A processing unit can train a model as a joint multi-domain recurrent neural network (JRNN), such as a bi-directional recurrent neural network (bRNN) and/or a recurrent neural network with long-short term memory (RNN-LSTM) for spoken language understanding (SLU). The processing unit can use the trained model to, e.g., jointly model slot filling, intent determination, and domain classification. The joint multi-domain model described herein can estimate a complete semantic frame per query, and the joint multi-domain model enables multi-task deep learning leveraging the data from multiple domains. The joint multi-domain recurrent neural network (JRNN) can leverage semantic intents (such as, finding or identifying, e.g., a domain specific goal) and slots (such as, dates, times, locations, subjects, etc.) across multiple domains.
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 a multi-domain model using a recurrent neural network (RNN) with semantic parsing from a plurality of queries; applying the multi-domain model to a query of the plurality of queries; and operating the multi-domain model across a plurality of domains. 2. A system as claim 1 recites, wherein the multi-domain model comprises a joint recurrent neural network (JRNN). 3. A system as claim 1 recites, wherein the individual query of the plurality of queries represents a verbal utterance. 4. A system as claim 1 recites, wherein the RNN represents a bi-directional recurrent neural network. 5. A system as claim 1 recites, wherein an RNN is activated by long-short term memory (LSTM) from the plurality of queries. 6. A system as claim 1 recites, wherein applying the multi-domain model includes: identifying the query from the plurality of queries; and estimating a domain, an intent, and a slot for the query of the plurality of queries. 7. A system as claim 1 recites, wherein operating the multi-domain model across a plurality of domains includes performing semantic parsing across the plurality of domains. 8. A method comprising: building a multi-domain model using a recurrent neural network (RNN) with semantic parsing from a plurality of queries; applying the multi-domain model to a query of the plurality of queries; and operating the multi-domain model across a plurality of domains. 9. A method as claim 8 recites, wherein the multi-domain model comprises a joint recurrent neural network (JRNN). 10. A method as claim 8 recites, wherein the individual query of the plurality of queries represents a verbal utterance. 11. A method as claim 8 recites, wherein the RNN represents a bi-directional recurrent neural network. 12. A method as claim 8 recites, wherein an RNN is activated by long-short term memory (LSTM) from the plurality of queries. 13. A method as claim 8 recites, wherein applying the multi-domain model comprises identifying the query from the plurality of queries. 14. A method as claim 8 recites, wherein applying the multi-domain model comprises estimating a domain, an intent, and a slot for the query of the plurality of queries. 15. A method as claim 8 recites, wherein operating the multi-domain model across a plurality of domains includes performing semantic parsing across the plurality of domains. 16. A computer-readable medium having encoded thereon computer-executable instructions to, upon execution, configure a computer to perform a method as claim 8 recites. 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 multi-domain model comprising a joint recurrent neural network (JRNN) built for semantic parsing; a training engine configured to generate a trained multi-domain model by training the multi-domain model by: identifying a plurality of queries; and estimating a domain, an intent, and a slot for an individual query of the plurality of queries; and an operation engine configured to operate the trained multi-domain model to perform semantic parsing for the individual query of the plurality of queries across a plurality of domains. 18. A system as claim 17 recites, wherein the RNN represents a bi-directional RNN. 19. A system as claim 1 recites, wherein the RNN is activated by long short term memory (LSTM). 20. A system as claim 17 recites, wherein the query comprises an utterance.