Explore structured prediction with local independence assumptions in this lecture from CMU's Neural Networks for NLP course. Delve into the fundamentals of structured prediction, understand the importance of local independence assumptions, and learn about Conditional Random Fields (CRFs). Discover how to model interactions in output for consistency, examine sequence labeling with recurrent decoders, and understand the concepts of teacher forcing and exposure bias. Investigate models with local dependencies, comparing local and global normalization. Gain insights into BILSTM-CRF for sequence labeling, CRF training and decoding, and the intricacies of forward calculation. Examine the partition function, training details, and generalized dynamic programming models, including decomposition structures and featurization techniques.