Neural Nets for NLP 2017 - Attention

Explore a comprehensive lecture on attention mechanisms in neural networks for natural language processing. Delve into the fundamentals of attention, including what to attend to, improvements to attention, and specialized attention varieties. Examine a case study on the "Attention is All You Need" paper. Learn about various attention score functions, hierarchical structures, multiple sources, and intra-attention. Discover advanced concepts such as coverage, bidirectional training, supervised training, and monotonic attention. Investigate convolutional attention and multi-headed attention, concluding with a summary of the "Transformer" model and useful attention tricks. Access accompanying slides and code examples to enhance your understanding of these crucial NLP concepts.

Intro
Sentence Representations
Basic Idea (Bahdanau et al. 2015)
Calculating Attention (1)
A Graphical Example
Attention Score Functions (2)
Input Sentence
Previously Generated Things
Various Modalities
Hierarchical Structures (Yang et al. 2016)
Multiple Sources
Intra-Attention / Self Attention (Cheng et al. 2016) • Each element in the sentence attends to other elements + context sensitive encodings!
Coverage
Incorporating Markov Properties (Cohn et al. 2015)
Bidirectional Training (Cohn et al. 2015)
Supervised Training (Mi et al. 2016)
Attention is not Alignment! (Koehn and Knowles 2017) • Attention is often blurred
Monotonic Attention (e.g. Yu et al. 2016)
Convolutional Attention (Allamanis et al. 2016)
Multi-headed Attention
Summary of the "Transformer" (Vaswani et al. 2017)
Attention Tricks