Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation

TL;DR

This paper introduces a novel method for synthesizing conversational data from unlabeled documents by learning data segmentation, significantly improving conversational question answering systems without relying on labeled datasets.

Contribution

It proposes a robust dialog synthesis approach that segments data beyond sentence boundaries, enhancing synthetic dataset quality for training ConvQA systems.

Findings

Synthetic data outperforms WikiDialog in quality assessments.

Pre-training with inpainted data improves ConvQA performance.

Method reduces reliance on costly labeled datasets.

Abstract

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.