TART: Improved Few-shot Text Classification Using Task-Adaptive
  Reference Transformation
Shuo Lei Xuchao Zhang Jianfeng He Fanglan Chen Chang-Tien Lu
Meta-learning has emerged as a trending technique to tackle few-shot text
classification and achieve state-of-the-art performance. However, the
performance of existing approaches heavily depends on the inter-class variance
of the support set. As a result, it can perform well on tasks when the
semantics of sampled classes are distinct while failing to differentiate
classes with similar semantics. In this paper, we propose a novel Task-Adaptive
Reference Transformation (TART) network, aiming to enhance the generalization
by transforming the class prototypes to per-class fixed reference points in
task-adaptive metric spaces. To further maximize divergence between transformed
prototypes in task-adaptive metric spaces, TART introduces a discriminative
reference regularization among transformed prototypes. Extensive experiments
are conducted on four benchmark datasets and our method demonstrates clear
superiority over the state-of-the-art models in all the datasets. In
particular, our model surpasses the state-of-the-art method by 7.4% and 5.4% in
1-shot and 5-shot classification on the 20 Newsgroups dataset, respectively.