  We propose NeuralWOZ, a novel dialogue collection framework that uses
model-based dialogue simulation. NeuralWOZ has two pipelined models, Collector
and Labeler. Collector generates dialogues from (1) user's goal instructions,
which are the user context and task constraints in natural language, and (2)
system's API call results, which is a list of possible query responses for user
requests from the given knowledge base. Labeler annotates the generated
dialogue by formulating the annotation as a multiple-choice problem, in which
the candidate labels are extracted from goal instructions and API call results.
We demonstrate the effectiveness of the proposed method in the zero-shot domain
transfer learning for dialogue state tracking. In the evaluation, the synthetic
dialogue corpus generated from NeuralWOZ achieves a new state-of-the-art with
improvements of 4.4% point joint goal accuracy on average across domains, and
improvements of 5.7% point of zero-shot coverage against the MultiWOZ 2.1
dataset.
