TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation

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 stateof-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. Our code is available at https: //github.com/slei109/TART Class Testing Sample Task 1: Support class: 1,2,3,4 Task 2: Support class: 1,2,3,5 MLADA Ours MLADA Ours 1 Animal photos of the week: baby tiger goes for a swim. 1 1 1 1 2 Twitter helps confirm X-shaped bulge at Center of Milky Way. 4 2 2 2 3 Toronto van attack suspect's Facebook post praised misogynist mass killer. 4 3 2 3 4 Apple just solved one of the iphone's most harmful features. 2 4 --5 Apple fritter season is here, and so are the recipes you'll need.