Abstract: Handling challenging real-world problems in Natural Language Processing (NLP) include tackling class imbalance, problem complexity and the lack of availability of enough labeled data for training. Thanks to the recent advancements in deep transfer learning in NLP, we have been able to make rapid strides in not only tackling these problems but also leverage these models for diverse downstream NLP tasks. The intent of this session is to journey through the recent advancements in deep transfer learning for NLP by taking a look at various state-of-the-art models and methodologies including: - Pre-trained embedding models - Universal Embedding models - Contextual Embedding models - Transformers