# S2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answering

arXiv: https://arxiv.org/abs/2312.16511 · HTML: https://mlsanity.com/paper/2312.16511/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 32 references that are in the map.
- First posted on arXiv 27 Dec 2023.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them](https://mlsanity.com/paper/2102.07033/) (2021)
- [Open-Domain Question Answering Goes Conversational via Question Rewriting](https://mlsanity.com/paper/2010.04898/) (2020)
- [Retrospective Reader for Machine Reading Comprehension](https://mlsanity.com/paper/2001.09694/) (2020)
- [Dialogue-Based Relation Extraction](https://mlsanity.com/paper/2004.08056/) (2020)
- [Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph](https://mlsanity.com/paper/1801.10314/) (2018)
- [GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension](https://mlsanity.com/paper/1908.00059/) (2019)
- [FlowQA: Grasping Flow in History for Conversational Machine Comprehension](https://mlsanity.com/paper/1810.06683/) (2018)
- [Attentive History Selection for Conversational Question Answering](https://mlsanity.com/paper/1908.09456/) (2019)
- [OneRel:Joint Entity and Relation Extraction with One Module in One Step](https://mlsanity.com/paper/2203.05412/) (2022)
- [Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations](https://mlsanity.com/paper/2004.14530/) (2020)
- [ChainCQG: Flow-Aware Conversational Question Generation](https://mlsanity.com/paper/2102.02864/) (2021)
- [Generating Information-Seeking Conversations from Unlabeled Documents](https://mlsanity.com/paper/2205.12609/) (2022)
- [Reinforced Dynamic Reasoning for Conversational Question Generation](https://mlsanity.com/paper/1907.12667/) (2019)
- [RoR: Read-over-Read for Long Document Machine Reading Comprehension](https://mlsanity.com/paper/2109.04780/) (2021)
- [DetIE: Multilingual Open Information Extraction Inspired by Object Detection](https://mlsanity.com/paper/2206.12514/) (2022)
- [Cooperative Self-training of Machine Reading Comprehension](https://mlsanity.com/paper/2103.07449/) (2021)
- [What Do You Mean `Why?': Resolving Sluices in Conversations](https://mlsanity.com/paper/1911.09478/) (2019)

## Built on by (most cited first, 3 of 3)

- [Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check](https://mlsanity.com/paper/2403.18243/) (2024)
- [Agent-Based Detection and Resolution of Incompleteness and Ambiguity in Interactions with Large Language Models](https://mlsanity.com/paper/2507.03726/) (2025)
- [LLM-Driven Multi-Turn Task-Oriented Dialogue Synthesis for Realistic Reasoning](https://mlsanity.com/paper/2602.23610/) (2026)

## Abstract (arXiv)

Supplying data augmentation to conversational question answering (CQA) can effectively improve model performance. However, there is less improvement from single-turn datasets in CQA due to the distribution gap between single-turn and multi-turn datasets. On the other hand, while numerous single-turn datasets are available, we have not utilized them effectively. To solve this problem, we propose a novel method to convert single-turn datasets to multi-turn datasets. The proposed method consists of three parts, namely, a QA pair Generator, a QA pair Reassembler, and a question Rewriter. Given a sample consisting of context and single-turn QA pairs, the Generator obtains candidate QA pairs and a knowledge graph based on the context. The Reassembler utilizes the knowledge graph to get sequential QA pairs, and the Rewriter rewrites questions from a conversational perspective to obtain a multi-turn dataset S2M. Our experiments show that our method can synthesize effective training resources for CQA. Notably, S2M ranks 1st place on the QuAC leaderboard at the time of submission (Aug 24th, 2022).