From google institution;

1. Before this, DNN cannot be used to map sequences to sequences. In this paper, we propose a sequence learning that makes minimal assumptions on the sequence structure.

use lstm to map the input sequence to a vector of a fixed dimensionality;

input sequence-----> lstm -----> vector -----> decoder(lstm) -----> target sequence.

translation task;

Limitation: Despite their flexibility and power, DNNs can only be applied to problems whose inputs and targets can be sensibly encoded with vectors of fixed dimensionality.

Sequential problem: speech recognition and machine translation.

Before: DNNs require that the dimensionality of the inputs and outputs is known and fixed.

Problem: sequence to sequence problems.

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