Methods › Sequential › Sequence To Sequence Models
Sequence To Sequence Models
The archive attaches this collection's text per method and the copies differ: 2 distinct texts across 3 of the 16 methods here. All are shown, most-carried first (a tie goes to the text carrying Papers with Code's collection boilerplate, then to the longer text); no vote is taken between them.
Text 1, carried by 2 of 16 methods:
Transformers are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.
Text 2, carried by 1 of 16 methods:
Language Models are models for predicting the next word or character in a document. Below you can find a continuously updating list of language models.
Methods
All 16 methods in this collection, most-tagged first. Year is the archive's introduced_year; the archive stores 2000 when it has none, shown here as “–”. Papers counts distinct papers the archive tags with the method. Click a heading to sort.
| BART | – | 1,642 |
| T5 | – | 708 |
| Seq2Seq Sequence to Sequence | – | 700 |
| Pointer Network | – | 105 |
| Tacotron | – | 65 |
| GTS Goal-Driven Tree-Structured Neural Model | – | 46 |
| ESIM Enhanced Sequential Inference Model | – | 15 |
| ClariNet | – | 7 |
| Graph2Tree Graph-to-Tree MWP Solver | – | 4 |
| HBMP Hierarchical BiLSTM Max Pooling | – | 3 |
| GAN-TTS | – | 2 |
| ResBiLSTM Residual Bidirectional Long Short-Term Memory | – | 2 |
| Cyclic Transformer | – | 1 |
| LayoutReader | – | 1 |
| WaveTTS | – | 1 |
| mBARTHez | – | 1 |