A processing unit can extract salient semantics to model knowledge carryover, from one turn to the next, in multi-turn conversations. Architecture described herein can use the end-to-end memory networks to encode inputs, e.g., utterances, with intents and slots, which can be stored as embeddings in memory, and in decoding the architecture can exploit latent contextual information from memory, e.g., demographic context, visual context, semantic context, etc. e.g., via an attention model, to leverage previously stored semantics for semantic parsing, e.g., for joint intent prediction and slot tagging. In examples, architecture is configured to build an end-to-end memory network model for contextual, e.g., multi-turn, language understanding, to apply the end-to-end memory network model to multiple turns of conversational input; and to fill slots for output of contextual, e.g., multi-turn, language understanding of the conversational input. The neural network can be learned using backpropagation from output to input using gradient descent optimization.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: one or more processing unit(s); one or more computer-readable media coupled to one or more of the processing unit(s), the one or more computer readable media having thereon one or more modules of computer-executable instructions to configure a computer to perform operations comprising: building an end-to-end memory network model for contextual, e.g., multi-turn, language understanding; applying the end-to-end memory network model to multiple turns of conversational input; and filling slots for output of contextual language understanding of the conversational input. 2. A system as claim 1 recites, wherein: the conversational input includes an utterance; a store of inputs includes a history of utterances; and a history vector includes a representation of an utterance from the store of inputs. 3. A system as claim 1 recites, the one or more computer readable media having thereon one or more modules of computer-executable instructions to configure the computer to perform operations further comprising comparing a vector representation of a current input from a turn of the multiple turns of conversational input to a history vector, the history vector including a representation of inputs read from a store of inputs. 4. A system as claim 3 recites, wherein a neural (NN) architecture is configured to read from the store of inputs for the contextual language understanding. 5. A system as claim 4 recites, wherein the NN includes a recurrent neural network (RNN). 6. A system as claim 3 recites, the one or more computer readable media having thereon one or more modules of computer-executable instructions to configure the computer to perform operations further comprising: calculating a softmax for the vector representation of the current input to individual vector representations of inputs from the store of inputs. 7. A system as claim 1 recites, the one or more computer readable media having thereon one or more modules of computer-executable instructions to configure the computer to perform operations further comprising calculating an attention vector for context associated with the conversational input. 8. A system as claim 7 recites, the one or more computer readable media having thereon one or more modules of computer-executable instructions to configure the computer to perform operations further comprising operating the end-to-end memory network model including the attention vector to perform end-to-end semantic parsing of the multiple turns of conversational input. 9. A method comprising: comparing a vector representation of a current input to a history vector in an end-to-end memory network, the history vector including a representation of inputs read from a store of inputs; calculating an attention vector for context associated with the current input; and using the history vector and the vector representation of the current input to parse the current input; and operating the model including the attention vector to perform end-to-end semantic parsing of inputs. 10. A method as claim 9 recites, wherein a neural (NN) architecture reads from the store of inputs. 11. A method as claim 9 recites, wherein the store of inputs includes utterances. 12. A method as claim 9 recites, wherein: the current input includes an utterance; the store of inputs includes utterances; and the history vector includes a representation of an utterance from the store of inputs. 13. A method as claim 9 recites, wherein end-to-end semantic parsing of inputs includes end-to-end tagging of inputs. 14. A method as claim 9 recites, further comprising applying a recurrent neural network (RNN) to fill slots to output from the end-to-end semantic parsing of inputs. 15. A method as claim 9 recites, wherein end-to-end semantic parsing of inputs includes end-to-end semantic parsing of inputs from a conversation. 16. A method as claim 9 recites, further comprising: calculating a softmax for the vector representation of the current input to individual vector representations of inputs from the store of inputs. 17. A system comprising: one or more processing unit(s); one or more computer-readable media coupled to one or more of the processing unit(s), the one or more computer readable media including: a neural network (NN) architecture that reads from a store of inputs; a training engine configured to generate an end-to-end trained model from input-output pairs by: comparing a vector representation of a current input to a history vector, the history vector including a representation of inputs from the store of inputs; estimating an attention vector based at least in part on input and context vectors; and using the history vector and the vector representation of the current input to parse the current input; and an operation engine configured to operate the model including the attention vector to perform end-to-end semantic parsing of inputs. 18. A system as claim 17 recites, wherein the NN includes a recurrent neural network (RNN). 19. A system as claim 17 recites, wherein the store of inputs includes utterances. 20. A system as claim 17 recites, wherein estimating the attention vector includes calculating an attention vector for context associated with the input.