According to this paper (https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf), [the] RNN can easily map sequences to sequences whenever the alignment between the inputs the outputs is known ahead of time.I know that RNN's are used for sequence processing, which theoretically can include sequence-to-sequence mapping, but why do they need to know the alignment between input and output sequences beforehand?How does using an encoder and decoder setup solve the problem of variable alignment? If the above limitation applies, then the alignment between the input sequence and the intermediate encoding is fixed, and the alignment between the intermediate encoding and the output sequence is fixed. In this case, wouldn't that mean that the alignment of input to output would still be fixed?
Understanding encoder-decoder sequence-to-sequence model
neural networks;natural language processing