STraTA Self Training with Task Augmentation for Better Few shot Learning

A super cool method that improve model accuracy drastically without using additional task-specific annotated data Connect Linkedin Twitter email edwindeeplearning@ 0:00 - Intro 3:07 - Task augmentation self-training 5:13 - Intermediate fine-tuning 6:09 - Task augmentation setup 10:49 - Overgeneration & filtering 12:17 - Self-training algorithm 16:15 - Results 20:23 - My thoughts STraTA: Self-Training with Task Augmentation for Better Few-shot Learning 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 effect