Contextual sentence embeddings for natural language processing applications

An electronic device, comprising:

• one or more processors;
• a memory;
• and
• one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for operating the electronic device, and the instructions include operations comprising:
• receiving a first n-gram at the electronic device, wherein the first n-gram includes a first set tokens and represents a first semantic context within a first natural language associated with the first n-gram, the first set of tokens being an ordered set of tokens of the first natural language
• employing a first language model to generate a token vector for each token in the first set of tokens;
• employing a second language model and the token vector of each token in the first set of tokens to generate a first sentence vector for the first n-gram that embeds the first semantic context within a vector space of the second language model;
• and
• selecting, from a plurality of other n-grams, a second n-gram based on a semantic relationship between the first semantic context and a second semantic context represented by the second n-gram, wherein the semantic relationship is based on the first sentence vector, and the second n-gram is associated with a second natural language.

A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions for operating an electronic device, when the instructions are executed by one or more processors of the electronic device, cause the electronic device performs operations comprising:

• receiving a first n-gram at the electronic device, wherein the first n-gram includes a first set tokens and represents a first semantic context within a first natural language associated with the first n-gram, the first set of tokens being an ordered set of tokens of the first natural language
• employing a first language model to generate a token vector for each token in the first set of tokens;
• employing a second language model and the token vector of each token in the first set of tokens to generate a first sentence vector for the first n-gram that embeds the first semantic context within a vector space of the second language model;
• and
• selecting, from a plurality of other n-grams, a second n-gram based on a semantic relationship between the first semantic context and a second semantic context represented by the second n-gram, wherein the semantic relationship is based on the first sentence vector and the second n-gram corresponds to a second natural language.