# Recent Advances in Neural Question Generation

arXiv: https://arxiv.org/abs/1905.08949 · HTML: https://mlsanity.com/paper/1905.08949/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2019.

## Built on by (most cited first, 30 of 42)

- [Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering](https://mlsanity.com/paper/2101.00774/) (2021)
- [Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting](https://mlsanity.com/paper/2105.11698/) (2021)
- [On the application of Large Language Models for language teaching and assessment technology](https://mlsanity.com/paper/2307.08393/) (2023)
- [Enhancing Critical Thinking in Education by means of a Socratic Chatbot](https://mlsanity.com/paper/2409.05511/) (2024)
- [Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation](https://mlsanity.com/paper/2209.11000/) (2022)
- [Guiding Visual Question Generation](https://mlsanity.com/paper/2110.08226/) (2021)
- [FOLLOWUPQG: Towards Information-Seeking Follow-up Question Generation](https://mlsanity.com/paper/2309.05007/) (2023)
- [EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain](https://mlsanity.com/paper/2210.06104/) (2022)
- [ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper Pages](https://mlsanity.com/paper/2403.17859/) (2024)
- [QAScore -- An Unsupervised Unreferenced Metric for the Question Generation Evaluation](https://mlsanity.com/paper/2210.04320/) (2022)
- [Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources](https://mlsanity.com/paper/2311.07589/) (2023)
- [What Has Been Lost with Synthetic Evaluation?](https://mlsanity.com/paper/2505.22830/) (2025)
- [CoHS-CQG: Context and History Selection for Conversational Question Generation](https://mlsanity.com/paper/2209.06652/) (2022)
- [OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach](https://mlsanity.com/paper/2102.12128/) (2021)
- [I Do Not Understand What I Cannot Define: Automatic Question Generation With Pedagogically-Driven Content Selection](https://mlsanity.com/paper/2110.04123/) (2021)
- [Towards Standard Criteria for human evaluation of Chatbots: A Survey](https://mlsanity.com/paper/2105.11197/) (2021)
- [NewsPod: Automatic and Interactive News Podcasts](https://mlsanity.com/paper/2202.07146/) (2022)
- [Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation](https://mlsanity.com/paper/2305.03088/) (2023)
- [MultiQG-TI: Towards Question Generation from Multi-modal Sources](https://mlsanity.com/paper/2307.04643/) (2023)
- [Navigating the Landscape of Hint Generation Research: From the Past to the Future](https://mlsanity.com/paper/2404.04728/) (2024)
- [MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price](https://mlsanity.com/paper/2105.00707/) (2021)
- [Evaluation of Question Answering Systems: Complexity of judging a natural language](https://mlsanity.com/paper/2209.12617/) (2022)
- [Proactive and Reactive Engagement of Artificial Intelligence Methods for Education: A Review](https://mlsanity.com/paper/2301.10231/) (2023)
- [A Survey on Neural Question Generation: Methods, Applications, and Prospects](https://mlsanity.com/paper/2402.18267/) (2024)
- [The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language Models](https://mlsanity.com/paper/2305.13669/) (2023)
- [Unified Question Generation with Continual Lifelong Learning](https://mlsanity.com/paper/2201.09696/) (2022)
- [Are Large Language Models Fit For Guided Reading?](https://mlsanity.com/paper/2305.10645/) (2023)
- [Backtracing: Retrieving the Cause of the Query](https://mlsanity.com/paper/2403.03956/) (2024)
- [ECIS-VQG: Generation of Entity-centric Information-seeking Questions from Videos](https://mlsanity.com/paper/2410.09776/) (2024)
- [From Superficial to Deep: Integrating External Knowledge for Follow-up Question Generation Using Knowledge Graph and LLM](https://mlsanity.com/paper/2504.05801/) (2025)

## Abstract (arXiv)

Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition. These trends point to NQG as a bellwether for NLP, about how human intelligence embodies the skills of curiosity and integration. We present a comprehensive survey of neural question generation, examining the corpora, methodologies, and evaluation methods. From this, we elaborate on what we see as emerging on NQG's trend: in terms of the learning paradigms, input modalities, and cognitive levels considered by NQG. We end by pointing out the potential directions ahead.