# Reinforced Dynamic Reasoning for Conversational Question Generation

arXiv: https://arxiv.org/abs/1907.12667 · HTML: https://mlsanity.com/paper/1907.12667/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2019.

## Built on by (most cited first, 15 of 15)

- [A Survey on Recent Advances in Conversational Data Generation](https://mlsanity.com/paper/2405.13003/) (2024)
- [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)
- [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)
- [Conversational QA Dataset Generation with Answer Revision](https://mlsanity.com/paper/2209.11396/) (2022)
- [A Survey on Neural Question Generation: Methods, Applications, and Prospects](https://mlsanity.com/paper/2402.18267/) (2024)
- [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)
- [Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation](https://mlsanity.com/paper/2406.03703/) (2024)
- [Let's Talk! Striking Up Conversations via Conversational Visual Question Generation](https://mlsanity.com/paper/2205.09327/) (2022)
- [CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite](https://mlsanity.com/paper/2403.11873/) (2024)
- [An Answer is just the Start: Related Insight Generation for Open-Ended Document-Grounded QA](https://mlsanity.com/paper/2604.19685/) (2026)

## Abstract (arXiv)

This paper investigates a new task named Conversational Question Generation (CQG) which is to generate a question based on a passage and a conversation history (i.e., previous turns of question-answer pairs). CQG is a crucial task for developing intelligent agents that can drive question-answering style conversations or test user understanding of a given passage. Towards that end, we propose a new approach named Reinforced Dynamic Reasoning (ReDR) network, which is based on the general encoder-decoder framework but incorporates a reasoning procedure in a dynamic manner to better understand what has been asked and what to ask next about the passage. To encourage producing meaningful questions, we leverage a popular question answering (QA) model to provide feedback and fine-tune the question generator using a reinforcement learning mechanism. Empirical results on the recently released CoQA dataset demonstrate the effectiveness of our method in comparison with various baselines and model variants. Moreover, to show the applicability of our method, we also apply it to create multi-turn question-answering conversations for passages in SQuAD.