# Machine Comprehension by Text-to-Text Neural Question Generation

arXiv: https://arxiv.org/abs/1705.02012 · HTML: https://mlsanity.com/paper/1705.02012/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2017.

## Built on by (most cited first, 30 of 32)

- [Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling](https://mlsanity.com/paper/2402.17019/) (2024)
- [Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask](https://mlsanity.com/paper/2204.02908/) (2022)
- [Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges](https://mlsanity.com/paper/2405.15604/) (2024)
- [Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation](https://mlsanity.com/paper/2209.11000/) (2022)
- [Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey](https://mlsanity.com/paper/2208.03197/) (2022)
- [A Survey of Natural Language Generation](https://mlsanity.com/paper/2112.11739/) (2021)
- [Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language](https://mlsanity.com/paper/2205.06111/) (2022)
- [Diversify Question Generation with Retrieval-Augmented Style Transfer](https://mlsanity.com/paper/2310.14503/) (2023)
- [QAScore -- An Unsupervised Unreferenced Metric for the Question Generation Evaluation](https://mlsanity.com/paper/2210.04320/) (2022)
- [OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach](https://mlsanity.com/paper/2102.12128/) (2021)
- [Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data](https://mlsanity.com/paper/2102.01226/) (2021)
- [Evaluation of Question Answering Systems: Complexity of judging a natural language](https://mlsanity.com/paper/2209.12617/) (2022)
- [Automatic Answerability Evaluation for Question Generation](https://mlsanity.com/paper/2309.12546/) (2023)
- [Diversity Enhanced Narrative Question Generation for Storybooks](https://mlsanity.com/paper/2310.16446/) (2023)
- [Automatic Generation and Evaluation of Reading Comprehension Test Items with Large Language Models](https://mlsanity.com/paper/2404.07720/) (2024)
- [AnswerQuest: A System for Generating Question-Answer Items from Multi-Paragraph Documents](https://mlsanity.com/paper/2103.03820/) (2021)
- [M-SENSE: Modeling Narrative Structure in Short Personal Narratives Using Protagonist's Mental Representations](https://mlsanity.com/paper/2302.09418/) (2023)
- [Exploring the Potential of Large Language Models in Generating Code-Tracing Questions for Introductory Programming Courses](https://mlsanity.com/paper/2310.15317/) (2023)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [E-QGen: Educational Lecture Abstract-based Question Generation System](https://mlsanity.com/paper/2404.13547/) (2024)
- [Learning by Asking Questions for Knowledge-based Novel Object Recognition](https://mlsanity.com/paper/2210.05879/) (2022)
- [Weakly-Supervised Questions for Zero-Shot Relation Extraction](https://mlsanity.com/paper/2301.09640/) (2023)
- [On the Evaluation of Answer-Agnostic Paragraph-level Multi-Question Generation](https://mlsanity.com/paper/2203.04464/) (2022)
- [Automatic question generation based on sentence structure analysis using machine learning approach](https://mlsanity.com/paper/2205.12811/) (2022)
- [Inflected Forms Are Redundant in Question Generation Models](https://mlsanity.com/paper/2301.00397/) (2023)
- [Prompt-Engineering and Transformer-based Question Generation and Evaluation](https://mlsanity.com/paper/2310.18867/) (2023)
- [Automating Turkish Educational Quiz Generation Using Large Language Models](https://mlsanity.com/paper/2406.03397/) (2024)
- [Semantic Bridge: Universal Multi-Hop Question Generation via AMR-Driven Graph Synthesis](https://mlsanity.com/paper/2508.10013/) (2025)
- [Using Implicit Feedback to Improve Question Generation](https://mlsanity.com/paper/2304.13664/) (2023)
- [Exploring Answer Information Methods for Question Generation with Transformers](https://mlsanity.com/paper/2312.03483/) (2023)

## Abstract (arXiv)

We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervised and reinforcement learning. After teacher forcing for standard maximum likelihood training, we fine-tune the model using policy gradient techniques to maximize several rewards that measure question quality. Most notably, one of these rewards is the performance of a question-answering system. We motivate question generation as a means to improve the performance of question answering systems. Our model is trained and evaluated on the recent question-answering dataset SQuAD.