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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.

The Connectionist Sequence Classification is another popular ... This is by far the best result achieved by direct translation with large neural net-works. For comparison,the BLEU score of an SMT baseline on this dataset is 33.30 [29]. The 34.81

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