Neural Nets for NLP 2019 - Document Level Models

Neural Nets for NLP 2019 - Document Level Models

Graham Neubig via YouTube Direct link

Intro

1 of 19

1 of 19

Intro

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Neural Nets for NLP 2019 - Document Level Models

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  1. 1 Intro
  2. 2 Some Connections to Tasks over Documents
  3. 3 Document Level Language Modeling
  4. 4 What Context to Incorporate?
  5. 5 How to Evaluate Document Coherence Models?
  6. 6 Mention(Noun Phrase) Detection
  7. 7 Components of a Coreference Model . Like a traditional machine learning model
  8. 8 Coreference Models:Instances
  9. 9 Mention Pair Models
  10. 10 Entity Models
  11. 11 Advantages of Neural Network Models for Coreference
  12. 12 Coreference Resolution w/ Entity- Level Distributed Representations
  13. 13 End-to-End Neural Coreference (Span Model)
  14. 14 End-to-End Neural Coreference (Coreference Model)
  15. 15 Using Coreference in Neural Models
  16. 16 Document Problems: Discourse Parsing
  17. 17 Shift-reduce Parsing Discourse Structure Parsing w/ Distributed Representations (Ji and Eisenstein 2014) . Shift-reduce parser with features from 2 stack elements and queue element
  18. 18 Discourse Parsing w/ Attention- based Hierarchical Neural Networks
  19. 19 Uses of Discourse Structure in Neural Models

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