# Conversational QA Dataset Generation with Answer Revision

Source: [Conversational QA Dataset Generation with Answer Revision](<https://www.sota2.com/research/paper/68fa6d841e209131cea3a512>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:36:22.217Z

Authors: Seonjeong Hwang, Gary Geunbae Lee

Published: 2022-09-23T04:05:38.000Z

## Abstract

Conversational question--answer generation is a task that automatically generates a large-scale conversational question answering dataset based on input passages. In this paper, we introduce a novel framework that extracts question-worthy phrases from a passage and then generates corresponding questions considering previous conversations. In particular, our framework revises the extracted answers after generating questions so that answers exactly match paired questions. Experimental results show that our simple answer revision approach leads to significant improvement in the quality of synthetic data. Moreover, we prove that our framework can be effectively utilized for domain adaptation of conversational question answering.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2209.11396>)

[PDF](<https://arxiv.org/pdf/2209.11396.pdf>)