# GenQ: Automated Question Generation to Support Caregivers While Reading Stories with Children

arXiv: https://arxiv.org/abs/2305.16809 · HTML: https://mlsanity.com/paper/2305.16809/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 56 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 12 of 12)

- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [Unsupervised Question Answering by Cloze Translation](https://mlsanity.com/paper/1906.04980/) (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)
- [Cross-Lingual Training for Automatic Question Generation](https://mlsanity.com/paper/1906.02525/) (2019)
- [Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds](https://mlsanity.com/paper/1911.02365/) (2019)
- [Two can play this Game: Visual Dialog with Discriminative Question Generation and Answering](https://mlsanity.com/paper/1803.11186/) (2018)
- [Generating Natural Questions from Images for Multimodal Assistants](https://mlsanity.com/paper/2012.03678/) (2020)
- [Pedagogical Agents for Fostering Question-Asking Skills in Children](https://mlsanity.com/paper/2004.03472/) (2020)
- [Words of Wisdom: Representational Harms in Learning From AI Communication](https://mlsanity.com/paper/2111.08581/) (2021)
- [Generating Answer Candidates for Quizzes and Answer-Aware Question Generators](https://mlsanity.com/paper/2108.12898/) (2021)

## Abstract (arXiv)

When caregivers ask open--ended questions to motivate dialogue with children, it facilitates the child's reading comprehension skills.Although there is scope for use of technological tools, referred here as "intelligent tutoring systems", to scaffold this process, it is currently unclear whether existing intelligent systems that generate human--language like questions is beneficial. Additionally, training data used in the development of these automated question generation systems is typically sourced without attention to demographics, but people with different cultural backgrounds may ask different questions. As a part of a broader project to design an intelligent reading support app for Latinx children, we crowdsourced questions from Latinx caregivers and noncaregivers as well as caregivers and noncaregivers from other demographics. We examine variations in question--asking within this dataset mediated by individual, cultural, and contextual factors. We then design a system that automatically extracts templates from this data to generate open--ended questions that are representative of those asked by Latinx caregivers.