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

• URL: http://arxiv.org/abs/2306.02175v1
• Date: Sat, 3 Jun 2023 18:38:02 GMT
• Title: TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation
• Authors: Shuo Lei, Xuchao Zhang, Jianfeng He, Fanglan Chen, Chang-Tien Lu
• Abstract summary: We propose a novel Task-Adaptive Reference Transformation (TART) network to enhance the generalization. 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.
• Score: 23.02986307143718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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