Meta-learning (ML) has emerged as a promising direction in learning models under constrained resource settings like few-shot learning. The popular approaches for ML either learn a generalizable initial model or a generic parametric optimizer through episodic training. The former approaches leverage the knowledge from a batch of tasks to learn an optimal prior. In this work, we study the importance of tasks in a batch for ML. We hypothesize that the common assumption in batch episodic training where each task in a batch has an equal contribution to learning an optimal meta-model need not be true. We propose to weight the tasks in a batch according to their ``importance" in improving the meta-model's learning. To this end, we introduce a training curriculum, called task attended meta-training, to weight the tasks in a batch. The task attention is a standalone unit and can be integrated with any batch episodic training regimen. The comparisons of the task-attended ML models with their non-task-attended counterparts on complex datasets like miniImageNet, FC100 and tieredImageNet validate its effectiveness.