Learn about transition-based parsing, shift-reduce parsing with feed-forward networks, stack LSTMs, and semantic parsing in this comprehensive lecture from CMU's Neural Networks for NLP course. Explore the fundamentals of linguistic structure generation, including common types and semantic parsing tasks. Dive into shift-reduce examples, classification techniques, feature extraction, and the importance of tree structures in natural language processing. Examine recursive neural networks and their applications. Understand the core research questions for improving models, focusing on adding inductive biases to better capture program structures. Gain insights into supervised learning of semantic parsers and the key considerations in designing decoders that align with program structures.