Neural Nets for NLP 2019 - Unsupervised and Semi-supervised Learning of Structure
Explore a comprehensive lecture on unsupervised and semi-supervised learning of structure in neural networks for natural language processing. Delve into the differences between learning features and learning structure, examine various semi-supervised and unsupervised learning methods, and understand key design decisions for unsupervised models. Gain insights into practical examples of unsupervised learning, including hidden Markov models with Gaussian emissions, normalizing flow, and gated convolution. Investigate the challenges of learning latent structure and compare phrase structure to dependency structure. Learn about innovative approaches such as learning dependency heads with attention and reinforcement learning techniques for structure learning in NLP tasks.