Meta-learning algorithms aim at learning good initializations that can be useful for fine-tuning on various tasks with minimal training data, which makes them appealing alternatives to multi-task learning.
we investigate the recently proposed model-agnostic metalearning algorithm (MAML) (Finn et al., 2017) and its variants, namely first-order MAML and Reptile (Nichol et al., 2018), for NLU tasks.