  Collecting data for training dialog systems can be extremely expensive due to
the involvement of human participants and need for extensive annotation.
Especially in document-grounded dialog systems, human experts need to carefully
read the unstructured documents to answer the users' questions. As a result,
existing document-grounded dialog datasets are relatively small-scale and
obstruct the effective training of dialogue systems. In this paper, we propose
an automatic data augmentation technique grounded on documents through a
generative dialogue model. The dialogue model consists of a user bot and agent
bot that can synthesize diverse dialogues given an input document, which are
then used to train a downstream model. When supplementing the original dataset,
our method achieves significant improvement over traditional data augmentation
methods. We also achieve great performance in the low-resource setting.
