Sequence To Sequence Models
Sequence to Sequence
Introduced by Sutskever et al. in Sequence to Sequence Learning with Neural NetworksSeq2Seq, or Sequence To Sequence, is a model used in sequence prediction tasks, such as language modelling and machine translation. The idea is to use one LSTM, the encoder, to read the input sequence one timestep at a time, to obtain a large fixed dimensional vector representation (a context vector), and then to use another LSTM, the decoder, to extract the output sequence from that vector. The second LSTM is essentially a recurrent neural network language model except that it is conditioned on the input sequence.
(Note that this page refers to the original seq2seq not general sequence-to-sequence models)
Source: Sequence to Sequence Learning with Neural Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Decoder | 88 | 7.86% |
| Sentence | 72 | 6.43% |
| Machine Translation | 66 | 5.89% |
| Translation | 62 | 5.54% |
| Text Generation | 46 | 4.11% |
| Language Modelling | 45 | 4.02% |
| Semantic Parsing | 40 | 3.57% |
| Question Answering | 25 | 2.23% |
| Abstractive Text Summarization | 21 | 1.88% |
LSTM
