Few Shot and Meta Learning for NLP

Meta learning and few shot learning approaches have shown promising results in computer vision, with low-resouce tasks. Recently they have gained attention in natural language processing tasks such as machine translation and text classification. In this lecture we cover how meta learning approaches such as MAML and metric learning approaches such as matching and prototypical networks are used, combined with episodic training to improve the performance on low resource NLP tasks.