  In this study, we tackle the challenge of inadequate and costly training data
that has hindered the development of conversational question answering (ConvQA)
systems. Enterprises have a large corpus of diverse internal documents. Instead
of relying on a searching engine, a more compelling approach for people to
comprehend these documents is to create a dialogue system. In this paper, we
propose a robust dialog synthesising method. We learn the segmentation of data
for the dialog task instead of using segmenting at sentence boundaries. The
synthetic dataset generated by our proposed method achieves superior quality
when compared to WikiDialog, as assessed through machine and human evaluations.
By employing our inpainted data for ConvQA retrieval system pre-training, we
observed a notable improvement in performance across OR-QuAC benchmarks.
