# Towards Enriched Controllability for Educational Question Generation

arXiv: https://arxiv.org/abs/2306.14917 · HTML: https://mlsanity.com/paper/2306.14917/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 21 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension](https://mlsanity.com/paper/2203.13947/) (2022)
- [Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric Summarization](https://mlsanity.com/paper/2203.14187/) (2022)
- [Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask](https://mlsanity.com/paper/2204.02908/) (2022)
- [Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs](https://mlsanity.com/paper/2205.00355/) (2022)

## Built on by (most cited first, 4 of 4)

- [On Few-Shot Prompting for Controllable Question-Answer Generation in Narrative Comprehension](https://mlsanity.com/paper/2404.02800/) (2024)
- [FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced Languages](https://mlsanity.com/paper/2406.04233/) (2024)
- [Difficulty-Controllable Multiple-Choice Question Generation Using Large Language Models and Direct Preference Optimization](https://mlsanity.com/paper/2510.19265/) (2025)
- [From Model to Classroom: Evaluating Generated MCQs for Portuguese with Narrative and Difficulty Concerns](https://mlsanity.com/paper/2506.15598/) (2025)

## Abstract (arXiv)

Question Generation (QG) is a task within Natural Language Processing (NLP) that involves automatically generating questions given an input, typically composed of a text and a target answer. Recent work on QG aims to control the type of generated questions so that they meet educational needs. A remarkable example of controllability in educational QG is the generation of questions underlying certain narrative elements, e.g., causal relationship, outcome resolution, or prediction. This study aims to enrich controllability in QG by introducing a new guidance attribute: question explicitness. We propose to control the generation of explicit and implicit wh-questions from children-friendly stories. We show preliminary evidence of controlling QG via question explicitness alone and simultaneously with another target attribute: the question's narrative element. The code is publicly available at github.com/bernardoleite/question-generation-control.