Neural Nets for NLP 2020 - Machine Reading with Neural Nets
Explore machine reading with neural networks in this comprehensive lecture from CMU's Neural Networks for NLP course. Dive into various machine reading datasets, methods for encoding context and multi-hop reasoning, and important caveats about dataset biases. Learn about multiple-choice questions, span selection, and cloze tasks in question answering formats. Examine case studies on popular datasets, including bAbl, CNN/Daily Mail, and Natural Questions. Discover basic models for document attention, such as Attentive Reader and Bidirectional Attention Flow. Investigate multi-step reasoning techniques, including memory networks and question decomposition. Gain insights into combining traditional computational semantics with neural approaches for tasks like numerical calculation and solving word problems.