Don’t Miss the Labels: Label-semantic Augmented Meta-Learner for Few-Shot Text Classification
Qiaoyang Luo, Lingqiao Liu, Yuhao Lin, Wei Zhang · 2021
Increasing studies leverage pre-trained language models and meta-learning frameworks to solve few-shot text classification problems.Most of the current studies focus on building a meta-learner from the information of input texts but ignore abundant semantic information beneath class labels.In this work, we show that class-label information can be utilized for extracting more discriminative feature representation of the input text from a pretrained language model like BERT, and can achieve a performance boost when the samples are scarce.Building on top of this discovery, we propose a framework called Labelsemantic augmented meta-learner (LaSAML) to make full use of label semantics.We systematically investigate various factors in this framework and show that it can be plugged into the existing few-shot text classification system.Through extensive experiments, we demonstrate that the few-shot text classification system upgraded by LaSAML can lead to significant performance improvement over its original counterparts.