Abstract: A method for spoken language understanding (SLU) includes generating a first encoded representation of words from a user based on an output of a recurrent neural network (RNN), generating an intent label corresponding to the words based on an output of a first RNN decoder based on the first encoded representation, generating a corrected plurality of words based on an output of a second RNN decoder based on the first encoded representation and the intent label, generating a second encoded representation corresponding to the plurality of corrected words using the RNN encoder based on the plurality of corrected words, and generating a machine-readable dialog phrase that includes at least one word in the plurality of corrected words assigned to at least one slot based on an output of a third RNN decoder based on the second encoded representation of the plurality of corrected words and the intent label.

Abstract: A method for spoken language understanding (SLU) includes generating a first encoded representation of words from a user based on an output of a recurrent neural network (RNN), generating an intent label corresponding to the words based on an output of a first RNN decoder based on the first encoded representation, generating a corrected plurality of words based on an output of a second RNN decoder based on the first encoded representation and the intent label, generating a second encoded representation corresponding to the plurality of corrected words using the RNN encoder based on the plurality of corrected words, and generating a machine-readable dialogue phrase that includes at least one word in the plurality of corrected words assigned to at least one slot based on an output of a third RNN decoder based on the second encoded representation of the plurality of corrected words and the intent label.