# Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask

arXiv: https://arxiv.org/abs/2204.02908 · HTML: https://mlsanity.com/paper/2204.02908/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 74 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## Builds on (most cited first, 30 of 43)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [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)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [Towards Understanding and Mitigating Social Biases in Language Models](https://mlsanity.com/paper/2106.13219/) (2021)
- [Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://mlsanity.com/paper/1909.00277/) (2019)
- [Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them](https://mlsanity.com/paper/1903.03862/) (2019)
- [Why We Need New Evaluation Metrics for NLG](https://mlsanity.com/paper/1707.06875/) (2017)
- [UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training](https://mlsanity.com/paper/2002.12804/) (2020)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [End-to-End Open-Domain Question Answering with BERTserini](https://mlsanity.com/paper/1902.01718/) (2019)
- [Neural Question Generation from Text: A Preliminary Study](https://mlsanity.com/paper/1704.01792/) (2017)
- [Probing Across Time: What Does RoBERTa Know and When?](https://mlsanity.com/paper/2104.07885/) (2021)
- [MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension](https://mlsanity.com/paper/1905.13453/) (2019)
- [Generating Natural Questions About an Image](https://mlsanity.com/paper/1603.06059/) (2016)
- [Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus](https://mlsanity.com/paper/2002.00748/) (2020)
- [Machine Comprehension by Text-to-Text Neural Question Generation](https://mlsanity.com/paper/1705.02012/) (2017)
- [MixQG: Neural Question Generation with Mixed Answer Types](https://mlsanity.com/paper/2110.08175/) (2021)
- [CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering](https://mlsanity.com/paper/2010.16021/) (2020)
- [Supervised and Unsupervised Transfer Learning for Question Answering](https://mlsanity.com/paper/1711.05345/) (2017)
- [Cross-lingual Zero- and Few-shot Hate Speech Detection Utilising Frozen Transformer Language Models and AXEL](https://mlsanity.com/paper/2004.13850/) (2020)
- [ChainCQG: Flow-Aware Conversational Question Generation](https://mlsanity.com/paper/2102.02864/) (2021)
- [Learning to Generate Questions by Learning What not to Generate](https://mlsanity.com/paper/1902.10418/) (2019)
- [Question-type Driven Question Generation](https://mlsanity.com/paper/1909.00140/) (2019)
- [GTM: A Generative Triple-Wise Model for Conversational Question Generation](https://mlsanity.com/paper/2106.03635/) (2021)

## Built on by (most cited first, 20 of 20)

- [ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions](https://mlsanity.com/paper/2303.06594/) (2023)
- [Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling](https://mlsanity.com/paper/2402.17019/) (2024)
- [QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation](https://mlsanity.com/paper/2406.05707/) (2024)
- [EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios](https://mlsanity.com/paper/2505.16160/) (2025)
- [FOLLOWUPQG: Towards Information-Seeking Follow-up Question Generation](https://mlsanity.com/paper/2309.05007/) (2023)
- [Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources](https://mlsanity.com/paper/2311.07589/) (2023)
- [Question Suggestion for Conversational Shopping Assistants Using Product Metadata](https://mlsanity.com/paper/2405.01738/) (2024)
- [Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizers](https://mlsanity.com/paper/2406.05255/) (2024)
- [Automatic Generation and Evaluation of Reading Comprehension Test Items with Large Language Models](https://mlsanity.com/paper/2404.07720/) (2024)
- [Towards Enriched Controllability for Educational Question Generation](https://mlsanity.com/paper/2306.14917/) (2023)
- [On Few-Shot Prompting for Controllable Question-Answer Generation in Narrative Comprehension](https://mlsanity.com/paper/2404.02800/) (2024)
- [Bridging Information Gaps with Comprehensive Answers: Improving the Diversity and Informativeness of Follow-Up Questions](https://mlsanity.com/paper/2502.17715/) (2025)
- [SkillQG: Learning to Generate Question for Reading Comprehension Assessment](https://mlsanity.com/paper/2305.04737/) (2023)
- [FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced Languages](https://mlsanity.com/paper/2406.04233/) (2024)
- [Context Selection and Rewriting for Video-based Educational Question Generation](https://mlsanity.com/paper/2504.19406/) (2025)
- [Advancing Question Generation with Joint Narrative and Difficulty Control](https://mlsanity.com/paper/2506.06812/) (2025)
- [Automatic Generation of Inference Making Questions for Reading Comprehension Assessments](https://mlsanity.com/paper/2506.08260/) (2025)
- [ErrEval: Error-Aware Evaluation for Question Generation through Explicit Diagnostics](https://mlsanity.com/paper/2601.10406/) (2026)
- [Exploring Answer Information Methods for Question Generation with Transformers](https://mlsanity.com/paper/2312.03483/) (2023)
- [From Model to Classroom: Evaluating Generated MCQs for Portuguese with Narrative and Difficulty Concerns](https://mlsanity.com/paper/2506.15598/) (2025)

## Abstract (arXiv)

Reading is integral to everyday life, and yet learning to read is a struggle for many young learners. During lessons, teachers can use comprehension questions to increase engagement, test reading skills, and improve retention. Historically such questions were written by skilled teachers, but recently language models have been used to generate comprehension questions. However, many existing Question Generation (QG) systems focus on generating literal questions from the text, and have no way to control the type of the generated question. In this paper, we study QG for reading comprehension where inferential questions are critical and extractive techniques cannot be used. We propose a two-step model (HTA-WTA) that takes advantage of previous datasets, and can generate questions for a specific targeted comprehension skill. We propose a new reading comprehension dataset that contains questions annotated with story-based reading comprehension skills (SBRCS), allowing for a more complete reader assessment. Across several experiments, our results show that HTA-WTA outperforms multiple strong baselines on this new dataset. We show that the HTA-WTA model tests for strong SCRS by asking deep inferential questions.