Cross-lingual Transfer Learning Research in natural language processing (NLP) has seen many advances over the recent years, from word embeddings to pretrained language models. However, most of these approaches rely on large labelled datasets, which has constrained their success to languages where such data is plentiful. In this talk, I will give an overview of approaches that transfer knowledge across languages and enable us to scale NLP models to more of the world's 7,000 languages. I will cover the spectrum of recent cross-lingual transfer approaches, from word embeddings to deep pretrained models. The talk will conclude with a discussion of the cutting-edge of learning such representations, their limitations, and future directions.