  It is expensive and difficult to obtain the large number of sentence-level
intent and token-level slot label annotations required to train neural network
(NN)-based Natural Language Understanding (NLU) components of task-oriented
dialog systems, especially for the many real world tasks that have a large and
growing number of intents and slot types. While zero shot learning approaches
that require no labeled examples -- only features and auxiliary information --
have been proposed only for slot labeling, we show that one can profitably
perform joint zero-shot intent classification and slot labeling. We demonstrate
the value of capturing dependencies between intents and slots, and between
different slots in an utterance in the zero shot setting. We describe NN
architectures that translate between word and sentence embedding spaces, and
demonstrate that these modifications are required to enable zero shot learning
for this task. We show a substantial improvement over strong baselines and
explain the intuition behind each architectural modification through
visualizations and ablation studies.
