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.
where α,β,γ is the translation of a,b,c. This way, ais in close proximity to α, bis fairly close to β, and so on, a fact that makes it easy for SGD to “establish communication”between the input and the output. We found this simple data transformation to greatly improve the performance of the LSTM. 3 Experiments
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