# Learning to Automatically Generate Fill-In-The-Blank Quizzes

arXiv: https://arxiv.org/abs/1806.04524 · HTML: https://mlsanity.com/paper/1806.04524/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

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

- [ELQA: A Corpus of Metalinguistic Questions and Answers about English](https://mlsanity.com/paper/2205.00395/) (2022)
- [Constructing Open Cloze Tests Using Generation and Discrimination Capabilities of Transformers](https://mlsanity.com/paper/2204.07237/) (2022)
- [An overview of artificial intelligence in computer-assisted language learning](https://mlsanity.com/paper/2505.02032/) (2025)
- [Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning](https://mlsanity.com/paper/2306.01584/) (2023)

## Abstract (arXiv)

In this paper we formalize the problem automatic fill-in-the-blank question generation using two standard NLP machine learning schemes, proposing concrete deep learning models for each. We present an empirical study based on data obtained from a language learning platform showing that both of our proposed settings offer promising results.