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        <title>Automatic True/False Question Generation for Educational Purpose</title>
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            <title>Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)</title>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>In field of teaching, true/false questioning is an important educational method for assessing students’ general understanding of learning materials. Manually creating such questions requires extensive human effort and expert knowledge. Question Generation (QG) technique offers the possibility to automatically generate a large number of questions. However, there is limited work on automatic true/false question generation due to the lack of training data and difficulty finding question-worthy content. In this paper, we propose an unsupervised True/False Question Generation approach (TF-QG) that automatically generates true/false questions from a given passage for reading comprehension test. TF-QG consists of a template-based framework that aims to test the specific knowledge in the passage by leveraging various NLP techniques, and a generative framework to generate more flexible and complicated questions by using a novel masking-and-infilling strategy. Human evaluation shows that our approach can generate high-quality and valuable true/false questions. In addition, simulated testing on the generated questions challenges the state-of-the-art inference models from NLI, QA, and fact verification tasks.</abstract>
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%0 Conference Proceedings
%T Automatic True/False Question Generation for Educational Purpose
%A Zou, Bowei
%A Li, Pengfei
%A Pan, Liangming
%A Aw, Ai Ti
%Y Kochmar, Ekaterina
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Madnani, Nitin
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%Y Zesch, Torsten
%S Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F zou-etal-2022-automatic
%X In field of teaching, true/false questioning is an important educational method for assessing students’ general understanding of learning materials. Manually creating such questions requires extensive human effort and expert knowledge. Question Generation (QG) technique offers the possibility to automatically generate a large number of questions. However, there is limited work on automatic true/false question generation due to the lack of training data and difficulty finding question-worthy content. In this paper, we propose an unsupervised True/False Question Generation approach (TF-QG) that automatically generates true/false questions from a given passage for reading comprehension test. TF-QG consists of a template-based framework that aims to test the specific knowledge in the passage by leveraging various NLP techniques, and a generative framework to generate more flexible and complicated questions by using a novel masking-and-infilling strategy. Human evaluation shows that our approach can generate high-quality and valuable true/false questions. In addition, simulated testing on the generated questions challenges the state-of-the-art inference models from NLI, QA, and fact verification tasks.
%R 10.18653/v1/2022.bea-1.10
%U https://aclanthology.org/2022.bea-1.10
%U https://doi.org/10.18653/v1/2022.bea-1.10
%P 61-70

Markdown (Informal)

[Automatic True/False Question Generation for Educational Purpose](https://aclanthology.org/2022.bea-1.10) (Zou et al., BEA 2022)

• Automatic True/False Question Generation for Educational Purpose (Zou et al., BEA 2022)

ACL

• Bowei Zou, Pengfei Li, Liangming Pan, and Ai Ti Aw. 2022. Automatic True/False Question Generation for Educational Purpose. In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022), pages 61–70, Seattle, Washington. Association for Computational Linguistics.