Facebook, Inc.

Antoine Bordes

Jason Edward Weston

Alexandre Lebrun

Martin Jean Raison

Sumit Chopra

Adam Kal Lerer

Timothee Lacroix

Adam Joshua Fisch

Techniques to predictively respond to user requests using natural language processing are described. In one embodiment, an apparatus may comprise a client communication component operative to receive a user service request from a user client; an interaction processing component operative to submit the user service request to a memory-based natural language processing component; generate a series of user interaction exchanges with the user client based on output from the memory-based natural language processing component, wherein the series of user interaction exchanges are represented in a memory component of the memory-based natural language processing component; and receive one or more operator instructions for the performance of the user service request from the memory-based natural language processing component; and a user interface component operative to display the one or more operator instructions in an operator console. Other embodiments are described and claimed.

Techniques to predictively respond to user requests using natural language processing

To generate an embedding model for entities in an online system, a first set of partitions is generated. Each partition of the first set of partitions includes a subset of entities of the online system. Each partition of at least a subset of partitions of the first set of partitions is assigned to embedding workers. Each of the embedding worker determines embedding vectors for each entity in the partition assigned to the embedding worker. A second set of partitions is generated. Each partition of at least a subset of partitions of the second set of partitions are assigned to embedding workers. Each embedding worker retrieves embedding vectors for the entities in the partition assigned to embedding worker, and determines updated embedding vectors for each of the entities based on the retrieved embedding vectors and information about interaction between the entities.

Embeddings with multiple relationships

Embodiments are disclosed for providing a machine-generated response (e.g., answer) to an input (e.g., question) based on long-term memory information. A method according to some embodiments include receiving an input; converting the input into an input feature vector in an internal feature representation space; updating a memory data structure by incorporating the input feature vector into the memory data structure; generating an output feature vector in the internal feature representation space, based on the updated memory data structure and the input feature vector; converting the output feature vector into an output object; and providing an output based on the output object as a response to the input.

Generating responses using memory networks

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Brooklyn, NY, United States of America

Antoine Bordes