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.

通过学习对话任务数据的分割而不是使用句子边界的分割方法，我们提出了一种强大的对话合成方法，通过该方法生成的合成数据集在机器和人员评估中表现出优越的质量，同时在 ConvQA 检索系统预训练中使用我们的填充数据，观察到 OR-QuAC 基准测试数据的显著提升。