  Training a spoken language understanding system, as the one in Alexa,
typically requires a large human-annotated corpus of data. Manual annotations
are expensive and time consuming. In Alexa Skill Kit (ASK) user experience with
the skill greatly depends on the amount of data provided by skill developer. In
this work, we present an automatic natural language generation system, capable
of generating both human-like interactions and annotations by the means of
paraphrasing. Our approach consists of machine translation (MT) inspired
encoder-decoder deep recurrent neural network. We evaluate our model on the
impact it has on ASK skill, intent, named entity classification accuracy and
sentence level coverage, all of which demonstrate significant improvements for
unseen skills on natural language understanding (NLU) models, trained on the
data augmented with paraphrases.
