# Mask and Cloze: Automatic Open Cloze Question Generation using a Masked Language Model

arXiv: https://arxiv.org/abs/2205.07202 · HTML: https://mlsanity.com/paper/2205.07202/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2022.

## Built on by (most cited first, 3 of 3)

- [Mask-based Membership Inference Attacks for Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.20142/) (2024)
- [An overview of artificial intelligence in computer-assisted language learning](https://mlsanity.com/paper/2505.02032/) (2025)
- [FarsiMCQGen: a Persian Multiple-choice Question Generation Framework](https://mlsanity.com/paper/2510.15134/) (2025)

## Abstract (arXiv)

Open cloze questions have been attracting attention for both measuring the ability and facilitating the learning of L2 English learners. In spite of its benefits, the open cloze test has been introduced only sporadically on the educational front, largely because it is burdensome for teachers to manually create the questions. Unlike the more commonly used multiple choice questions (MCQ), open cloze questions are in free form and thus teachers have to ensure that only a ground truth answer and no additional words will be accepted in the blank. To help ease this burden, we developed CLOZER, an automatic open cloze question generator. In this work, we evaluate CLOZER through quantitative experiments on 1,600 answers and show statistically that it can successfully generate open cloze questions that only accept the ground truth answer. A comparative experiment with human-generated questions also reveals that CLOZER can generate OCQs better than the average non-native English teacher. Additionally, we conduct a field study at a local high school to clarify the benefits and hurdles when introducing CLOZER. The results demonstrate that while students found the application useful for their language learning. Finally, on the basis of our findings, we proposed several design improvements.