Abstract: An approach to extending the recognizable labels of a label recognizer makes use of an encoding of linguistic inputs and label attributes into comparable vectors. The encodings may be determined with artificial neural networks (ANNs) that are jointly trained, and a comparison between the encoding of a sentence input and the encoding of an intent attribute vector may use a fixed function, which does not have to be trained. The encoding of label attributes can generalize permitting adding of a new label via corresponding attributes, thereby avoiding the need to immediately retrain a label recognizer with example inputs.