Learning to Automatically Generate Fill-In-The-Blank Quizzes
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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.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 12 Jun 2018.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 1.
4 papers.
- 2022ELQA: A Corpus of Metalinguistic Questions and Answers about Englishcited by 8
- 2022Constructing Open Cloze Tests Using Generation and Discrimination Capabilities of Transformerscited by 5
- 2025An overview of artificial intelligence in computer-assisted language learningcited by 2
- 2023Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learningcited by 1
- 2025An overview of artificial intelligence in computer-assisted language learning4 May 2025
- 2023Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning2 Jun 2023
- 2022ELQA: A Corpus of Metalinguistic Questions and Answers about English1 May 2022
- 2022Constructing Open Cloze Tests Using Generation and Discrimination Capabilities of Transformers14 Apr 2022