Abstract: As part of a dialog session between a user and an automated assistant, implementations can process, using a streaming ASR model, a stream of audio data to generate ASR output, process, using an NLU model, the ASR output to generate NLU output, and generate, based on the NLU output, a stream of fulfillment data. Further, implementations can further determine, based on processing the stream of audio data, audio-based characteristics associated with spoken utterance(s) captured in the stream of audio data. Based on a current state of the stream of NLU output, the stream of fulfillment data, and the audio-based characteristics, implementations can determine whether a next interaction state to be implemented is: (i) causing fulfillment output to be implemented; (ii) causing natural conversation output to be audibly rendered; or (iii) refrain from causing any interaction to be implemented, can cause the next interaction state to be implemented.

Abstract: As part of a dialog session between a user and an automated assistant, implementations can process, using a streaming ASR model, a stream of audio data to generate ASR output, process, using an NLU model, the ASR output to generate NLU output, and generate, based on the NLU output, a stream of fulfillment data. Further, implementations can further determine, based on processing the stream of audio data, audio-based characteristics associated with spoken utterance(s) captured in the stream of audio data. Based on a current state of the stream of NLU output, the stream of fulfillment data, and the audio-based characteristics, implementations can determine whether a next interaction state to be implemented is: (i) causing fulfillment output to be implemented; (ii) causing natural conversation output to be audibly rendered; or (iii) refrain from causing any interaction to be implemented, can cause the next interaction state to be implemented.

Abstract: Systems and methods provides for dialog management in real-time rather than turn taking. An example method included generating first candidate responses to triggering event. The triggering event may be receipt of a live stream chunk for the dialog or receipt of a backend response to a previous backend request for a dialog shema. The method also includes updating a list of candidate responses that are accepted or pending with at least on of the first candidate responses, and determining, for the triggering event, whether the list of candidate responses includes a candidate response that has a confidence score that meets a triggering threshold. The method also includes waiting for a next triggering event without providing a candidate response when the list does not include a candidate response that has a confidence score that meets the triggering threshold.

Abstract: Systems and methods provides for dialog management in real-time rather than turn taking. An example method included generating first candidate responses to triggering event. The triggering event may be receipt of a live stream chunk for the dialog or receipt of a backend response to a previous backend request for a dialog shema. The method also includes updating a list of candidate responses that are accepted or pending with at least on of the first candidate responses, and determining, for the triggering event, whether the list of candidate responses includes a candidate response that has a confidence score that meets a triggering threshold. The method also includes waiting for a next triggering event without providing a candidate response when the list does not include a candidate response that has a confidence score that meets the triggering threshold.

Abstract: Systems and methods provides for dialog management in real-time rather than turn taking. An example method included generating first candidate responses to triggering event. The triggering event may be receipt of a live stream chunk for the dialog or receipt of a backend response to a previous backend request for a dialog schema. The method also includes updating a list of candidate responses that are accepted or pending with at least on of the first candidate responses, and determining, for the triggering event, whether the list of candidate responses includes a candidate response that has a confidence score that meets a triggering threshold. The method also includes waiting for a next triggering event without providing a candidate response when the list does not include a candidate response that has a confidence score that meets the triggering threshold.

Abstract: Systems and methods provides for dialog management in real-time rather than turn taking. An example method included generating first candidate responses to triggering event. The triggering event may be receipt of a live stream chunk for the dialog or receipt of a backend response to a previous backend request for a dialog schema. The method also includes updating a list of candidate responses that are accepted or pending with at least on of the first candidate responses, and determining, for the triggering event, whether the list of candidate responses includes a candidate response that has a confidence score that meets a triggering threshold. The method also includes waiting for a next triggering event without providing a candidate response when the list does not include a candidate response that has a confidence score that meets the triggering threshold.

Abstract: Systems and methods provides for dialog management in real-time rather than turn taking. An example method included generating first candidate responses to triggering event. The triggering event may be receipt of a live stream chunk for the dialog or receipt of a backend response to a previous backend request for a dialog shema. The method also includes updating a list of candidate responses that are accepted or pending with at least on of the first candidate responses, and determining, for the triggering event, whether the list of candidate responses includes a candidate response that has a confidence score that meets a triggering threshold. The method also includes waiting for a next triggering event without providing a candidate response when the list does not include a candidate response that has a confidence score that meets the triggering threshold.

Abstract: As part of a dialog session between a user and an automated assistant, implementations can process, using a streaming ASR model, a stream of audio data to generate ASR output, process, using an NLU model, the ASR output to generate NLU output, and generate, based on the NLU output, a stream of fulfillment data. Further, implementations can further determine, based on processing the stream of audio data, audio-based characteristics associated with spoken utterance(s) captured in the stream of audio data. Based on a current state of the stream of NLU output, the stream of fulfillment data, and the audio-based characteristics, implementations can determine whether a next interaction state to be implemented is: (i) causing fulfillment output to be implemented; (ii) causing natural conversation output to be audibly rendered; or (iii) refrain from causing any interaction to be implemented, can cause the next interaction state to be implemented.

Abstract: Systems and methods for annotating conversations between a user and an electronic assistant in a hierarchical manner are disclosed. An example system includes a data store of hierarchical annotation records. The hierarchical annotation records include root records and non-root records. Each root record has a conversation identifier and an annotation identifier. Each non-root record includes an annotation identifier and a parent identifier, the parent identifier being an annotation identifier of another record. At least some of the non-root records further include an annotation tag. The system generates a first new root record and first non-root records for an input dialog act and generates a second new root record and second non-root records for an output dialog act that is responsive to the input dialog act. At least one of the second non-root records includes an annotation tag linking back to a record of the first non-root records.

Abstract: Systems and methods provides for dialog management in real-time rather than turn taking. An example method included generating first candidate responses to triggering event. The triggering event may be receipt of a live stream chunk for the dialog or receipt of a backend response to a previous backend request for a dialog shema. The method also includes updating a list of candidate responses that are accepted or pending with at least on of the first candidate responses, and determining, for the triggering event, whether the list of candidate responses includes a candidate response that has a confidence score that meets a triggering threshold. The method also includes waiting for a next triggering event without providing a candidate response when the list does not include a candidate response that has a confidence score that meets the triggering threshold.

Abstract: Systems and methods provide for dialog management in real-time rather than turn taking. An example method includes generating first candidate responses to a triggering event. The triggering event may be receipt of a live-stream chunk for the dialog or receipt of a backend response to a previous backend request for a dialog schema. The method also includes updating a list of candidate responses that are accepted or pending with at least one of the first candidate responses, and determining, for the triggering event, whether the list of candidate responses includes a candidate response that has a confidence score that meets a triggering threshold. The method also includes waiting for a next triggering event without providing a candidate response when the list does not include a candidate response that has a confidence score that meets the triggering threshold.

Abstract: Systems and methods for annotating conversations between a user and an electronic assistant in a hierarchical manner are disclosed. An example system includes a data store of hierarchical annotation records. The hierarchical annotation records include root records and non-root records. Each root record has a conversation identifier and an annotation identifier. Each non-root record includes an annotation identifier and a parent identifier, the parent identifier being an annotation identifier of another record. At least some of the non-root records further include an annotation tag. The system generates a first new root record and first non-root records for an input dialog act and generates a second new root record and second non-root records for an output dialog act that is responsive to the input dialog act. At least one of the second non-root records includes an annotation tag linking back to a record of the first non-root records.

Abstract: Systems and methods provide for dialog management in real-time rather than turn taking. An example method includes generating first candidate responses to a triggering event. The triggering event may be receipt of a live-stream chunk for the dialog or receipt of a backend response to a previous backend request for a dialog schema. The method also includes updating a list of candidate responses that are accepted or pending with at least one of the first candidate responses, and determining, for the triggering event, whether the list of candidate responses includes a candidate response that has a confidence score that meets a triggering threshold. The method also includes waiting for a next triggering event without providing a candidate response when the list does not include a candidate response that has a confidence score that meets the triggering threshold.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for ranking search results. One of the methods includes identifying one or more sessions for a query and associating watch times of the respective resources watched in the sessions with the query. One or more watch time signals are calculated for a first resource and the query based on the watch times associated with the query. A first search result responsive to the query is obtained, wherein the first search result identifies the first resource and has an associated score S. A new score S? is calculated based on a least S and a watch time function, the watch time function being a function of the one or more watch time signals. The new score S? is provided to a process for ranking search results including the first search result.

Abstract: An apparatus and a method for searching through a parse tree of a source code of a computer program use a new type of parse tree including all computational as well as non-computational entries. The new type of parse tree in combination with a new query language and query language processor provide for fast and easy search through a parse tree for the occurrences of one or more selected terms in the new type of parse tree, and for easy generation of new source code differing from the original source code by modifications specified in the query language statements.The principles of the new method for searching is also applicable on other types of structured data, such as simulation programs and multi-level computer-aided-engineering documents.