Explore latent variable models in neural networks for natural language processing in this comprehensive lecture from CMU's Neural Networks for NLP course. Delve into the differences between generative and discriminative models, as well as deterministic and random variables. Learn about Variational Autoencoders (VAEs) and their applications in NLP, including techniques for handling discrete latent variables. Discover the challenges in training latent variable models and strategies to overcome them, such as KL divergence annealing and weakening the decoder. Examine methods for dealing with discrete latent variables, including enumeration, sampling, and reparameterization techniques. Access accompanying slides and code examples to reinforce your understanding of these advanced concepts in neural network-based NLP.