Facebook, Inc.

Feng Liang

Daniel Bernhardt

Ian Douglas Hegerty

Agnieszka Anna Podsiadlo

Ying Zhang

Fei Huang

The described technology can provide semantic translations of a selected language snippet. This can be accomplished by mapping snippets for output languages into a vector space; creating predicates that can map new snippets into that vector space; and, when a new snippet is received, generating and matching a vector representing that new snippet to the closest vector for a snippet of a desired output language, which is used as the translation of the new snippet. The procedure for mapping new snippets into the vector space can include creating a dependency structure for the new snippet and computing a vector for each dependency structure node. The vector computed for the root node of the dependency structure is the vector representing the new snippet. A similar process is used to train a transformation function for each possible node type, using language snippets already associated with a dependency structure and corresponding vectors.

Deep translations

In one embodiment, a method includes, receiving, from a client system of a user, a search query including n-grams. The method includes associating each n-gram with verticals based on an analysis of the n-grams by language models. The method includes determining, for each n-gram, if a bloom filter for a vertical associated with the n-gram indicates, based on sub-bloom filters of the bloom filter, the n-gram does exist or does not exist in a set of object names associated with the vertical. Each sub-bloom filter is associated with a subset of the set of object names and indicates the n-gram does exist or does not exist in its subset of object names. The method includes sending, to the client system, an indication that an n-gram of the n-grams is misspelled if a bloom filter indicates the n-gram does not exist in the set of object names associated with the vertical.

Social-based spelling correction for online social networks

In one embodiment, a method includes receiving a search query, determining if a bloom filter indicates an n-gram of the query does not exist in a set of object names associated with a vertical, identifying variant-tokens for each n-gram that does not exist in the set of object names, generating unique combinations of the n-grams and variant-tokens, where each unique combination includes a variant-token corresponding to each n-gram that does not exist in the set of object names for the n-gram, calculating a confidence score for each unique combination based at least in part on the search query and whether the unique combination exists in the set of object names, identifying objects matching each unique combination, where the unique combination has a confidence score greater than a threshold confidence score, and sending a search-results page responsive to the search query to the client device of the user.

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London, United Kingdom

Feng Liang