The Encoder-Decoder architecture with recurrent neural networks has become an effective and standard approach for both neural machine translation (NMT) and sequence-to-sequence (seq2seq) prediction in general.
The key benefits of the approach are the ability to train a single end-to-end model directly on source and target sentences and the ability to handle variable length input and output sequences of text.
RNN Decoder is sequence to sequence mapping model. In combination with Encoder takes a sequence as input and generates another sequence as output. In many to many sequence learning setups, the input is a sequence of vectors and the output is another sequence of vectors.
Eg: Speech Wave to Text, Language Translation etc
In General We use Normal RNN for one to one sequence learning setup.
Eg: Next Character Prediction.