Using semantic frames for intent classification

A non-transitory computer readable medium storing a plurality of instructions executable by one or more processors for identifying an intent for an utterance, wherein the plurality of instructions, when executed by the one or more processors, causes the one or more processors to perform processing comprising:

• receiving an utterance from a user;
• generating a semantic frame for the utterance, wherein the semantic frame comprises one or more structured grammatical relations that define semantic roles of a plurality of words in the utterance in relation to one another;
• determining a plurality of scores for a respective plurality of intents based on matching of a particular relation in the semantic frame to a plurality of features associated with the plurality of intents, wherein the plurality of scores are determined based on an intent model, and wherein the plurality of scores are determined based on weights assigned to each of the respective features in the intent model;
• identifying, based on the scores, a particular intent, of the plurality of intents, as corresponding to the utterance;
• generating a response based on the identified intent;
• and
• transmitting the response as output.

A method for training one or more intent models comprising:

• receiving, by a computer system, a set of training utterances, each training utterance being labeled with a particular intent;
• and
• for each training utterance:
• extracting, by the computer system from the training utterance, one or more features, wherein at least one of the features is a semantically relevant grammatical relation, wherein each feature has a value represented by one or more words included in the training utterance, and wherein extracting the one or more features comprises parsing the training utterance and generating a semantic frame to identify relations between the one or more words in the training utterance;
• and
• storing, by the computer system, the one or more features along with their corresponding values in association with the intent with which the training utterance is labeled,
• wherein the one or more features and their corresponding values are stored to one or more intent models useable for determining an intent for an utterance.