Seq2seq, or encoder-decoder (see Sequence to Sequence Learning with Neural Networks at https://arxiv.org/abs/1409.3215), models use RNNs in a way that's especially suited for solving tasks with indirect many-to-many relationships between the input and the output. A similar model was also proposed in another pioneering paper, Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (go to https://arxiv.org/abs/1406.1078 for more information). The following is a diagram of the seq2seq model. The input sequence [A, B, C, <EOS>] is decoded into the output sequence [W, X, Y, Z, <EOS>]:
A seq2seq model case by https://arxiv.org/abs/1409.3215
The model consists of two parts: an encoder and a decoder. Here's how the inference part works:
- The encoder is an RNN. The original paper uses LSTM, but GRU...
