An encoder and decoder for translating sequential data into a fixed dimensional vector are created by applying an encoding-trainer input vector set as input to an encoding neural network to generate an encoding-trainer output vector set. One vector of the encoding-trainer output vector set is selected and a decoding-trainer input vector set is generated from it. A decoding neural network is trained by applying the generated decoding-trainer input vector set to the decoding neural network. The encoder and decoder can be used in implementations processing sequential data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for translating between a first context and a second context comprising: encoding source data of the first context using an encoding neural network to create an encoded input fixed dimensional vector representation of the source data; applying the encoded input fixed dimensional vector as input to a translator neural network trained to generate an output encoded fixed dimensional vector representation of target data of the second context, wherein the first context and second context differ; and decoding the output encoded fixed dimensional vector representation using a decoding neural network to recreate the target data. 2. The method of claim 1, wherein the encoding neural network is trained by applying a plurality of sequences of training input vectors from a first data corpus of the first context as input to the encoding neural network. 3. The method of claim 1, wherein encoding the source data using the encoding neural network includes: generating a sequence of input vectors corresponding the source data, applying the sequence of input vectors to the encoding network; and, selecting one vector from the output of the encoding neural network. 4. The method of claim 3, wherein generating the sequence of input vectors corresponding the source data includes: generating sequential indices for the source data, and mapping the sequential indices to vectors in an embedding layer. 5. The method of claim 1, wherein the decoding neural network is trained by applying a plurality of encoded fixed dimensional vectors representing sequential data encoded from a second data corpus of the second context as input to the decoding neural network. 6. The method of claim 5, wherein applying the plurality of encoded fixed dimensional vectors representing sequential data from the second data corpus includes: creating training vector sets for each of the plurality of encoded fixed dimensional vectors. 7. The method of claim 6, wherein creating the training vector sets for each of the plurality of encoded fixed dimensional vectors includes concatenating the each of the plurality of encoded fixed dimensional vectors with a beginning of sequence vector and a subset of vectors used to encode the each of the plurality of encoded fixed dimensional vectors. 8. The method of claim 1, wherein decoding the output encoded fixed dimensional vector representation using the decoding neural network includes concatenating the output encoded fixed dimensional vector representation with a beginning of sequence vector used when training the decoding neural network. 9. The method of claim 8, wherein decoding the output encoded fixed dimensional vector representation using the decoding neural network further includes concatenating the output encoded fixed dimensional vector representation with one or more output vectors of the decoding neural network. 10. A method for creating an encoder and a decoder comprising: training an encoding neural network by applying an encoding-trainer input vector set as input to the encoding neural network to generate an encoding-trainer output vector set so that the encoding-trainer output vector set is an additive inverse of the encoding-trainer input vector set, wherein the encoding-trainer input vector set corresponds to sequential data; determining a selected encoding vector by selecting one vector of the encoding-trainer output vector set; generating a decoding-trainer input vector set by concatenating the selected encoding vector with a beginning of sequence vector and concatenating the selected encoding vector with each non-selected vector of the encoding-training output vector set; and training a decoding neural network by applying the generated decoding-trainer input vector set to the decoding neural network so that the decoding neural network outputs an additive inverse of the encoding-trainer input vector set. 11. The method of claim 10 wherein the encoding neural network includes a plurality of hidden layers. 12. The method of claim 11 wherein the encoding neural network is trained such that connections between nodes of consecutive hidden layers of the plurality of hidden layers have the same weight. 13. The method of claim 10 wherein the decoding neural network includes a plurality of hidden layers. 14. The method of claim 13 wherein the decoding neural network includes is trained such that connections between nodes of consecutive hidden layers of the plurality of hidden layers have the same weight. 15. A method for decoding an encoded fixed dimensional vector into a sequence of data, the method comprising: performing a number of iterations equal to a set size, wherein each iteration comprises: concatenating the encoded fixed dimensional vector with a beginning of sequence vector to configure a first vector of an ordered input vector set of set size vectors, concatenating the encoded fixed dimensional vector with an ordered output vector set determined during a previous iteration of the number of iterations to configure subsequent vectors of the ordered input vector set, the subsequent vectors occurring in the ordered input vector set after the first vector of the ordered input vector set, and applying the ordered input vector set to a decoding neural network trained concurrently with an encoding neural network uses to encode the encoded fixed dimensional vector; and, determining, once the number of iterations have been performed, the sequence of data based on the ordered output vector set of the last iteration. 16. The method of claim 15, wherein concatenating the encoded fixed dimensional vector with the ordered output vector set determined during the previous iteration includes concatenating the encoded fixed dimensional vector with a vector of the ordered output vector set located at a position in the sequence equal to the number of iterations performed plus one. 17. The method of claim 15 wherein the decoding neural network and encoding neural network were trained using an embedding layer. 18. The method of claim 17 wherein determining the sequence of data is further based on correlating the ordered output vector set of the last iteration with the embedding layer. 19. The method of claim 15 wherein the decoding neural network is trained by creating decoding training vector sets based on a vector output from the encoding neural network during training of the encoding neural network. 20. The method of claim 19, wherein creating the training vector sets includes concatenating the vector output from the encoding neural network with a beginning of sequence vector and input vector sets provided as input to the encoding neural network during training.