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Few-shot transfer often shows substantial gain over zero-shot transfer , which is a practically useful trade-off between fully supervised and unsupervised learning approaches for multilingual pretrained model-based systems. This paper explores various strategies for selecting data for annotation that can result in a better few-shot transfer. The proposed approaches rely on multiple measures such as data entropy using n-gram language model, predictive entropy, and gradient embedding. We proposearXiv:2206.15010v1 fatcat:js53kwvhyjewzkcnvyhb5v5x54