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 inpainter to predict what the imagined reader asked or said in between each of the writer's utterances. By applying this approach to passages from Wikipedia and the web, we produce WikiDialog and WebDialog, two datasets totalling 19 million diverse information-seeking dialogs -- 1,000x larger than the largest existing ConvQA dataset. Furthermore, human raters judge the answer adequacy and conversationality of WikiDialog to be as good or better than existing manually-collected datasets. Using our inpainted data to pre-train ConvQA retrieval systems, we significantly advance state-of-the-art across three benchmarks (QReCC, OR-QuAC, TREC CAsT) yielding up to 40% relative gains on standard evaluation metrics.

提出一种通过生成dialog数据来解决ConvQA系统缺乏训练数据的问题的技术--dialog inpainting，并将其应用于来自维基百科和互联网的篇章中，生成了两个数据集：WikiDialog和WebDialog，共19m条对话数据，相对于现有数据集增加了1000倍，进而预训练ConvQA检索系统，在三个基准测试中（QReCC，OR-QuAC，TREC CAsT）实现了显著提升，最高可达40%。

对话修复: 将文档转化为对话