A tutorial given by Chris Dyer, Yoav Goldberg, and Graham Neubig at EMNLP 2016 in Austin. The tutorial covers the basic of neural networks for NLP, and how to implement a variety of networks simply and efficiently in the DyNet toolkit.

• Slides, part 1: Basics

  • Computation graphs and their construction
  • Neural networks in DyNet
  • Recurrent neural networks
  • Minibatching
  • Adding new differentiable functions

• Slides, part 2: Case studies in NLP

  • Tagging with bidirectional RNNs and character-based embeddings
  • Transition-based dependency parsing
  • Structured prediction meets deep learning