Abstract: A method includes receiving an utterance at a computerized automated assistant system, and detecting, via a date/time constraint module of the computerized automated assistant system, one or more constraints in the utterance associated with a date or time. The utterance is associated with a domain. The method further comprises generating, via the date/time constraint module, a periodic set for each of the one or more constraints associated with the date or time, and combining, via the date/time constraint module, the one or more periodic sets. The method further comprises processing, via a dialogue manager module of the computerized automated assistant system, the combined periodic sets to determine an action, and executing the action at the computerized automated assistant system.

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 for configuring an automated dialogue system uses traces of interactions via a graphical user interface (GUI) for an application. Each trace includes interactions in the context of a plurality of presentations of the GUI. Elements of one or more presentations of the GUI are identified, and templates are associated with portions of the trace. Each template has one or more defined inputs and a defined output. For each template of the plurality of templates, the portions of the traces are processed to automatically configure the template by specifying a procedure for providing values of inputs to the template via the GUI and obtaining a value of an output. The automated dialogue system is configured with the configured templates, thereby avoiding manual configuration of the dialogue system.

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 includes receiving an utterance at a computerized automated assistant system, and detecting, via a date/time constraint module of the computerized automated assistant system, one or more constraints in the utterance associated with a date or time. The utterance is associated with a domain. The method further comprises generating, via the date/time constraint module, a periodic set for each of the one or more constraints associated with the date or time, and combining, via the date/time constraint module, the one or more periodic sets. The method further comprises processing, via a dialogue manager module of the computerized automated assistant system, the combined periodic sets to determine an action, and executing the action at the computerized automated assistant system.

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 includes receiving an utterance at a computerized automated assistant system, and detecting, via a date/time constraint module of the computerized automated assistant system, one or more constraints in the utterance associated with a date or time. The utterance is associated with a domain. The method further comprises generating, via the date/time constraint module, a periodic set for each of the one or more constraints associated with the date or time, and combining, via the date/time constraint module, the one or more periodic sets. The method further comprises processing, via a dialogue manager module of the computerized automated assistant system, the combined periodic sets to determine an action, and executing the action at the computerized automated assistant system.

Abstract: A data collection system is based on a general set of dialogue acts which are derived from a database schema. Crowd workers perform two types of tasks: (i) identification of sensical dialogue paths and (ii) performing context-dependent paraphrasing of these dialogue paths into real dialogues. The end output of the system is a set of training examples of real dialogues which have been annotated with their logical forms. This data can be used to train all three components of the dialogue system: (i) the semantic parser for understanding context-dependent utterances, (ii) the dialogue policy for generating new dialogue acts given the current state, and (iii) the generation system for both deciding what to say and how to render it in natural language.

Abstract: A method for a dialogue system includes establishing a dialogue session between an application executing on a server and a remote machine. The dialogue session includes one or more utterances received from a user at the remote machine. A natural language processing machine identifies a request associated with a computer-readable representation of an utterance. A dialogue expansion machine generates a plurality of alternative actions for responding to the request. A previously-trained machine learning confidence model assesses a confidence score for each alternative. If a highest confidence score for a top alternative does not satisfy a threshold, the plurality of alternatives including the top alternative are transmitted to a remote machine (which may be the same remote machine or a different remote machine) for review by a human reviewer. After the dialogue system and/or the human reviewer select an alternative, computer-readable instructions defining the selected alternative are executed.

Abstract: A method for a dialogue system includes establishing a dialogue session between an application executing on a server and a remote machine. The dialogue session includes one or more utterances received from a user at the remote machine. A natural language processing machine identifies a request associated with a computer-readable representation of an utterance. A dialogue expansion machine generates a plurality of alternative actions for responding to the request. A previously-trained machine learning confidence model assesses a confidence score for each alternative. If a highest confidence score for a top alternative does not satisfy a threshold, the plurality of alternatives including the top alternative are transmitted to a remote machine (which may be the same remote machine or a different remote machine) for review by a human reviewer. After the dialogue system and/or the human reviewer select an alternative, computer-readable instructions defining the selected alternative are executed.

Abstract: A method for configuring an automated dialogue system uses traces of interactions via a graphical user interface (GUI) for an application. Each trace includes interactions in the context of a plurality of presentations of the GUI. Elements of one or more presentations of the GUI are identified, and templates are associated with portions of the trace. Each template has one or more defined inputs and a defined output. For each template of the plurality of templates, the portions of the traces are processed to automatically configure the template by specifying a procedure for providing values of inputs to the template via the GUI and obtaining a value of an output. The automated dialogue system is configured with the configured templates, thereby avoiding manual configuration of the dialogue system.

Abstract: A method for configuring an automated dialog system uses traces of interactions via a graphical user interface (GUI) for an application. Each trace includes interactions in the context of a plurality of presentations of the GUI. Elements of one or more presentations of the GUI are identified, and templates are associated with portions of the trace. Each template has one or more defined inputs and a defined output. For each template of the plurality of templates, the portions of the traces are processed to automatically configure the template by specifying a procedure for providing values of inputs to the template via the GUI and obtaining a value of an output. The automated dialog system is configured with the configured templates, thereby avoiding manual configuration of the dialog system.

Abstract: An interaction assistant conducts multiple turn interaction dialogs with a user in which context is maintained between turns, and the system manages the dialog to achieve an inferred goal for the user. The system includes a linguistic interface to a user and a parser for processing linguistic events from the user. A dialog manager of the system is configured to receive alternative outputs from the parser, and selecting an action and causing the action to be performed based on the received alternative outputs. The system further includes a dialog state for an interaction with the user, and the alternative outputs represent alternative transitions from a current dialog state to a next dialog state. The system further includes a storage for a plurality of templates, and wherein each dialog state is defined in terms of an interrelationship of one or more instances of the templates.

Abstract: An interaction assistant conducts multiple turn interaction dialogs with a user in which context is maintained between turns, and the system manages the dialog to achieve an inferred goal for the user. The system includes a linguistic interface to a user and a parser for processing linguistic events from the user. A dialog manager of the system is configured to receive alternative outputs from the parser, and selecting an action and causing the action to be performed based on the received alternative outputs. The system further includes a dialog state for an interaction with the user, and the alternative outputs represent alternative transitions from a current dialog state to a next dialog state. The system further includes a storage for a plurality of templates, and wherein each dialog state is defined in terms of an interrelationship of one or more instances of the templates.

Abstract: A method includes receiving an utterance at a computerized automated assistant system, and detecting, via a date/time constraint module of the computerized automated assistant system, one or more constraints in the utterance associated with a date or time. The utterance is associated with a domain. The method further comprises generating, via the date/time constraint module, a periodic set for each of the one or more constraints associated with the date or time, and combining, via the date/time constraint module, the one or more periodic sets. The method further comprises processing, via a dialogue manager module of the computerized automated assistant system, the combined periodic sets to determine an action, and executing the action at the computerized automated assistant system.

Abstract: A method for a dialogue system includes establishing a dialogue session between an application executing on a server and a remote machine. The dialogue session includes one or more utterances received from a user at the remote machine. A natural language processing machine identifies a request associated with a computer-readable representation of an utterance. A dialogue expansion machine generates a plurality of alternative actions for responding to the request. A previously-trained machine learning confidence model assesses a confidence score for each alternative. If a highest confidence score for a top alternative does not satisfy a threshold, the plurality of alternatives including the top alternative are transmitted to a remote machine (which may be the same remote machine or a different remote machine) for review by a human reviewer. After the dialogue system and/or the human reviewer select an alternative, computer-readable instructions defining the selected alternative are executed.

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.