Transcription of Sequence to Sequence Learning with Neural Networks
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Sequence to Sequence Learning with Neural Networks
[ ] 14 Dec 2014 Sequence to Sequence Learningwith Neural NetworksIlya V. Neural Networks (DNNs) are powerful models that have achieved excel-lent performance on difficult Learning tasks. Although DNNswork well wheneverlarge labeled training sets are available, they cannot be used to map sequences tosequences. In this paper, we present a general end-to-end approach to sequencelearning that makes minimal assumptions on the Sequence structure. Our methoduses a multilayered Long Short-Term Memory (LSTM) to map theinput sequenceto a vector of a fixed dimensionality, and then another deep LSTM to decode thetarget Sequence from the vector.
Short-Term Memory (LSTM) architecture [16] can solve general sequence to sequence problems. The idea is to use one LSTM to read the input sequence, one timestep at a time, to obtain large fixed-dimensional vector representation, and then to use another LSTM to extract the output sequence from that vector (fig. 1).
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