In one embodiment, a method includes receiving an initial input in a first modality from a first user from a client system associated with the first user, determining one or more intents corresponding to the initial input by an intent-understanding module, generating one or more candidate continuation-inputs based on the one or more intents, where the one or more candidate continuation-inputs are in one or more candidate modalities, respectively, and wherein the candidate modalities are different from the first modality, and sending instructions for presenting one or more suggested inputs corresponding to one or more of the candidate continuation-inputs to the client system.

In one embodiment, a method includes detecting a user input comprising an incomplete gesture performed by one or more hands of a first user by a client system associated with the first user; selecting one or more candidate gestures from a plurality of pre-defined gestures by the client system based on a personalized gesture-recognition model, wherein each of the candidate gestures is associated with a confidence score representing a likelihood the first user intended to input the respective candidate gesture, and presenting one or more suggested inputs corresponding to one or more of the candidate gestures at the client system.

In one embodiment, a method includes initiating a communication session with a second client system associated with a second user via a communication network, wherein the communication session is initiated in a first modality, receiving a ping to the first client system from the communication network to evaluate available bandwidth on the communication network, estimating, by the first client system, an amount of bandwidth available on the communication network for use by the first client system, determining, by the first client system, the amount of bandwidth available on the communication network for use by the first client system is insufficient for the first modality, and switching the communication session with the second client system to a second modality by the first client system, wherein the second modality uses less bandwidth than the first modality.

In one embodiment, a method includes receiving, from a client system of a user, a user input comprising a plurality of n-grams, parsing the user input to identify one or more overall intents, hidden intents, and slots associated with the one or more n-grams, wherein at least one of the hidden intents is non-resolvable for being associated with partial slot information corresponding to an n-gram that has not been resolved to a particular entity identifier, wherein the partial slot information is associated with two more entity identifiers of two or more entities, respectively, sending, to the client system, instructions for prompting the user to select one of the entities to be associated with the non-resolvable hidden intent, resolving the non-resolvable hidden intent based on the entity identifier of the entity selected by the user, and generating a response to the user input based on the resolved hidden intent.

In one embodiment, a method includes, by one or more computing systems, receiving, from a client system via an assistant xbot, a first audio input from a user, parsing the first audio input to identify a request for a content digest from an online social network, retrieving a plurality of content objects from the online social network, each of the content objects being accessible by the user, generating a customized newsfeed summary based on a determined semantical meaning of each of the plurality of content objects, and sending, to the client system via the assistant xbot, instructions for presenting an audio clip to the user responsive to the request for the content digest, wherein the audio clip comprises the customized newsfeed summary.

In one embodiment, a method includes receiving an indication of a trigger action by a first user at a client system, wherein the trigger action is associated with a priming content object, identifying related content objects associated with the priming content object, selecting recommended content objects based on the priming content object, the related content objects, and profile information of the first user, wherein each of the selected recommended content objects comprises entity information of entities associated with the priming content object, and presenting content suggestions at the client system, wherein each content suggestion comprises one of the selected recommended content objects.

In one embodiment, a method includes, by one or more computing systems, receiving, by an assistant xbot associated with the one or more computing systems, a user request for a content digest, retrieving one or more content objects corresponding to the user request, generating one or more slides for the one or more retrieved content objects, respectively, and providing, by the assistant xbot, instructions for presenting the content digest responsive to the request from the first user, wherein the content digest comprises the one or more slides, and wherein one or more of the slides of the content digest are removed from the content digest after a predetermined time period.

In one embodiment, a method includes, by one or more computing systems, receiving a request from a user for a content digest from an online social network, retrieving one or more content objects associated with the online social network that are accessible by the user, determining a semantical-embedding for each retrieved content object based on a query model, determining one or more categories for each retrieved content object, generating a set of content suggestions for each retrieved content object based on the one or more categories associated with the content object and the semantical-embedding of the content object, ranking for each retrieved content object, the one or more content suggestions in the respective set based on a comparison of a semantical-embedding associated with each content suggestion to the semantical-embedding of the content object, and sending instructions for presenting the content digest to the user.

In one embodiment, a method includes, by one or more computing systems, receiving a user input comprising a plurality of n-grams from a user of a client system, generating a tree-structured representation for the user input based on a parsing by a compositional model, resolving the tree-structured representation by applying a depth-first search algorithm, wherein the tree-structured representation comprises one or more non-resolvable non-terminal nodes associated with one or more slots, and wherein each non-terminal parent node of a non-resolvable non-terminal node is partially resolved based on partial slot information associated with the non-resolvable non-terminal node, and wherein each non-resolvable non-terminal node is resolved based on the respective partially resolved non-terminal parent node of the non-resolvable non-terminal node, generating a response to the user input based on the resolved tree-structured representation, sending instructions for presenting the response to the client system of the user.

In one embodiment, a method includes, by one or more computing systems, receiving, from a client system associated with a user, an initial portion of a user input, wherein the initial portion comprises a partial request, and wherein the initial portion is received while the user is continuing to provide further input, generating, responsive to receiving the initial portion of the user input, one or more speculative queries based on the partial request and a machine-learning predictive model, wherein each speculative query is a predicted complete request based on the partial request, calculating a confidence score for each speculative query based on the predictive model, ranking the one or more speculative queries based on their respective confidence scores and associated costs, executing one or more of the speculative queries based on their ranks, and caching one or more results of the executed one or more speculative queries.