Distributed server system for language understanding

A language understanding system, the language understanding system comprising:

• a trained language understanding server, the trained language understanding server comprises:
• at least one processor;
• and
• memory encoding computer executable instructions that, when executed by the at least one processor, perform a method comprising:
• receiving a natural language input from a client device;
• sending the natural language input to a plurality of feature extractors in response to receiving the natural language input;
• receiving potential features from the plurality of feature extractors after sending the natural language input to the plurality of feature extractors;
• receiving and evaluating the potential features to determine input features for the natural language input;
• determining a semantic meaning of the natural language input based on the input features;
• and
• sending a response to the client device that includes the semantic meaning of the natural language input.

A method for using a trained natural language understanding system, the method comprising:

• receiving a natural language input from a client device;
• sending the natural language input to a plurality of feature extractors,
• wherein the plurality of feature extractors are each located on different feature servers,
• receiving potential features for the natural language input from the plurality of feature extractors;
• evaluating the potential features utilizing a trained language understanding model to determine input features for the natural language input;
• and
• generating a response to the natural language input based on the input features.

A system comprising:

• at least one processor;
• and
• a memory encoding computer executable instructions that, when executed by the at least one processor, cause the at least one processor to perform a method for language understanding, the method comprising:
• receiving a natural language input from a client device on a natural language server,
• sending the natural language input to a first feature extractor on a first server from the natural language server;
• sending the natural language input to a second feature extractor on a second server from the natural language server,
• wherein the first server, the second server, and the natural language server are different and separate from each other;
• receiving a first set of potential features for the natural language input from the first feature extractor by the natural language server;
• receiving a second set of potential features for the natural language input from the second feature extractor by the natural language server;
• aggregating the first set of potential features and the second set of potential features to form an aggregated set of potential features;
• evaluating the aggregated set of potential features utilizing a language understanding model trained with training features from the first feature extractor and the second feature extractor;
• determining at least one of a user intent, a domain, or entities with associated confidence scores based on evaluating the aggregated set of potential features;
• and
• generating a response based on the at least one of the user intent, the domain, and the entities with the associated confidence scores.