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.
Source: STraTA: Self-Training with Task Augmentation for Better Few-shot LearningPaper | Code | Results | Date | Stars |
---|
Component | Type |
|
---|---|---|
🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |