Abstract: In one embodiment, a method includes, by one or more computing devices, receiving, from a client system associated with a first user of an online social network, an indication of a trigger action by the first user, wherein the trigger action is associated with a user activity, determining a first user context based on the trigger action, accessing one or more recommended content objects associated with the first user context, calculating a recommendation score for each recommended content object, generating one or more content suggestions comprising one of the one or more recommended content objects, respectively, each content suggestion corresponding to a recommended content object having a recommendation score above a threshold recommendation score, and sending, to the client system in response to the trigger action, instructions for presenting one or more of the content suggestions to the first user.

Abstract: In one embodiment, a method includes receiving an indication of a trigger action by a first user, wherein the trigger action is associated with a priming content object, identifying one or more related content objects associated with the priming content object, generating a first feature vector representing the priming content object, the one or more related content objects, and profile information of the first user, accessing a plurality of second feature vectors representing a plurality of recommended content objects, respectively, selecting one or more of the recommended content objects based on comparisons between the first feature vector and the respective second feature vectors representing the recommended content objects, and sending, to a client system in response to the trigger action, instructions for presenting one or more content suggestions to the first user, wherein each content suggestion comprises one of the selected recommended content objects.

Abstract: In one embodiment, a method includes receiving, from a client system associated with a first user, a first audio input. The method includes generating multiple transcriptions corresponding to the first audio input based on multiple automatic speech recognition (ASR) engines. Each ASR engine is associated with a respective domain out of multiple domains. The method includes determining, for each transcription, a combination of one or more intents and one or more slots to be associated with the transcription. The method includes selecting, by a meta-speech engine, one or more combinations of intents and slots from the multiple combinations to be associated with the first user input. The method includes generating a response to the first audio input based on the selected combinations and sending, to the client system, instructions for presenting the response to the first audio input.

Abstract: System and method for processing and presenting a conversation. For example, a system includes a sensor configured to capture an audio-form conversation, and a processor configured to automatically transform the audio-form conversation into a transformed conversation. The transformed conversation includes a synchronized text, and the synchronized text is synchronized with the audio-form conversation. Additionally, the system includes a presenter configured to present the transformed conversation including the synchronized text and the audio-form conversation.

Abstract: In one embodiment, a method includes receiving a user request for a summarization of a particular type of content objects from a client system associated with a first user, determining one or more modalities associated with the user request, selecting a plurality of content objects of the particular type based on a user profile of the first user, wherein the user profile comprises one or more confidence scores associated with one or more subjects associated with the first user, respectively, and wherein the plurality of content objects are selected based on the one or more confidence scores, generating a summary of each content object based on the user profile and the determined modalities, and sending, to the client system in response to the user request, instructions for presenting the summaries of the plurality of content objects, wherein the summaries are presented via one or more of the determined modalities.

Abstract: In one embodiment, a method includes receiving a first user input from a first user, wherein the first user input comprises a partial request, presenting one or more suggested intent auto-completions corresponding to the partial request, receiving a selection by the first user of a first suggested intent auto-completion of the suggested intent auto-completions and a second user input, presenting one or more suggested slot auto-completions corresponding to one or more candidate slot-hypotheses corresponding to the second user input, respectively, wherein each of the candidate slot-hypotheses comprise a slot-suggestion, and wherein each suggested slot auto-completion comprises the second user input and the corresponding candidate slot-hypothesis, receiving a selection by the first user of a first suggested slot auto-completion of the suggested slot auto-completions, and presenting execution results of one or more tasks corresponding to the first suggested intent auto-completion and the first suggested slot auto

Abstract: In one embodiment, a method includes receiving a user input including a partial request from a client system of a first user, analyzing the user input to generate one or more candidate hypotheses based on a personalized language model where each of the candidate hypotheses includes one or more of an intent-suggestion or a slot-suggestion, sending instructions for presenting one or more suggested auto-completions corresponding to one or more of the candidate hypotheses, respectively, to the client system, where each suggested auto-completion comprises the partial request and the corresponding candidate hypothesis, receiving an indication of a selection by the first user of a first suggested auto-completion of the suggested auto-completions from the client system, and executing one or more tasks based on the first suggested auto-completion selected by the first user via one or more agents.

Abstract: In one embodiment, a method includes receiving, from a client system via an assistant xbot of a messaging application, a first user input comprising a command for controlling a network-connected device, wherein the device is associated with a contact identifier (ID) in the messaging application, identifying the device based on the contact ID, generating one or more control instructions for the device based on the command, sending, to the device via a network, the control instructions to cause the device to perform one or more actions associated with the command, receiving, from the device via the network, a response to the command, and sending, to the client system via the assistant xbot, an output based on the response from the device. The first user input may include a character string that specifies the command or an indication of a selected user interface feature that corresponds to the command.

Abstract: An online system receives a request to generate presentation content for presentation to a user. The online system receives a set of content items and identifies a surface for presenting the presentation information to the user. For example, the surface may be a voice only surface, a voice and graphical display, a graphical display only. Based on the identified surface, the online system ranks the set of content items. The online system then determines presentation information for a subset of the content items and transmits instructions to present the presentation information at the surface.

Abstract: A computing device receives a message including a request for a recommendation. A representation of a hypothetical ideal recommendation to provide in response to the message is determined based on the message content. Data regarding entities that are potential recommendations are retrieved from a data store, the data regarding each entity including a representation of the entity (e.g., a vector) derived from factual information about the entity and opinions of other users of the entity. Ranking scores are determined for at least a subset of the entities based on the difference between the entity representations and the representation of the hypothetical ideal recommendation. An entity to recommend is selected based on the ranking scores and a reply to the message is sent that identifies the selected entity.

Abstract: An online system receives a request to generate presentation content for presentation to a user. The online system receives a set of content items and identifies a surface for presenting the presentation information to the user. For example, the surface may be a voice only surface, a voice and graphical display, a graphical display only. Based on the identified surface, the online system ranks the set of content items. The online system then determines presentation information for a subset of the content items and transmits instructions to present the presentation information at the surface.

Abstract: In one embodiment, a method includes receiving a sharing request to share content generated during a current dialog session from a client system associated with a first user, identifying one or more content objects associated with the sharing request based on a natural-language understanding module, wherein the one or more content objects were previously generated during the current dialog session, determining one or more second users the sharing request is directed to based on a user profile associated with first user, and sending instructions for accessing one or more of the identified content objects to one or more client systems associated with tPhe one or more second users.

Abstract: In one embodiment, a method includes receiving a user request for a summarization of a particular type of content objects from a client system associated with a first user, determining one or more modalities associated with the user request, selecting a plurality of content objects of the particular type based on a user profile of the first user, wherein the user profile comprises one or more confidence scores associated with one or more subjects associated with the first user, respectively, and wherein the plurality of content objects are selected based on the one or more confidence scores, generating a summary of each content object based on the user profile and the determined modalities, and sending, to the client system in response to the user request, instructions for presenting the summaries of the plurality of content objects, wherein the summaries are presented via one or more of the determined modalities.

Abstract: In one embodiment, a method includes receiving a user input including a partial request from a client system of a first user, analyzing the user input to generate one or more candidate hypotheses based on a personalized language model where each of the candidate hypotheses includes one or more of an intent-suggestion or a slot-suggestion, sending instructions for presenting one or more suggested auto-completions corresponding to one or more of the candidate hypotheses, respectively, to the client system, where each suggested auto-completion comprises the partial request and the corresponding candidate hypothesis, receiving an indication of a selection by the first user of a first suggested auto-completion of the suggested auto-completions from the client system, and executing one or more tasks based on the first suggested auto-completion selected by the first user via one or more agents.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating representations of input sequences. One of the methods includes receiving a grapheme sequence, the grapheme sequence comprising a plurality of graphemes arranged according to an input order; processing the sequence of graphemes using a long short-term memory (LSTM) neural network to generate an initial phoneme sequence from the grapheme sequence, the initial phoneme sequence comprising a plurality of phonemes arranged according to an output order; and generating a phoneme representation of the grapheme sequence from the initial phoneme sequence generated by the LSTM neural network, wherein generating the phoneme representation comprises removing, from the initial phoneme sequence, phonemes in one or more positions in the output order.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage medium, for implementing a pronunciation dictionary that stores entity name pronunciations. In one aspect, a method includes actions of receiving audio data corresponding to an utterance that includes a command and an entity name. Additional actions may include generating, by an automated speech recognizer, an initial transcription for a portion of the audio data that is associated with the entity name, receiving a corrected transcription for the portion of the utterance that is associated with the entity name, obtaining a phonetic pronunciation that is associated with the portion of the audio data that is associated with the entity name, updating a pronunciation dictionary to associate the phonetic pronunciation with the entity name, receiving a subsequent utterance that includes the entity name, and transcribing the subsequent utterance based at least in part on the updated pronunciation dictionary.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for presenting notifications in an enterprise system. In one aspect, a method include actions of obtaining a template that defines (i) trigger criteria for presenting a notification type and (ii) content rules for determining content to include in a notification of the notification type. Additional actions include accessing enterprise resources of an enterprise, the enterprise resources including data describing entities related to the enterprise and relationships among the entities. Further actions include, accessing user information specific to a user and determining that the trigger criteria is satisfied by the enterprise resources and the user information. Additional actions include generating a particular notification of the notification type based at least on the content rules and providing the particular notification to the user.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for verifying pronunciations. In one aspect, a method includes obtaining a first transcription for an utterance. A second transcription for the utterance is obtained. The second transcription is different from the first transcription. One or more feature scores are determined based on the first transcription and the second transcription. The one or more feature scores are input to a trained classifier. An output of the classifier is received. The output indicates which of the first transcription and the second transcription is more likely to be a correct transcription of the utterance.

Abstract: A server accesses an initial query associated with a classification, the classification corresponding to a likely intent of the initial query. The server obtains a set of queries, wherein each query in the set of queries is identified as having resulted in one or more users selecting a resource that was selected by one or more users in response to submitting the initial query. The server then determines a metric for one or more queries in the set of queries, wherein the metric for each of the one or more queries in the set of queries is based on a similarity between the respective query and the initial query. Next, the server selects a subset of queries from the set of queries based on the metric for each selected query satisfying a threshold and associates the selected subset of queries with the classification of the initial query.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, including selecting terms; obtaining an expected phonetic transcription of an idealized native speaker of a natural language speaking the terms; receiving audio data corresponding to a particular user speaking the terms in the natural language; obtaining, based on the audio data, an actual phonetic transcription of the particular user speaking the terms in the natural language; aligning the expected phonetic transcription of the idealized native speaker of the natural language with the actual phonetic transcription of the particular user; identifying, based on the aligning, a portion of the expected phonetic transcription that is different than a corresponding portion of the actual phonetic transcription; and based on identifying the portion of the expected phonetic transcription, designating the expected phonetic transcription as a substitute pronunciation for the corresponding portion of the actual phonetic tr