  A significant barrier to progress in data-driven approaches to building
dialog systems is the lack of high quality, goal-oriented conversational data.
To help satisfy this elementary requirement, we introduce the initial release
of the Taskmaster-1 dataset which includes 13,215 task-based dialogs comprising
six domains. Two procedures were used to create this collection, each with
unique advantages. The first involves a two-person, spoken "Wizard of Oz" (WOz)
approach in which trained agents and crowdsourced workers interact to complete
the task while the second is "self-dialog" in which crowdsourced workers write
the entire dialog themselves. We do not restrict the workers to detailed
scripts or to a small knowledge base and hence we observe that our dataset
contains more realistic and diverse conversations in comparison to existing
datasets. We offer several baseline models including state of the art neural
seq2seq architectures with benchmark performance as well as qualitative human
evaluations. Dialogs are labeled with API calls and arguments, a simple and
cost effective approach which avoids the requirement of complex annotation
schema. The layer of abstraction between the dialog model and the service
provider API allows for a given model to interact with multiple services that
provide similar functionally. Finally, the dataset will evoke interest in
written vs. spoken language, discourse patterns, error handling and other
linguistic phenomena related to dialog system research, development and design.
