Uncertainty-aware Self-training for Few-shot Text Classification

Meta Review

This work presents a novel approach of integrating uncertainty into self-training to obtain strong results on text classification with very few labels. The work compares against a strong set of baselines and has extensive ablations. The reviewers agreed the response answered most of their concerns. The work could be improved with more diverse low-resource setups and by improving the clarity of the writing.