Explore search-based structured prediction in natural language processing through this comprehensive lecture from CMU's Neural Networks for NLP course. Delve into the Structured Perceptron algorithm, examining its simplicity in training non-probabilistic global models. Contrast perceptron and global normalization approaches, and investigate structured training techniques. Learn about cost-augmented hinge loss and its application to sequence modeling. Gain insights into addressing exposure bias with simple remedies. Understand the intricacies of structured max-margin objectives and their role in NLP tasks. Discover how corrupt training data impacts model performance and explore strategies to mitigate its effects.