STraTA: Self-Training with Task Augmentation for Better Few-shot Learning

• URL: http://arxiv.org/abs/2109.06270v1
• Date: Mon, 13 Sep 2021 19:14:01 GMT
• Title: STraTA: Self-Training with Task Augmentation for Better Few-shot Learning
• Authors: Tu Vu, Minh-Thang Luong, Quoc V. Le, Grady Simon, Mohit Iyyer
• Abstract summary: We propose STraTA, which stands for Self-Training with Task Augmentation. Our experiments demonstrate that STraTA can substantially improve sample efficiency across 12 few-shot benchmarks. Our analyses reveal that task augmentation and self-training are both complementary and independently effective.
• Score: 77.04780470527432
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite their recent successes in tackling many NLP tasks, large-scale pre-trained language models do not perform as well in few-shot settings where only a handful of training examples are available. To address this shortcoming, we propose STraTA, which stands for Self-Training with Task Augmentation, an approach that builds on two key ideas for effective leverage of unlabeled data. First, STraTA uses task augmentation, a novel technique that synthesizes a large amount of data for auxiliary-task fine-tuning from target-task unlabeled texts. Second, STraTA performs self-training by further fine-tuning the strong base model created by task augmentation on a broad distribution of pseudo-labeled data. Our experiments demonstrate that STraTA can substantially improve sample efficiency across 12 few-shot benchmarks. Remarkably, on the SST-2 sentiment dataset, STraTA, with only 8 training examples per class, achieves comparable results to standard fine-tuning with 67K training examples. Our analyses reveal that task augmentation and self-training are both complementary and independently effective.

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