Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation
This addresses the problem of costly data creation for enterprises needing conversational AI to comprehend internal documents, though it appears incremental as it builds on existing dialog synthesis approaches.
The study tackled the challenge of inadequate training data for conversational question answering systems by proposing a method to synthesize conversations from unlabeled documents, achieving superior quality compared to WikiDialog and improving performance on OR-QuAC benchmarks.
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.