Meta Platforms Technologies, LLC

Facebook Technologies, LLC

Meta Platforms, Inc.

Pararth Paresh Shah

Rajen Subba

Anuj Kumar

Seungwhan Moon

Bing Liu

Baiyang Liu

Paul Anthony Crook

Honglei Liu

Wenhai Yang

Xiaolei Li

Hao Zhou

Yulong Qiu

Daniel Chai

Hu Xu

Wenxuan Li

In one embodiment, a method includes training a target machine-learning model iteratively by accessing training data of content objects, training an intermediate machine-learning model that outputs contextual evaluation measurements based on the training data, generating state-indications associated with the training data, wherein the state-indications comprise user-intents, system actions, and user actions, training the target machine-learning model based on the contextual evaluation measurements, the state-indications, and an action set comprising possible system actions, extracting rules based on the target machine-learning model by a sequential pattern-mining model, generating synthetic training data based on the rules, updating the training data by adding the synthetic training data to the training data, determining if a completion condition is reached for the training, and if the completion condition is reached returning the target machine-learning model, else repeating the iterative training of the target machine-learning model.

Interpretability of deep reinforcement learning models in assistant systems

In one embodiment, a method includes receiving a user request from a client system associated with a user, generating a response to the user request which references one or more entities, generating a personalized recommendation based on the user request and the response, wherein the personalized recommendation references one or more of the entities of the response, and sending instructions for presenting the response and the personalized recommendation to the client system.

Personalized conversational recommendations by assistant systems

In one embodiment, a method includes receiving a user input at a client system, wherein the user input is associated with one or more intents and one or more slots, generating one or more first dialog acts based on the user input, calculating a task-confidence score based on one or more intent-confidence scores associated with the one or more intents, respectively, and one or more slot-confidence scores associated with the one or more slots, respectively, generating one or more second dialog acts modifying the one or more first dialog acts responsive to the task-confidence score being less than a threshold score, and presenting a response to the user input at the client system, wherein the response is based on one or more of the first dialog acts or the second dialog acts.

Dialog session override policies for assistant systems

In one embodiment, a method includes receiving a query from a user from a client system associated with the user, determining one or more initial memory slots based on the query, accessing a memory graph associated with the user which comprises a plurality of nodes and a plurality of edges connecting the nodes, and wherein one or more of the nodes correspond to one or more episodic memories of the user, respectively, and wherein each edge corresponds to a relationship between the connected nodes, selecting one or more candidate nodes from the memory graph by one or more machine-learning models based on the initial memory slots, generating a response based on the initial memory slots and episodic memories corresponding to the selected candidate nodes, and sending instructions for presenting the response to the client system in response to the query.

Memory grounded conversational reasoning and question answering for assistant systems

In one embodiment, a method includes receiving a query from a user from a client system associated with the user, accessing a knowledge graph comprising a plurality of nodes and edges connecting the nodes, wherein each node corresponds to an entity and each edge corresponds to a relationship between the entities corresponding to the connected nodes, determining one or more initial entities associated with the query based on the query, selecting one or more candidate nodes by a conversational reasoning model from the knowledge graph corresponding to one or more candidate entities, respectively, wherein each candidate node is selected based on the nodes corresponding to the initial entities, dialog states associated with the query, and a context associated with the query, generating a response based on the initial entities and the candidate entities, and sending instructions for presenting the response to the client system in response to the query.

Conversational reasoning with knowledge graph paths for assistant systems

In one embodiment, a method includes receiving a user input from a client system associated with a user, determining a task based on the user input and a confidence score associated with the task, generating one or more first dialog acts based on a task policy which specifies dialog acts associated with the task, generating one or more second dialog acts based on an override policy responsive to the confidence score being less than a threshold score, wherein the override policy specifies dialog acts that modify dialog acts specified by the task policy; and sending instructions for presenting a response to the user input to the client system, wherein the response is based on one or more of the first dialog acts or the second dialog acts.

Growing community of inventors

Sunnyvale, CA, United States of America

Pararth Paresh Shah