  Popular dialog datasets such as MultiWOZ are created by providing crowd
workers an instruction, expressed in natural language, that describes the task
to be accomplished. Crowd workers play the role of a user and an agent to
generate dialogs to accomplish tasks involving booking restaurant tables,
calling a taxi etc. In this paper, we present a data creation strategy that
uses the pre-trained language model, GPT2, to simulate the interaction between
crowd workers by creating a user bot and an agent bot. We train the simulators
using a smaller percentage of actual crowd-generated conversations and their
corresponding instructions. We demonstrate that by using the simulated data, we
achieve significant improvements in low-resource settings on two publicly
available datasets - the MultiWOZ dataset and the Persona chat dataset.
