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Summary of design dimensions
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Neural Nets for NLP - Multi-task, Multi-lingual Learning
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- 1 Intro
- 2 Remember, Neural Nets are Feature Extractors!
- 3 Types of Learning
- 4 Plethora of Tasks in NLP
- 5 Rule of Thumb 1: Multitask to Increase Data
- 6 Rule of Thumb 2
- 7 Standard Multi-task Learning
- 8 Examples of Pre-training Encoders . Common to pre-train encoders for downstream tasks, common to use
- 9 Regularization for Pre-training (e.g. Barone et al. 2017) Pre-training relies on the fact that we won't move too far from the
- 10 Selective Parameter Adaptation Sometimes it is better to adapt only some of the parameters
- 11 Soft Parameter Tying
- 12 Supervised Domain Adaptation through Feature Augmentation
- 13 Unsupervised Learning through Feature Matching
- 14 Multilingual Structured Prediction/ Multilingual Outputs • Things are harder when predicting a sequence of actions (parsing) or words (MT) in different languages
- 15 Multi-lingual Sequence-to- sequence Models
- 16 Types of Multi-tasking
- 17 Multiple Annotation Standards
- 18 Different Layers for Different
- 19 Summary of design dimensions
