Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating representations of input sequences. One of the methods includes obtaining an input sequence, the input sequence comprising a plurality of inputs arranged according to an input order; processing the input sequence using a first long short term memory (LSTM) neural network to convert the input sequence into an alternative representation for the input sequence; and processing the alternative representation for the input sequence using a second LSTM neural network to generate a target sequence for the input sequence, the target sequence comprising a plurality of outputs arranged according to an output order.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method performed by one or more computers, the method comprising: obtaining an input sequence, the input sequence comprising a plurality of inputs arranged according to an input order; processing the input sequence using a first long short term memory (LSTM) neural network to convert the input sequence into an alternative representation for the input sequence; and processing the alternative representation for the input sequence using a second LSTM neural network to generate a target sequence for the input sequence, the target sequence comprising a plurality of outputs arranged according to an output order. 2. The method of claim 1, wherein the input sequence is a variable length input sequence. 3. The method of claim 2, wherein the alternative representation is a vector of fixed dimensionality. 4. The method of claim 1, wherein processing the input sequence comprises: adding an end-of-sentence token to the end of the input sequence to generate a modified input sequence; and processing the modified input sequence using the first LSTM neural network. 5. The method of claim 1, wherein processing the alternative representation for the input sequence using the second LSTM neural network comprises initializing a hidden state of the second LSTM neural network to the alternative representation for the input sequence. 6. The method of claim 5, wherein processing the alternative representation for the input sequence using the second LSTM neural network comprises: processing the alternative representation for the input sequence using the second LSTM neural network to generate a respective sequence score for each of a set of possible target sequences; and selecting a possible target sequence having a highest sequence score as the target sequence for the input sequence. 7. The method of claim 6, wherein the set of possible target sequences comprises possible target sequences of varying lengths. 8. The method of claim 6, wherein processing the alternative representation for the input sequence using the second LSTM neural network comprises: processing the alternative representation using the second LSTM neural network using a left to right beam search decoding. 9. The method of claim 1, further comprising: training the first LSTM neural network and the second LSTM neural network using Stochastic Gradient Descent. 10. The method of claim 1, wherein the input sequence is a sequence of words in a first language and the target sequence is a translation of the sequence of words into a second language. 11. The method of claim 1, wherein the input sequence is a sequence of words and the target sequence is an autoencoding of the input sequence. 12. The method of claim 1, wherein the input sequence is a sequence of graphemes and the target sequence is a phoneme representation of the sequence of graphemes. 13. A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising: obtaining an input sequence, the input sequence comprising a plurality of inputs arranged according to an input order; processing the input sequence using a first long short term memory (LSTM) neural network to convert the input sequence into an alternative representation for the input sequence; and processing the alternative representation for the input sequence using a second LSTM neural network to generate a target sequence for the input sequence, the target sequence comprising a plurality of outputs arranged according to an output order. 14. The system of claim 13, wherein the input sequence is a variable length input sequence. 15. The system of claim 14, wherein the alternative representation is a vector of fixed dimensionality. 16. The system of claim 13, wherein processing the alternative representation for the input sequence using the second LSTM neural network comprises initializing a hidden state of the second LSTM neural network to the alternative representation for the input sequence. 17. The system of claim 16, wherein processing the alternative representation for the input sequence using the second LSTM neural network comprises: processing the alternative representation for the input sequence using the second LSTM neural network to generate a respective sequence score for each of a set of possible target sequences; and selecting a possible target sequence having a highest sequence score as the target sequence for the input sequence. 18. The system of claim 17, wherein the set of possible target sequences comprises possible target sequences of varying lengths. 19. The system of claim 17, wherein processing the alternative representation for the input sequence using the second LSTM neural network comprises: processing the alternative representation using the second LSTM neural network using a left to right beam search decoding. 20. A computer program product encoded on one or more non-transitory storage media, the computer program product comprising instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising: obtaining an input sequence, the input sequence comprising a plurality of inputs arranged according to an input order; processing the input sequence using a first long short term memory (LSTM) neural network to convert the input sequence into an alternative representation for the input sequence; and processing the alternative representation for the input sequence using a second LSTM neural network to generate a target sequence for the input sequence, the target sequence comprising a plurality of outputs arranged according to an output order.