We explore Few-Shot Learning (FSL) for Relation Classification (RC). Focusing on the realistic scenario of FSL, in which a test instance might not belong to any of the target categories (none-of-the-above, aka NOTA), we first revisit the recent popular dataset structure for FSL, pointing out its unrealistic data distribution. To remedy this, we propose a novel methodology for deriving more realistic few-shot test data from available datasets for supervised RC, and apply it to the TACRED dataset. This yields a new challenging benchmark for FSL RC, on which state of the art models show poor performance. Next, we analyze classification schemes within the popular embedding-based nearest-neighbor approach for FSL, with respect to constraints they impose on the embedding space. Triggered by this analysis we propose a novel classification scheme, in which the NOTA category is represented as learned vectors, shown empirically to be an appealing option for FSL.

研究使用少样本学习(FSL)处理关系分类（RC），并提出了一种新方法来从可用的监督 RC 数据集中产生更现实的 FSL 测试数据，针对嵌入式最近邻方法的分类方案进行了分析，并提出了一种新的分类方案，其中 NOTA 类别被表示为学习向量，并展示了该方法对 FSL 的吸引力。

重新审视少样本关系分类：评估数据和分类方案