  Goal-oriented dialog systems enable users to complete specific goals like
requesting information about a movie or booking a ticket. Typically the dialog
system pipeline contains multiple ML models, including natural language
understanding, state tracking and action prediction (policy learning). These
models are trained through a combination of supervised or reinforcement
learning methods and therefore require collection of labeled domain specific
datasets. However, collecting annotated datasets with language and dialog-flow
variations is expensive, time-consuming and scales poorly due to human
involvement. In this paper, we propose an approach for automatically creating a
large corpus of annotated dialogs from a few thoroughly annotated sample
dialogs and the dialog schema. Our approach includes a novel goal-sampling
technique for sampling plausible user goals and a dialog simulation technique
that uses heuristic interplay between the user and the system (Alexa), where
the user tries to achieve the sampled goal. We validate our approach by
generating data and training three different downstream conversational ML
models. We achieve 18 ? 50% relative accuracy improvements on a held-out test
set compared to a baseline dialog generation approach that only samples natural
language and entity value variations from existing catalogs but does not
generate any novel dialog flow variations. We also qualitatively establish that
the proposed approach is better than the baseline. Moreover, several different
conversational experiences have been built using this method, which enables
customers to have a wide variety of conversations with Alexa.
