Before revision:  Specifically, we develop self-training and meta-learning techniques for few-shot training of neural sequence taggers , namely MetaST . While self-training serves as an effective mechanism to learn from large amounts of unlabeled data -- meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels.
After revision:  Specifically, we develop self-training and meta-learning techniques for few-shot training of neural sequence taggers with few labels . While self-training serves as an effective mechanism to learn from large amounts of unlabeled data -- meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels.