Abstract: Various embodiments discussed herein enable client applications to be heavily integrated with a voice assistant in order to perform commands associated with voice utterances of users via voice assistant functionality and also seamlessly cause client applications to automatically perform native functions as part of executing the voice utterance. Such heavy integration also allows particular embodiments to support multi-modal input from a user for a single conversational interaction. In this way, client application user interface interactions, such as clicks, touch gestures, or text inputs are executed alternative or in addition to the voice utterances.

Abstract: Various embodiments discussed herein enable client applications to be heavily integrated with a voice assistant in order to both perform commands associated with voice utterances of users via voice assistant functionality and also seamlessly cause client applications to automatically perform native functions as part of executing the voice utterance. For example, some embodiments can automatically and intelligently cause a switch to a page the user needs and automatically and intelligently cause a population of particular fields of the page the user needs based on a user view context and the voice utterance.

Abstract: Various embodiments discussed herein enable client applications to be heavily integrated with a voice assistant in order to both perform commands associated with voice utterances of users via voice assistant functionality and also seamlessly cause client applications to automatically perform native functions as part of executing the voice utterance. For example, some embodiments can automatically and intelligently cause a switch to a page the user needs and automatically and intelligently cause a population of particular fields of the page the user needs based on a user view context and the voice utterance.

Abstract: Various embodiments discussed herein enable client applications to be heavily integrated with a voice assistant in order to perform commands associated with voice utterances of users via voice assistant functionality and also seamlessly cause client applications to automatically perform native functions as part of executing the voice utterance. Such heavy integration also allows particular embodiments to support multi-modal input from a user for a single conversational interaction. In this way, client application user interface interactions, such as clicks, touch gestures, or text inputs are executed alternative or in addition to the voice utterances.

Abstract: Various embodiments discussed herein enable client applications to be heavily integrated with a voice assistant in order to both perform commands associated with voice utterances of users via voice assistant functionality and also seamlessly cause client applications to automatically perform native functions as part of executing the voice utterance. For example, some embodiments can automatically and intelligently cause a switch to a page the user needs and automatically and intelligently cause a population of particular fields of the page the user needs based on a user view context and the voice utterance.

Abstract: A system that provides a sharable language interface for implementing automated assistants in new domains and applications. A dialogue assistant that is trained in a first domain can receive a specification in a second domain. The specification can include language structure data such as schemas, recognizers, resolvers, constraints and invariants, actions, language hints, generation template, and other data. The specification data is applied to the automated assistant to enable the automated assistant to provide interactive dialogue with a user in a second domain associated with the received specification. In some instances, portions of the specification may be automatically mapped to portions of the first domain. By having the ability to learn new domains and applications through receipt of objects and properties rather than retooling the interface entirely, the present system is much more efficient at learning how to provide interactive dialogue in new domains than previous systems.

Abstract: A method comprising recognizing a user utterance including an ambiguity. The method further comprises using a previously-trained code-generation machine to produce, from the user utterance, a data-flow program including a search-history function. The search-history function is configured to select a highest-confidence disambiguating concept from one or more candidate concepts stored in a context-specific dialogue history.

Abstract: A method, comprising recognizing a user utterance for processing. The method further comprises using a previously-trained code-generation machine to generate, from the user utterance, a data-flow program configured to produce a return value upon successful execution. The method further comprises beginning execution of the data-flow program. Responsive to reaching an error condition resulting from execution of the data-flow program, the method further comprises, prior to the data-flow program producing the return value, suspending execution of the data flow program. The method further comprises using the previously-trained code-generation machine to generate an error-handling data-flow program, wherein the error-handling data-flow program is configured to produce the return value; beginning execution of the error-handling data-flow program to produce the return value; and outputting the return value.

Abstract: A system that provides a sharable language interface for implementing automated assistants in new domains and applications. A dialogue assistant that is trained in a first domain can receive a specification in a second domain. The specification can include language structure data such as schemas, recognizers, resolvers, constraints and invariants, actions, language hints, generation template, and other data. The specification data is applied to the automated assistant to enable the automated assistant to provide interactive dialogue with a user in a second domain associated with the received specification. In some instances, portions of the specification may be automatically mapped to portions of the first domain. By having the ability to learn new domains and applications through receipt of objects and properties rather than retooling the interface entirely, the present system is much more efficient at learning how to provide interactive dialogue in new domains than previous systems.

Abstract: A system that provides a sharable language interface for implementing automated assistants in new domains and applications. A dialogue assistant that is trained in a first domain can receive a specification in a second domain. The specification can include language structure data such as schemas, recognizers, resolvers, constraints and invariants, actions, language hints, generation template, and other data. The specification data is applied to the automated assistant to enable the automated assistant to provide interactive dialogue with a user in a second domain associated with the received specification. In some instances, portions of the specification may be automatically mapped to portions of the first domain. By having the ability to learn new domains and applications through receipt of objects and properties rather than retooling the interface entirely, the present system is much more efficient at learning how to provide interactive dialogue in new domains than previous systems.

Abstract: A method, comprising recognizing a user utterance for processing. The method further comprises using a previously-trained code-generation machine to generate, from the user utterance, a data-flow program configured to produce a return value upon successful execution. The method further comprises beginning execution of the data-flow program. Responsive to reaching an error condition resulting from execution of the data-flow program, the method further comprises, prior to the data-flow program producing the return value, suspending execution of the data flow program. The method further comprises using the previously-trained code-generation machine to generate an error-handling data-flow program, wherein the error-handling data-flow program is configured to produce the return value; beginning execution of the error-handling data-flow program to produce the return value; and outputting the return value.

Abstract: A method comprising recognizing a user utterance including an ambiguity. The method further comprises using a previously-trained code-generation machine to produce, from the user utterance, a data-flow program including a search-history function. The search-history function is configured to select a highest-confidence disambiguating concept from one or more candidate concepts stored in a context-specific dialogue history.

Abstract: A system that provides a sharable language interface for implementing automated assistants in new domains and applications. A dialogue assistant that is trained in a first domain can receive a specification in a second domain. The specification can include language structure data such as schemas, recognizers, resolvers, constraints and invariants, actions, language hints, generation template, and other data. The specification data is applied to the automated assistant to enable the automated assistant to provide interactive dialogue with a user in a second domain associated with the received specification. In some instances, portions of the specification may be automatically mapped to portions of the first domain. By having the ability to learn new domains and applications through receipt of objects and properties rather than retooling the interface entirely, the present system is much more efficient at learning how to provide interactive dialogue in new domains than previous systems.

Abstract: An automated assistant automatically recognizes speech, decode paraphrases in the recognized speech, performs an action or task based on the decoder output, and provides a response to the user. The response may be text or audio, and may be translated to include paraphrasing. The automatically recognized speech may be processed to determine partitions in the speech, which may be in turn processed to identify paraphrases in the partitions. A decoder may process an input utterance text to identify paraphrases content to include in a segment or sentence. The decoder may paraphrase the input utterance to make the utterance, updated with one or more paraphrases, more easily parsed by a parser. A translator may process a generated response to make the response sound more natural. The translator may replace content of the generated response with paraphrase content based on the state of the conversation with the user, including salience data.