrc), which improves not only the model's robustness in varying nota rate scenarios but also its overall performance. initially, except for the instance-level decision (a.k.a. using support samples), we introduce relation descriptions as external knowledge for the relationlevel decision to assist the model in accurately understanding the semantics of relations. for example, the description of the "country" relation is "sovereign state of this item". assuming we train a model with the 50% nota rate scenario in figure 1 , it faces an unseen relation "province". by leveraging the description "a territory governed as a unit of a country or empire", our method gains an in-depth comprehension of semantics and reduces the negative effects of the increase or decrease of the nota proportion in the query set. additionally, differing from previous work that incorporates relation description for encoding representations @xcite , we leverage it into the decision stage. to further promote robustness, we propose a novel agreement loss to seek decision consistency between the instance-level and relation-level decisions. such consistency forces the proposed rofrc to comprehend relation semantics better. our method is evaluated on the popular fewrel @xcite and nyt-25 dataset, and extensive experiments show its effectiveness and robustness in handling varying nota rate scenarios, outperforming state-of-the-art fewshot relation classification approaches. in summary, the main contributions of our work are as follows: ## related work previous research @xcite in relation classification typically train models on labeled datasets with a predetermined number of classes, limiting their capability to handle unseen relations. to address this issue, @xcite propose the use of few-shot relation classification tasks and introduce a comprehensive supervised dataset called fewrel. @xcite propose a simple and efficient framework that incorporates relation description information, proving to be a significant improvement over hcrp @xcite . borchert et al. (2024) present a novel method that jointly leverages contrastive learning and diverse sentence representations. dong et al. (2024) propose a method that incorporates relation-aware prompt templates and multi-level contrastive learning to improve prototype representations and mitigate relation confusion. sun and chen (2025) propose a local-to-global optimization framework that enhances prototype learning through entity-relation alignment, local contrastive learning, and a local adaptive focal loss. in terms of incorporating external information, wang et al. (2020) propose the incorporation of additional relative position and syntactic relation information. yang et al. (2020) utilize a collaborative attention mechanism to integrate text descriptions of relations and entities. furthermore, yang et al. (2021) introduce the utilization of inherent entity concepts to provide clues for relation classification. gao et al. (2019) identify the nota challenge in the few-shot relation classification task, which simulates real-world conditions. they propose a solution to the nota problem by utilizing a sentencepair model. sabo et al. (2021) represent the prototype of the nota relation in a prototypical network by using multiple trainable vectors. liu et al. sentence encoder r 2 r 1 relation descriptions prototypes query instances relation descriptions support instances query instances similarity prediction class 1 class 2 nota similarity ⊕ decision blender training objective cross entropy loss agreement loss 0 0.2 0.4 0.6 0.8 1 p (1) 1 p (2) 1 0 0.2 0.4 0.6 0.8 1 p (1) 1 p (2) 1 r na c 1 c 2 q 1 q 2 q 3 figure 2: the overview of the rofrc framework. refers to the addition of the two similarities in a corresponding relation. in this example, the support set is 2-way. both relation descriptions and instances are encoded using the same encoder. in addition, there is a special nota relation description in the relation set. (2022a) propose a multiple-choice network with the pretraining fine-tuning paradigm for the fewshot relation classification task, in which an n + 1 option for handling nota challenge. wu et al. (2023) design a nota detection model based on instance density for classifying nota instances after the prototypical network. additionally, liu et al. (2022b) are the first to introduce relation descriptions to address the nota challenge. their novel rectification module fuses relation descriptions with prototypes to generate rectified prototypes and a pseudo-nota prototype for (n + 1)way classification. compared to prm, our proposed method aims to enable the model to learn how to refer to support instances and fully comprehend the actual meaning of relations. therefore, rofrc exhibits robustness in addressing the nota challenge. ## conclusion this paper introduces the rofrc architecture, a novel approach specifically designed to address the challenge of nota in the few-shot relation classification task. the primary objective is to enhance the model's performance and robustness across varying rates of nota. the proposed architecture includes a decision blender that efficiently performs few-shot relation classification and adapts to the nota scenario by merging instance-level and relation-level decisions. to ensure consistency, an agreement loss function is introduced to weigh the agreement between instance-level and relationlevel decisions. experimental results on a popular dataset fewrel confirm the superior performance of our rofrc architecture.