Methods › General › Self-Training Methods › STraTA
Self-Training with Task Augmentation
STraTA
Introduced by Tu Vu et al. in STraTA: Self-Training with Task Augmentation for Better Few-shot Learning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
STraTA, or Self-Training with Task Augmentation, is a self-training approach that builds on two key ideas for effective leverage of unlabeled data. First, STraTA uses task augmentation, a technique that synthesizes a large amount of data for auxiliary-task fine-tuning from target-task unlabeling 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.
In task augmentation, we train an NLI data generation model and use it to synthesize a large amount of in-domain NLI training data for each given target task, which is then used for auxiliary (intermediate) fine-tuning. The self-training algorithm iteratively learns a better model using a concatenation of labeled and pseudo-labeled examples. At each iteration, we always start with the auxiliary-task model produced by task augmentation and train on a broad distribution of pseudo-labeled data.
Papers archive 2025-07-28
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STraTA: Self-Training with Task Augmentation for Better Few-shot Learning 13 Sep 2021 · 1 repository · arXiv:2109.06270
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Few-Shot Learning | 1 |
| Few-Shot NLI | 1 |
| SST-2 | 1 |
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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