Covers: theory of Shrinking the Search Space for Seq2Seq Models

- What is the theory behind shrinking the search space for seq2seq models?

Read pages 1-8 of the paper.

Ben Peters and Andre F. T. Martins

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Contributors

- Objectives
- Help the reader understand the paper in question, as well as recent trends in seq2seq modeling, decoders, and loss functions
- Potential Use Cases
- Needing to improve a seq2seq model for downstream tasks like translation, inflection, and pronunciation
- Who is This For ?
- INTERMEDIATENLP practitioners working on seq2seq models

Click on each of the following **annotated items** to see details.

OTHER 1. What happened in Natural language generation decoders in 2019?

- What is the context for the paper?
- What developments have there been in decoders in NLP in 2019?

5 minutes

ARTICLE 2. What is Label Smoothing?

- What is the label smoothing discussed in the paper that is the subject of this RECIPE?

20 minutes

PAPER 3. Learning with Fenchel-Young Losses

- What are the Fenchel-Young Losses mentioned in the paper that is the subject of this RECIPE?

5 minutes

PAPER 4. On NMT Search Errors and Model Errors: Cat Got Your Tongue?

- What is the cat got your tongue problem in neural machine translation?

5 minutes

PAPER 5. Sparse Sequence-to-Sequence Models

- What is the background to the newer paper by these authors that is the subject of this RECIPE?
- What are sparse and dense sequence to sequence models?

10 minutes

PAPER 6. Smoothing and Shrinking the Sparse Seq2Seq Search Space

- What is the theory behind shrinking the search space for seq2seq models?

45 minutes

REPO 7. Smoothing and Shrinking the Sparse Seq2Seq Search Space

- How was label smoothing implemented in the paper that is the subject of this RECIPE?

20 minutes

OTHER 8. Morphological Inflection

- What is morphological inflection?

1 minutes

PAPER 9. Grapheme-to-Phoneme Conversion with Convolutional Neural Networks

- What is grapheme-to-phoneme (G2P) conversion?

1 minutes

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