Explore advanced techniques for modeling long sequences in natural language processing through this comprehensive lecture from CMU's Advanced NLP course. Delve into extracting features from extended text and tackling document processing tasks. Learn about various transformer architectures including Transformer XL, Compressive Transformers, and Sparse Transformers. Examine adaptive span and sparse span approaches, as well as the Reformer model. Investigate low rank approximation and sparse attention methods. Gain insights into evaluation techniques and other relevant methodologies. Conclude with an overview of coreference models, including mention pair models and their components.