# ChainCQG: Flow-Aware Conversational Question Generation

arXiv: https://arxiv.org/abs/2102.02864 · HTML: https://mlsanity.com/paper/2102.02864/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 45 references that are in the map.
- First posted on arXiv 4 Feb 2021.

## Builds on (most cited first, 27 of 27)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://mlsanity.com/paper/1909.02622/) (2019)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [Evaluation of Text Generation: A Survey](https://mlsanity.com/paper/2006.14799/) (2020)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems](https://mlsanity.com/paper/1704.00057/) (2017)
- [Neural Question Generation from Text: A Preliminary Study](https://mlsanity.com/paper/1704.01792/) (2017)
- [Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia](https://mlsanity.com/paper/1805.05942/) (2018)
- [GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension](https://mlsanity.com/paper/1908.00059/) (2019)
- [Improving Neural Question Generation using Answer Separation](https://mlsanity.com/paper/1809.02393/) (2018)
- [SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering](https://mlsanity.com/paper/1812.03593/) (2018)
- [Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders](https://mlsanity.com/paper/1805.04843/) (2018)
- [Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling](https://mlsanity.com/paper/1906.06893/) (2019)
- [FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension](https://mlsanity.com/paper/1908.05117/) (2019)
- [Technical report on Conversational Question Answering](https://mlsanity.com/paper/1909.10772/) (2019)
- [Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations](https://mlsanity.com/paper/2004.14530/) (2020)
- [A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension](https://mlsanity.com/paper/1905.12848/) (2019)
- [Reinforced Dynamic Reasoning for Conversational Question Generation](https://mlsanity.com/paper/1907.12667/) (2019)
- [Improving Question Generation With to the Point Context](https://mlsanity.com/paper/1910.06036/) (2019)
- [Neural Models for Key Phrase Detection and Question Generation](https://mlsanity.com/paper/1706.04560/) (2017)
- [A Question Type Driven and Copy Loss Enhanced Frameworkfor Answer-Agnostic Neural Question Generation](https://mlsanity.com/paper/2005.11665/) (2020)

## Built on by (most cited first, 19 of 19)

- [A Survey on Recent Advances in Conversational Data Generation](https://mlsanity.com/paper/2405.13003/) (2024)
- [Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask](https://mlsanity.com/paper/2204.02908/) (2022)
- [QUDEVAL: The Evaluation of Questions Under Discussion Discourse Parsing](https://mlsanity.com/paper/2310.14520/) (2023)
- [Generating Information-Seeking Conversations from Unlabeled Documents](https://mlsanity.com/paper/2205.12609/) (2022)
- [MVP: Multi-task Supervised Pre-training for Natural Language Generation](https://mlsanity.com/paper/2206.12131/) (2022)
- [DG2: Data Augmentation Through Document Grounded Dialogue Generation](https://mlsanity.com/paper/2112.08342/) (2021)
- [CoHS-CQG: Context and History Selection for Conversational Question Generation](https://mlsanity.com/paper/2209.06652/) (2022)
- [Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation](https://mlsanity.com/paper/2305.03088/) (2023)
- [Inductive-Deductive Strategy Reuse for Multi-Turn Instructional Dialogues](https://mlsanity.com/paper/2404.11095/) (2024)
- [PCQPR: Proactive Conversational Question Planning with Reflection](https://mlsanity.com/paper/2410.01363/) (2024)
- [Conversational QA Dataset Generation with Answer Revision](https://mlsanity.com/paper/2209.11396/) (2022)
- [Data Augmentation for Conversational AI](https://mlsanity.com/paper/2309.04739/) (2023)
- [S2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answering](https://mlsanity.com/paper/2312.16511/) (2023)
- [Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions](https://mlsanity.com/paper/2210.12979/) (2022)
- [Follow-on Question Suggestion via Voice Hints for Voice Assistants](https://mlsanity.com/paper/2310.17034/) (2023)
- [Consistency Training by Synthetic Question Generation for Conversational Question Answering](https://mlsanity.com/paper/2404.11109/) (2024)
- [Question Generation in Knowledge-Driven Dialog: Explainability and Evaluation](https://mlsanity.com/paper/2404.07836/) (2024)
- [Beyond the Textual: Generating Coherent Visual Options for MCQs](https://mlsanity.com/paper/2508.18772/) (2025)
- [An Answer is just the Start: Related Insight Generation for Open-Ended Document-Grounded QA](https://mlsanity.com/paper/2604.19685/) (2026)

## Abstract (arXiv)

Conversational systems enable numerous valuable applications, and question-answering is an important component underlying many of these. However, conversational question-answering remains challenging due to the lack of realistic, domain-specific training data. Inspired by this bottleneck, we focus on conversational question generation as a means to generate synthetic conversations for training and evaluation purposes. We present a number of novel strategies to improve conversational flow and accommodate varying question types and overall fluidity. Specifically, we design ChainCQG as a two-stage architecture that learns question-answer representations across multiple dialogue turns using a flow propagation training strategy.ChainCQG significantly outperforms both answer-aware and answer-unaware SOTA baselines (e.g., up to 48% BLEU-1 improvement). Additionally, our model is able to generate different types of questions, with improved fluidity and coreference alignment.