Curriculum Meta-Learning for Few-shot Classification

We propose an adaptation of the curriculum training framework, applicable to state-of-the-art meta learning techniques for few-shot classification. Curriculum-based training popularly attempts to mimic human learning by progressively increasing the training complexity to enable incremental concept learning. As the meta-learner's goal is learning how to learn from as few samples as possible, the exact number of those samples (i.e. the size of the support set) arises as a natural proxy of a given task's difficulty. We define a simple yet novel curriculum schedule that begins with a larger support size and progressively reduces it throughout training to eventually match the desired shot-size of the test setup. This proposed method boosts the learning efficiency as well as the generalization capability. Our experiments with the MAML algorithm on two few-shot image classification tasks show significant gains with the curriculum training framework. Ablation studies corroborate the independence of our proposed method from the model architecture as well as the meta-learning hyperparameters

Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Tagging

Many modern Neural Machine Translation (NMT) systems are trained on nonhomogeneous datasets with several distinct dimensions of variation (e.g. domain, source, generation method, style, etc.). We describe and empirically evaluate multidimensional tagging (MDT), a simple yet effective method for passing sentence-level information to the model. Our human and BLEU evaluation results show that MDT can be applied to the problem of multi-domain adaptation and significantly reduce training costs without sacrificing the translation quality on any of the constituent domains.