Distinct Label Representations for Few-Shot Text Classification

Few-shot text classification aims to classify inputs whose label has only a few examples. Previous studies overlooked the semantic relevance between label representations. Therefore, they are easily confused by labels that are semantically relevant. To address this problem, we propose a method that generates distinct label representations that embed information specific to each label. Our method is widely applicable to conventional few-shot classification models. Experimental results show that our method significantly improved the performance of few-shot text classification across models and datasets.