  Sub-tasks of intent classification, such as robustness to distribution shift,
adaptation to specific user groups and personalization, out-of-domain
detection, require extensive and flexible datasets for experiments and
evaluation. As collecting such datasets is time- and labor-consuming, we
propose to use text generation methods to gather datasets. The generator should
be trained to generate utterances that belong to the given intent. We explore
two approaches to generating task-oriented utterances. In the zero-shot
approach, the model is trained to generate utterances from seen intents and is
further used to generate utterances for intents unseen during training. In the
one-shot approach, the model is presented with a single utterance from a test
intent. We perform a thorough automatic, and human evaluation of the dataset
generated utilizing two proposed approaches. Our results reveal that the
attributes of the generated data are close to original test sets, collected via
crowd-sourcing.
