Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks

@inproceedings{Bansal2020LearningTF,
  title={Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks},
  author={Trapit Bansal and Rishikesh Jha and Andrew McCallum},
  booktitle={COLING},
  year={2020}
}
Pre-trained transformer models have shown enormous success in improving performance on several downstream tasks. However, fine-tuning on a new task still requires large amounts of task-specific labeled data to achieve good performance. We consider this problem of learning to generalize to new tasks, with a few examples, as a meta-learning problem. While meta-learning has shown tremendous progress in recent years, its application is still limited to simulated problems or problems with limited… 
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