  Traditional goal-oriented dialogue systems rely on various components such as
natural language understanding, dialogue state tracking, policy learning and
response generation. Training each component requires annotations which are
hard to obtain for every new domain, limiting scalability of such systems.
Similarly, rule-based dialogue systems require extensive writing and
maintenance of rules and do not scale either. End-to-End dialogue systems, on
the other hand, do not require module-specific annotations but need a large
amount of data for training. To overcome these problems, in this demo, we
present Alexa Conversations, a new approach for building goal-oriented dialogue
systems that is scalable, extensible as well as data efficient. The components
of this system are trained in a data-driven manner, but instead of collecting
annotated conversations for training, we generate them using a novel dialogue
simulator based on a few seed dialogues and specifications of APIs and entities
provided by the developer. Our approach provides out-of-the-box support for
natural conversational phenomena like entity sharing across turns or users
changing their mind during conversation without requiring developers to provide
any such dialogue flows. We exemplify our approach using a simple pizza
ordering task and showcase its value in reducing the developer burden for
creating a robust experience. Finally, we evaluate our system using a typical
movie ticket booking task and show that the dialogue simulator is an essential
component of the system that leads to over $50\%$ improvement in turn-level
action signature prediction accuracy.
