Dialog Inpainting: Turning Documents into Dialogs

Many important questions (e.g. “How to eat healthier?”) require conversation to establish context and explore in depth. However, conversational question answering (ConvQA) systems have long been stymied by scarce training data that is expensive to collect. To address this problem, we propose a new technique for synthetically generating diverse and high-quality dialog data: dialog inpainting. Our approach takes the text of any document and transforms it into a two person dialog between the writer and an imagined reader: we treat sentences from the article as utterances spoken by the writer, and then use a dialog in painter to predict what the imagined reader asked or said in between each of the writer’s utterances.